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IRIDIA BibTeX Repository

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This list of references in automatically generated from a collection of BibTeX files organized in a way that tries to avoid redundancy, minimise mistakes and facilitate customization.

You only need to fork (or link) the git repository in your papers and sync with the main copy to send/receive updates.

Most customisations, such as shorter journal or conference names, do not require changing the existing .bib files. You should not need to edit the entries directly unless you find mistakes. See the README for more details.

References

[1]

Ossama Abdelkhalik and Ahmed Gad. Dynamic-Size Multiple Populations Genetic Algorithm for Multigravity-Assist Trajectory Optimization. Journal of Guidance, Control, and Dynamics, 35(2):520–529, 2012.
[ bib | DOI ]

[2]

David Abramson, Mohan Krishna Amoorthy, and Henry Dang. Simulated annealing cooling schedules for the school timetabling problem. Asia-Pacific Journal of Operational Research, 16(1):1–22, 1999.
[ bib ]

[3]

David Abramson. Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms. Management Science, 37(1):98–113, 1991.
[ bib ]

[4]

Tobias Achterberg. SCIP: Solving constraint integer programs. Mathematical Programming Computation, 1(1):1–41, July 2009.
[ bib | epub ]

[5]

Tobias Achterberg and Timo Berthold. Improving the feasibility pump. Discrete Optimization, 4(1):77–86, 2007.
[ bib ]

[6]

Héctor-Gabriel Acosta-Mesa, Fernando Rechy-Ramírez, Efrén Mezura-Montes, Nicandro Cruz-Ramírez, and Rodolfo Hernández Jiménez. Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions. Journal of Biomedical Informatics, 49:73–83, 2014.
[ bib | DOI ]

Keywords: irace

[7]

Bernardetta Addis, Marco Locatelli, and Fabio Schoen. Disk Packing in a Square: A New Global Optimization Approach. INFORMS Journal on Computing, 20(4):516–524, 2008.
[ bib | DOI ]

[8]

B. Adenso-Díaz. Restricted Neighborhood in the Tabu Search for the Flowshop Problem. European Journal of Operational Research, 62(1):27–37, 1992.
[ bib ]

[9]

B. Adenso-Díaz and Manuel Laguna. Fine-Tuning of Algorithms Using Fractional Experimental Design and Local Search. Operations Research, 54(1):99–114, 2006.
[ bib ]

Keywords: Calibra

[10]

Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor Awad, Theresa Eimer, Marius Thomas Lindauer, and Frank Hutter. Automated dynamic algorithm configuration. Journal of Artificial Intelligence Research, 75:1633–1699, 2022.
[ bib | DOI ]

[11]

Bekir Afsar, Kaisa Miettinen, and Francisco Ruiz. Assessing the Performance of Interactive Multiobjective Optimization Methods: A Survey. ACM Computing Surveys, 54(4), 2021.
[ bib | DOI ]

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

Keywords: decision-makers, Interactive methods, performance assessment, preference information, multiobjective optimization problems

[12]

Bekir Afsar, Johanna Silvennoinen, Giovanni Misitano, Francisco Ruiz, Ana B. Ruiz, and Kaisa Miettinen. Designing empirical experiments to compare interactive multiobjective optimization methods. Journal of the Operational Research Society, 74(11):2327–2338, November 2022.
[ bib | DOI ]

[13]

Ralph D'Agostino and E. S. Pearson. Tests for Departure from Normality. Empirical Results for the Distributions of b₂ and √b₁. Biometrika, 60(3):613–622, December 1973.
[ bib | DOI ]

[14]

Per J. Agrell. On redundancy in multi criteria decision making. European Journal of Operational Research, 98(3):571–586, 1997.
[ bib | DOI ]

The concept of redundancy is accepted in Operations Research and Information Theory. In Linear Programming, a constraint is said to be redundant if the feasible decision space is identical with or without the constraint. In Information Theory, redundancy is used as a measure of the stability against noise in transmission. Analogies with Multi Criteria Decision Making (MCDM) are indicated and it is argued that the redundancy concept should be used as a regular feature in conditioning and analysis of Multi Criteria Programs. Properties of a proposed conflict-based characterisation are stated and some existence results are derived. Redundancy is here intended for interactive methods, when the efficient set is progressively explored. A new redundancy test for the linear case is formulated from the framework. A probabilistic method based on correlation is proposed and tested for the non-linear case. Finally, some general guidelines are given concerning the redundancy problem.

Keywords: Multi criteria decision making, Redundancy, objective reduction, Vector optimisation

[15]

Hernán E. Aguirre and Kiyoshi Tanaka. Working principles, behavior, and performance of MOEAs on MNK-landscapes. European Journal of Operational Research, 181(3):1670–1690, 2007.
[ bib | DOI ]

[16]

Samad Ahmadi and Ibrahim H. Osman. Density Based Problem Space Search for the Capacitated Clustering p-Median Problem. Annals of Operations Research, 131:21–43, 2004.
[ bib ]

[17]

Ali Ahrari, Saber Elsayed, Ruhul Sarker, Daryl Essam, and Carlos A. Coello Coello. Weighted pointwise prediction method for dynamic multiobjective optimization. Information Sciences, 546:349–367, 2021.
[ bib ]

[18]

R. K. Ahuja, O. Ergun, and A. P. Punnen. A Survey of Very Large-scale Neighborhood Search Techniques. Discrete Applied Mathematics, 123(1–3):75–102, 2002.
[ bib ]

[19]

Sandip Aine, Rajeev Kumar, and P. P. Chakrabarti. Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off. Applied Soft Computing, 9(2):527–540, 2009.
[ bib | DOI ]

Keywords: anytime

[20]

A. A. Albrecht, P. C. R. Lane, and K. Steinhöfel. Analysis of Local Search Landscapes for k-SAT Instances. Mathematics in Computer Science, 3(4):465–488, 2010.
[ bib | DOI ]

[21]

Susanne Albers. Online Algorithms: A Survey. Mathematical Programming, 97(1):3–26, 2003.
[ bib ]

[22]

Aldeida Aleti and Irene Moser. A systematic literature review of adaptive parameter control methods for evolutionary algorithms. ACM Computing Surveys, 49(3, Article 56):35, October 2016.
[ bib | DOI ]

[23]

Pedro Alfaro-Fernández, Rubén Ruiz, Federico Pagnozzi, and Thomas Stützle. Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems. European Journal of Operational Research, 282(3):835–845, 2020.
[ bib | DOI ]

Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are NP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed AAD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.

Keywords: Scheduling, Hybrid flowshop, Automatic algorithm configuration, Automatic Algorithm Design

[24]

Alnur Ali and Marina Meilă. Experiments with Kemeny ranking: What Works When? Mathematical Social Science, 64(1):28–40, July 2012.
[ bib | DOI ]

Computational Foundations of Social Choice

Keywords: Borda ranking, Kemeny ranking

[25]

Ali Allahverdi and Harun Aydilek. Algorithms for no-wait flowshops with total completion time subject to makespan. International Journal of Advanced Manufacturing Technology, pp.  1–15, 2013.
[ bib ]

[26]

Richard Allmendinger, Andrzej Jaszkiewicz, Arnaud Liefooghe, and Christiane Tammer. What if we increase the number of objectives? Theoretical and empirical implications for many-objective combinatorial optimization. Computers & Operations Research, 145:105857, 2022.
[ bib | DOI ]

[27]

Richard Allmendinger and Joshua D. Knowles. On Handling Ephemeral Resource Constraints in Evolutionary Search. Evolutionary Computation, 21(3):497–531, September 2013.
[ bib | DOI ]

We consider optimization problems where the set of solutions available for evaluation at any given time t during optimization is some subset of the feasible space. This model is appropriate to describe many closed-loop optimization settings (i.e. where physical processes or experiments are used to evaluate solutions) where, due to resource limitations, it may be impossible to evaluate particular solutions at particular times (despite the solutions being part of the feasible space). We call the constraints determining which solutions are non-evaluable ephemeral resource constraints (ERCs). In this paper, we investigate two specific types of ERC: one encodes periodic resource availabilities, the other models `commitment' constraints that make the evaluable part of the space a function of earlier evaluations conducted. In an experimental study, both types of constraint are seen to impact the performance of an evolutionary algorithm significantly. To deal with the effects of the ERCs, we propose and test five different constrainthandling policies (adapted from those used to handle `standard' constraints), using a number of different test functions including a fitness landscape from a real closed-loop problem. We show that knowing information about the type of resource constraint in advance may be sufficient to select an effective policy for dealing with it, even when advance knowledge of the fitness landscape is limited.

[28]

Christian Almeder. A hybrid optimization approach for multi-level capacitated lot-sizing problems. European Journal of Operational Research, 200(2):599–606, 2010.
[ bib | DOI ]

Solving multi-level capacitated lot-sizing problems is still a challenging task, in spite of increasing computational power and faster algorithms. In this paper a new approach combining an ant-based algorithm with an exact solver for (mixed-integer) linear programs is presented. A MAX-MIN ant system is developed to determine the principal production decisions, a LP/MIP solver is used to calculate the corresponding production quantities and inventory levels. Two different local search methods and an improvement strategy based on reduced mixed-integer problems are developed and integrated into the ant algorithm. This hybrid approach provides superior results for small and medium-sized problems in comparison to the existing approaches in the literature. For large-scale problems the performance of this method is among the best

Keywords: Ant colony optimization, Manufacturing, Material requirements planning, Mixed-integer programming

[29]

S. Alupoaei and S. Katkoori. Ant Colony System Application to Marcocell Overlap Removal. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 12(10):1118–1122, 2004.
[ bib ]

[30]

André R. S. Amaral. The Corridor Allocation Problem. Computers & Operations Research, 39(12):3325–3330, 2012.
[ bib | DOI ]

The corridor allocation problem (CAP) seeks an arrangement of facilities along a central corridor defined by two horizontal lines parallel to the x-axis of a Cartesian coordinate system. The objective is to minimize the total communication cost among facilities, while respecting two main conditions: (i) no space is allowed between two adjacent facilities; (ii) the left-most point of the arrangement on either line should have zero abscissa. The conditions (i) and (ii) are required in many applications such as the arrangement of rooms at office buildings or hospitals. The CAP is a NP-Hard problem. In this paper, a mixed-integer programming formulation of the CAP is proposed, which allows us to compute optimal layouts in reasonable time for problem instances of moderate sizes. Moreover, heuristic procedures are presented that can handle larger instances.

Keywords: Facility layout, Double row layout, Integer programming

[31]

C. Amir, A. Badr, and I Farag. A Fuzzy Logic Controller for Ant Algorithms. Computing and Information Systems, 11(2):26–34, 2007.
[ bib ]

[32]

Christophe Andrieu, Nando de Freitas, Arnaud Doucet, and Michael I. Jordan. An Introduction to MCMC for Machine Learning. Machine Learning, 50(1-2):5–43, 2003.
[ bib ]

[33]

Henrik Andersson, Kjetil Fagerholt, and Kirsti Hobbesland. Integrated maritime fleet deployment and speed optimization: Case study from RoRo shipping. Computers & Operations Research, 55:233–240, March 2015.
[ bib | DOI ]

[34]

K. A. Andersen, K. Jörnsten, and M. Lind. On bicriterion minimal spanning trees: An approximation. Computers & Operations Research, 23(12):1171–1182, 1996.
[ bib ]

[35]

Y. P. Aneja and K. P. K. Nair. Bicriteria Transportation Problem. Management Science, 25(1):73–78, 1979.
[ bib ]

[36]

Eric Angel, Evripidis Bampis, and Laurent Gourvés. Approximating the Pareto curve with local search for the bicriteria TSP(1,2) problem. Theoretical Computer Science, 310(1-3):135–146, 2004.
[ bib | DOI ]

Keywords: Archiving, Local search, Multicriteria TSP, Approximation algorithms

[37]

Daniel Angus and Clinton Woodward. Multiple Objective Ant Colony Optimisation. Swarm Intelligence, 3(1):69–85, 2009.
[ bib | DOI ]

[38]

Miguel F. Anjos and Manuel V. C. Vieira. Mathematical optimization approaches for facility layout problems: The state-of-the-art and future research directions. European Journal of Operational Research, 261(1):1–16, 2017.
[ bib ]

[39]

Kurt Anstreicher, Nathan Brixius, Jean-Pierre Goux, and Jeff Linderoth. Solving large quadratic assignment problems on computational grids. Mathematical Programming Series B, 91(3):563–588, February 2002.
[ bib | DOI ]

The quadratic assignment problem (QAP) is among the hardest combinatorial optimization problems. Some instances of size n = 30 have remained unsolved for decades. The solution of these problems requires both improvements in mathematical programming algorithms and the utilization of powerful computational platforms. In this article we describe a novel approach to solve QAPs using a state-of-the-art branch-and-bound algorithm running on a federation of geographically distributed resources known as a computational grid. Solution of QAPs of unprecedented complexity, including the nug30, kra30b, and tho30 instances, is reported.

[40]

David Applegate, Robert E. Bixby, Vašek Chvátal, and William J. Cook. Implementing the Dantzig-Fulkerson-Johnson Algorithm for Large Traveling Salesman Problems. Mathematical Programming Series B, 97(1–2):91–153, 2003.
[ bib | DOI ]

[41]

David Applegate, Robert E. Bixby, Vašek Chvátal, and William J. Cook. On the Solution of Traveling Salesman Problems. Documenta Mathematica, Extra Volume ICM III:645–656, 1998.
[ bib ]

[42]

J. S. Appleby, D. V. Blake, and E. A. Newman. Techniques for producing school timetables on a computer and their application to other scheduling problems. The Computer Journal, 3(4):237–245, 1961.
[ bib | DOI ]

[43]

David Applegate and William J. Cook. A Computational Study of the Job-Shop Scheduling Problem. ORSA Journal on Computing, 3(2):149–156, 1991.
[ bib ]

[44]

David Applegate, William J. Cook, and André Rohe. Chained Lin-Kernighan for Large Traveling Salesman Problems. INFORMS Journal on Computing, 15(1):82–92, 2003.
[ bib | DOI ]

[45]

David Applegate, Robert E. Bixby, Vašek Chvátal, William J. Cook, D. Espinoza, M. Goycoolea, and Keld Helsgaun. Certification of an Optimal TSP Tour Through 85,900 Cities. Operations Research Letters, 37(1):11–15, 2009.
[ bib ]

[46]

Claus Aranha, Christian Leonardo Camacho-Villalón, Felipe Campelo, Marco Dorigo, Rubén Ruiz, Marc Sevaux, Kenneth Sörensen, and Thomas Stützle. Metaphor-based Metaheuristics, a Call for Action: the Elephant in the Room. Swarm Intelligence, 16(1):1–6, 2022.
[ bib | DOI ]

[47]

Claudia Archetti, Martin Savelsbergh, and M. Grazia Speranza. The Vehicle Routing Problem with Occasional Drivers. European Journal of Operational Research, 254(2):472–480, 2016.
[ bib | DOI ]

[48]

Florian Arnold, Ítalo Santana, Kenneth Sörensen, and Thibaut Vidal. PILS: Exploring high-order neighborhoods by pattern mining and injection. Arxiv preprint arXiv:1912.11462 [cs.AI], 2019.
[ bib | DOI ]

[49]

Florian Arnold and Kenneth Sörensen. Knowledge-guided local search for the vehicle routing problem. Computers & Operations Research, 105:32–46, 2019.
[ bib | DOI ]

[50]

Florian Arnold and Kenneth Sörensen. What makes a VRP solution good? The generation of problem-specific knowledge for heuristics. Computers & Operations Research, 106:280–288, 2019.
[ bib | DOI ]

[51]

Marvin A. Arostegui Jr, Sukran N. Kadipasaoglu, and Basheer M. Khumawala. An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problems. International Journal of Production Economics, 103(2):742–754, 2006.
[ bib ]

[52]

José Elias C. Arroyo and V. A. Armentano. A partial enumeration heuristic for multi-objective flowshop scheduling problems. Journal of the Operational Research Society, 55(9):1000–1007, 2004.
[ bib ]

[53]

José Elias C. Arroyo and V. A. Armentano. Genetic local search for multi-objective flowshop scheduling problems. European Journal of Operational Research, 167(3):717–738, 2005.
[ bib ]

Keywords: Multicriteria Scheduling

[54]

José Elias C. Arroyo and Joseph Y.-T. Leung. An Effective Iterated Greedy Algorithm for Scheduling Unrelated Parallel Batch Machines with Non-identical Capacities and Unequal Ready Times. Computers and Industrial Engineering, 105:84–100, 2017.
[ bib ]

[55]

Etor Arza, Josu Ceberio, Ekhine Irurozki, and Aritz Pérez. Comparing Two Samples Through Stochastic Dominance: A Graphical Approach. Journal of Computational and Graphical Statistics, pp.  1–38, June 2022.
[ bib | DOI ]

[56]

N. Ascheuer, Matteo Fischetti, and M. Grötschel. Solving asymmetric travelling salesman problem with time windows by branch-and-cut. Mathematical Programming, 90:475–506, 2001.
[ bib ]

[57]

John-Alexander M. Assael, Ziyu Wang, and Nando de Freitas. Heteroscedastic Treed Bayesian Optimisation. Arxiv preprint arXiv:1410.7172, 2014.
[ bib | DOI ]

Keywords: Treed-GP

[58]

Alper Atamtürk. On the facets of the mixed–integer knapsack polyhedron. Mathematical Programming, 98(1):145–175, 2003.
[ bib | DOI ]

[59]

Charles Audet, Jean Bigeon, Dominique Cartier, Sébastien Le Digabel, and Ludovic Salomon. Performance indicators in multiobjective optimization. European Journal of Operational Research, 292(2):397–422, 2021.
[ bib | DOI ]

[60]

Charles Audet, Cong-Kien Dang, and Dominique Orban. Optimization of Algorithms with OPAL. Mathematical Programming Computation, 6(3):233–254, 2014.
[ bib ]

[61]

P. Audze and Vilnis Eglãjs. New approach to the design of multifactor experiments. Problems of Dynamics and Strengths, 35:104–107, 1977. (in Russian).
[ bib ]

[62]

Charles Audet and Dominique Orban. Finding Optimal Algorithmic Parameters Using Derivative-Free Optimization. SIAM Journal on Optimization, 17(3):642–664, 2006.
[ bib | DOI ]

Keywords: mesh adaptive direct search; pattern search

[63]

Peter Auer. Using Confidence Bounds for Exploitation-Exploration Trade-offs. Journal of Machine Learning Research, 3:397–422, November 2002.
[ bib ]

We show how a standard tool from statistics — namely confidence bounds — can be used to elegantly deal with situations which exhibit an exploitation-exploration trade-off. Our technique for designing and analyzing algorithms for such situations is general and can be applied when an algorithm has to make exploitation-versus-exploration decisions based on uncertain information provided by a random process. We apply our technique to two models with such an exploitation-exploration trade-off. For the adversarial bandit problem with shifting our new algorithm suffers only O((ST)^1/2) regret with high probability over T trials with S shifts. Such a regret bound was previously known only in expectation. The second model we consider is associative reinforcement learning with linear value functions. For this model our technique improves the regret from O(T^3/4) to O(T^1/2).

[64]

Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2-3):235–256, 2002.
[ bib ]

[65]

Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler. Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications. Theoretical Computer Science, 425:75–103, 2012.
[ bib | DOI ]

[66]

Mustafa Avci and Seyda Topaloglu. A Multi-start Iterated Local Search Algorithm for the Generalized Quadratic Multiple Knapsack Problem. Computers & Operations Research, 83:54–65, 2017.
[ bib ]

[67]

Andreea Avramescu, Richard Allmendinger, and Manuel López-Ibáñez. Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models. Arxiv preprint arXiv:2105.12699 [econ.GN], 2021.
[ bib | http ]

[68]

Doǧan Aydın, Gürcan Yavuz, and Thomas Stützle. ABC-X: A Generalized, Automatically Configurable Artificial Bee Colony Framework. Swarm Intelligence, 11(1):1–38, 2017.
[ bib ]

[69]

Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez, and Matthieu Parizy. A Study of Scalarisation Techniques for Multi-Objective QUBO Solving. Arxiv preprint arXiv:2210.11321, 2022.
[ bib | DOI ]

[70]

Mahdi Aziz and Mohammad-H. Tayarani-N. An adaptive memetic Particle Swarm Optimization algorithm for finding large-scale Latin hypercube designs. Engineering Applications of Artificial Intelligence, 36:222–237, 2014.
[ bib | DOI ]

Keywords: F-race

[71]

François Bachoc, Céline Helbert, and Victor Picheny. Gaussian process optimization with failures: Classification and convergence proof. Journal of Global Optimization, 2020.
[ bib | DOI | epub ]

We consider the optimization of a computer model where each simulation either fails or returns a valid output performance. We first propose a new joint Gaussian process model for classification of the inputs (computation failure or success) and for regression of the performance function. We provide results that allow for a computationally efficient maximum likelihood estimation of the covariance parameters, with a stochastic approximation of the likelihood gradient. We then extend the classical improvement criterion to our setting of joint classification and regression. We provide an efficient computation procedure for the extended criterion and its gradient. We prove the almost sure convergence of the global optimization algorithm following from this extended criterion. We also study the practical performances of this algorithm, both on simulated data and on a real computer model in the context of automotive fan design.

Keywords: crashed simulation; latent gaussian process; automotive fan design; industrial application; GP classification; Expected Feasible Improvement with Gaussian Process Classification with signs; EFI GPC sign

[72]

Johannes Bader and Eckart Zitzler. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization. Evolutionary Computation, 19(1):45–76, 2011.
[ bib | DOI ]

[73]

Hossein Baharmand, Tina Comes, and Matthieu Lauras. Bi-objective multi-layer location– allocation model for the immediate aftermath of sudden-onset disasters. Transportation Research Part E: Logistics and Transportation Review, 127:86–110, 2019.
[ bib | DOI ]

Locating distribution centers is critical for humanitarians in the immediate aftermath of a sudden-onset disaster. A major challenge lies in balancing the complexity and uncertainty of the problem with time and resource constraints. To address this problem, we propose a location-allocation model that divides the topography of affected areas into multiple layers; considers constrained number and capacity of facilities and fleets; and allows decision-makers to explore trade-offs between response time and logistics costs. To illustrate our theoretical work, we apply the model to a real dataset from the 2015 Nepal earthquake response. For this case, our method results in a considerable reduction of logistics costs.

[74]

Monya Baker. Is there a reproducibility crisis? Nature, 533:452–454, 2016.
[ bib ]

[75]

Edward K. Baker. An Exact Algorithm for the Time-Constrained Traveling Salesman Problem. Operations Research, 31(5):938–945, 1983.
[ bib | DOI ]

[76]

Burcu Balcik and Benita M. Beamon. Facility location in humanitarian relief. International Journal of Logistics, 11(2):101–121, 2008.
[ bib ]

[77]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo. Adaptive Sampling Size and Importance Sampling in Estimation-based Local Search for the Probabilistic Traveling Salesman Problem. European Journal of Operational Research, 199(1):98–110, 2009.
[ bib ]

[78]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo. Estimation-based Metaheuristics for the Probabilistic Travelling Salesman Problem. Computers & Operations Research, 37(11):1939–1951, 2010.
[ bib | DOI ]

[79]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo. Estimation-based Metaheuristics for the Single Vehicle Routing Problem with Stochastic Demands and Customers. Computational Optimization and Applications, 61(2):463–487, 2015.
[ bib | DOI ]

[80]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Zhi Yuan, and Marco Dorigo. Estimation-based Ant Colony Optimization Algorithms for the Probabilistic Travelling Salesman Problem. Swarm Intelligence, 3(3):223–242, 2009.
[ bib ]

[81]

Egon Balas and M. C. Carrera. A Dynamic Subgradient-based Branch and Bound Procedure for Set Covering. Operations Research, 44(6):875–890, 1996.
[ bib ]

[82]

Egon Balas and C. Martin. Pivot and Complement–A Heuristic for 0–1 Programming. Management Science, 26(1):86–96, 1980.
[ bib ]

[83]

Egon Balas and M. W. Padberg. Set Partitioning: A Survey. SIAM Review, 18:710–760, 1976.
[ bib ]

[84]

Egon Balas and Neil Simonetti. Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study. INFORMS Journal on Computing, 13(1):56–75, 2001.
[ bib | DOI ]

Consider the following restricted (symmetric or asymmetric) traveling-salesman problem (TSP): given an initial ordering of the n cities and an integer k > 0, find a minimum-cost feasible tour, where a feasible tour is one in which city i precedes city j whenever j >= i + k in the initial ordering. Balas (1996) has proposed a dynamic-programming algorithm that solves this problem in time linear in n, though exponential in k. Some important real-world problems are amenable to this model or some of its close relatives. The algorithm of Balas (1996) constructs a layered network with a layer of nodes for each position in the tour, such that source-sink paths in this network are in one-to-one correspondence with tours that satisfy the postulated precedence constraints. In this paper we discuss an implementation of the dynamic-programming algorithm for the general case when the integer k is replaced with city-specific integers k(j), j = 1, . . ., n. We discuss applications to, and computational experience with, TSPs with time windows, a model frequently used in vehicle routing as well as in scheduling with setup, release and delivery times. We also introduce a new model, the TSP with target times, applicable to Just-in-Time scheduling problems. Finally for TSPs that have no precedence restrictions, we use the algorithm as a heuristic that finds in linear time a local optimum over an exponential-size neighborhood. For this case, we implement an iterated version of our procedure, based on contracting some arcs of the tour produced by a first application of the algorithm, then reapplying the algorithm to the shrunken graph with the same k.

Keywords: tsptw

[85]

Egon Balas and A. Vazacopoulos. Guided Local Search with Shifting Bottleneck for Job Shop Scheduling. Management Science, 44(2):262–275, 1998.
[ bib ]

[86]

Steven C. Bankes. Tools and techniques for developing policies for complex and uncertain systems. Proceedings of the National Academy of Sciences, 99(suppl 3):7263–7266, 2002.
[ bib | DOI ]

Agent-based models (ABM) are examples of complex adaptive systems, which can be characterized as those systems for which no model less complex than the system itself can accurately predict in detail how the system will behave at future times. Consequently, the standard tools of policy analysis, based as they are on devising policies that perform well on some best estimate model of the system, cannot be reliably used for ABM. This paper argues that policy analysis by using ABM requires an alternative approach to decision theory. The general characteristics of such an approach are described, and examples are provided of its application to policy analysis.ABM, agent-based model

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Eduardo Batista de Moraes Barbosa, Edson Luiz Francça Senne, and Messias Borges Silva. Improving the Performance of Metaheuristics: An Approach Combining Response Surface Methodology and Racing Algorithms. International Journal of Engineering Mathematics, 2015:Article ID 167031, 2015.
[ bib | DOI ]

Keywords: F-race

[88]

Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58:82–115, June 2020.
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Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel López-Ibáñez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, and Thomas Weise. Benchmarking in Optimization: Best Practice and Open Issues. Arxiv preprint arXiv:2007.03488 [cs.NE], 2020.
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Richard S. Barr, Bruce L. Golden, James P. Kelly, Mauricio G. C. Resende, and Jr. William R. Stewart. Designing and Reporting on Computational Experiments with Heuristic Methods. Journal of Heuristics, 1(1):9–32, 1995.
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Cynthia Barnhart, Ellis L. Johnson, George L. Nemhauser, Martin W. P. Savelsbergh, and Pamela H. Vance. Branch-and-price: Column generation for solving huge integer programs. Operations Research, 46(3):316–329, 1998.
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Erin Bartholomew and Jan H. Kwakkel. On considering robustness in the search phase of Robust Decision Making: A comparison of Many-Objective Robust Decision Making, multi-scenario Many-Objective Robust Decision Making, and Many Objective Robust Optimization. Environmental Modelling & Software, 127:104699, 2020.
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Elias Bareinboim and Judea Pearl. Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences, 113(27):7345–7352, 2016.
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Thomas Bartz-Beielstein and Martin Zaefferer. Model-based methods for continuous and discrete global optimization. Applied Soft Computing, 55:154–167, June 2017.
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Atanu Basu and L. Neil Frazer. Rapid Determination of the Critical Temperature in Simulated Annealing Inversion. Science, 249(4975):1409–1412, 1990.
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Roberto Battiti and Andrea Passerini. Brain-Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker. IEEE Transactions on Evolutionary Computation, 14(5):671–687, 2010.
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Errata: DTLZ6 and DTLZ7 in the paper are actually DTLZ7 and DTLZ8 in [1757]

Keywords: BC-EMOA

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Roberto Battiti and M. Protasi. Reactive Search, A History-Based Heuristic for MAX-SAT. ACM Journal of Experimental Algorithmics, 2, 1997.
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Michele Battistutta, Andrea Schaerf, and Tommaso Urli. Feature-based Tuning of Single-stage Simulated Annealing for Examination Timetabling. Annals of Operations Research, 252(2):239–254, 2017.
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Roberto Battiti and Giampietro Tecchiolli. Simulated annealing and Tabu search in the long run: A comparison on QAP tasks. Computer and Mathematics with Applications, 28(6):1–8, 1994.
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Roberto Battiti and Giampietro Tecchiolli. The Reactive Tabu Search. ORSA Journal on Computing, 6(2):126–140, 1994.
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Roberto Battiti and Giampietro Tecchiolli. The continuous reactive tabu search: blending combinatorial optimization and stochastic search for global optimization. Annals of Operations Research, 63(2):151–188, 1996.
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J. Bautista and J. Pereira. Ant algorithms for a time and space constrained assembly line balancing problem. European Journal of Operational Research, 177(3):2016–2032, 2007.
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William J. Baumol. Management models and industrial applications of linear programming. Naval Research Logistics Quarterly, 9(1):63–64, 1962.
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[104]

John Baxter. Local Optima Avoidance in Depot Location. Journal of the Operational Research Society, 32(9):815–819, 1981.
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John E. Beasley and P. C. Chu. A Genetic Algorithm for the Set Covering Problem. European Journal of Operational Research, 94(2):392–404, 1996.
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John E. Beasley and P. C. Chu. A Genetic Algorithm for the Multidimensional Knapsack Problem. Journal of Heuristics, 4(1):63–86, 1998.
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Jennifer Bealt, Duncan Shaw, Chris M. Smith, and Manuel López-Ibáñez. Peer Reviews for Making Cities Resilient: A Systematic Literature Review. International Journal of Emergency Management, 15(4):334–359, 2019.
[ bib | DOI ]

Peer reviews are a unique governance tool that use expertise from one city or country to assess and strengthen the capabilities of another. Peer review tools are gaining momentum in disaster management and remain an important but understudied topic in risk governance. Methodologies to conduct a peer review are still in their infancy. To enhance these, a systematic literature review (SLR) of academic and non-academic literature was conducted on city resilience peer reviews. Thirty-three attributes of resilience are identified, which provides useful insights into how research and practice can inform risk governance, and utilise peer reviews, to drive meaningful change. Moreover, it situates the challenges associated with resilience building tools within risk governance to support the development of interdisciplinary perspectives for integrated city resilience frameworks. Results of this research have been used to develop a peer review methodology and an international standard on conducting peer reviews for disaster risk reduction.

Keywords: city resilience, city peer review, disaster risk governance

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John E. Beasley. OR-Library: distributing test problems by electronic mail. Journal of the Operational Research Society, pp.  1069–1072, 1990. Currently available from http://people.brunel.ac.uk/~mastjjb/jeb/info.html.
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J. Behnamian and S. M. T. Fatemi Ghomi. Hybrid Flowshop Scheduling with Machine and Resource-dependent Processing Times. Applied Mathematical Modelling, 35(3):1107–1123, 2011.
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Richard Bellman. The theory of dynamic programming. Bulletin of the American Mathematical Society, 60:503–515, 1954.
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Ruggero Bellio, Sara Ceschia, Luca Di Gaspero, Andrea Schaerf, and Tommaso Urli. Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem. Computers & Operations Research, 65:83–92, 2016.
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Jon Louis Bentley. Fast Algorithms for Geometric Traveling Salesman Problems. ORSA Journal on Computing, 4(4):387–411, 1992.
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Una Benlic and Jin-Kao Hao. Breakout Local Search for the Quadratic Assignment Problem. Applied Mathematics and Computation, 219(9):4800–4815, 2013.
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Calem J. Bendell, Shalon Liu, Tristan Aumentado-Armstrong, Bogdan Istrate, Paul T. Cernek, Samuel Khan, Sergiu Picioreanu, Michael Zhao, and Robert A. Murgita. Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor. BMC Bioinformatics, 15:82, 2014.
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Yoshua Bengio, Andrea Lodi, and Antoine Prouvost. Machine learning for combinatorial optimization: A methodological tour d'horizon. European Journal of Operational Research, 290(2):405–421, 2021.
[ bib | DOI ]

This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.

Keywords: Combinatorial optimization, Machine learning, Branch and bound, Mixed-integer programming solvers

[116]

Alexander Javier Benavides and Marcus Ritt. Two Simple and Effective Heuristics for Minimizing the Makespan in Non-permutation Flow Shops. Computers & Operations Research, 66:160–169, 2016.
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[117]

J. F. Benders. Partitioning Procedures for Solving Mixed-variables Programming Problems. Numerische Mathematik, 4(3):238–252, 1962.
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[118]

Jon Louis Bentley. Multidimensional Divide-and-conquer. Communications of the ACM, 23(4):214–229, 1980.
[ bib | DOI ]

Most results in the field of algorithm design are single algorithms that solve single problems. In this paper we discuss multidimensional divide-and-conquer, an algorithmic paradigm that can be instantiated in many different ways to yield a number of algorithms and data structures for multidimensional problems. We use this paradigm to give best-known solutions to such problems as the ECDF, maxima, range searching, closest pair, and all nearest neighbor problems. The contributions of the paper are on two levels. On the first level are the particular algorithms and data structures given by applying the paradigm. On the second level is the more novel contribution of this paper: a detailed study of an algorithmic paradigm that is specific enough to be described precisely yet general enough to solve a wide variety of problems.

[119]

James S. Bergstra and Yoshua Bengio. Random Search for Hyper-Parameter Optimization. Journal of Machine Learning Research, 13:281–305, 2012.
[ bib | epub ]

Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid. Empirical evidence comes from a comparison with a large previous study that used grid search and manual search to configure neural networks and deep belief networks. Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Granting random search the same computational budget, random search finds better models by effectively searching a larger, less promising configuration space. Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search over the same 32-dimensional configuration space found statistically equal performance on four of seven data sets, and superior performance on one of seven. A Gaussian process analysis of the function from hyper-parameters to validation set performance reveals that for most data sets only a few of the hyper-parameters really matter, but that different hyper-parameters are important on different data sets. This phenomenon makes grid search a poor choice for configuring algorithms for new data sets. Our analysis casts some light on why recent "High Throughput" methods achieve surprising success: they appear to search through a large number of hyper-parameters because most hyper-parameters do not matter much. We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper-parameter optimization algorithms.

[120]

Loïc Berger, Johannes Emmerling, and Massimo Tavoni. Managing catastrophic climate risks under model uncertainty aversion. Management Science, 63(3):749–765, 2017.
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[121]

Livio Bertacco, Matteo Fischetti, and Andrea Lodi. A feasibility pump heuristic for general mixed-integer problems. Discrete Optimization, 4(1):63–76, 2007.
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[122]

Dimitris Bertsimas and Nathan Kallus. From predictive to prescriptive analytics. Management Science, 66(3):1025–1044, 2020.
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[123]

Felix Berkenkamp, Andreas Krause, and Angela P. Schoellig. Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics. Arxiv preprint arXiv:1602.04450, 2016.
[ bib | http ]

Keywords: Safe Optimization, SafeOpt

[124]

Felix Berkenkamp, Andreas Krause, and Angela P. Schoellig. Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics. Machine Learning, June 2021.
[ bib | DOI ]

Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algorithms. Data-efficient optimization algorithms, such as Bayesian optimization, have been used to automate this process. During experiments on real-world systems such as robotic platforms these methods can evaluate unsafe parameters that lead to safety-critical system failures and can destroy the system. Recently, a safe Bayesian optimization algorithm, called SafeOpt, has been developed, which guarantees that the performance of the system never falls below a critical value; that is, safety is defined based on the performance function. However, coupling performance and safety is often not desirable in practice, since they are often opposing objectives. In this paper, we present a generalized algorithm that allows for multiple safety constraints separate from the objective. Given an initial set of safe parameters, the algorithm maximizes performance but only evaluates parameters that satisfy safety for all constraints with high probability. To this end, it carefully explores the parameter space by exploiting regularity assumptions in terms of a Gaussian process prior. Moreover, we show how context variables can be used to safely transfer knowledge to new situations and tasks. We provide a theoretical analysis and demonstrate that the proposed algorithm enables fast, automatic, and safe optimization of tuning parameters in experiments on a quadrotor vehicle.

Preprint: http://arxiv.org/abs/1602.04450

[125]

Dimitri P. Bertsekas, John N. Tsitsiklis, and Cynara Wu. Rollout Algorithms for Combinatorial Optimization. Journal of Heuristics, 3(3):245–262, 1997.
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[126]

Judith O. Berkey and Pearl Y. Wang. Two-dimensional finite bin-packing algorithms. Journal of the Operational Research Society, 38(5):423–429, 1987.
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[127]

Nicola Beume, Carlos M. Fonseca, Manuel López-Ibáñez, Luís Paquete, and Jan Vahrenhold. On the complexity of computing the hypervolume indicator. IEEE Transactions on Evolutionary Computation, 13(5):1075–1082, 2009.
[ bib | DOI ]

The goal of multi-objective optimization is to find a set of best compromise solutions for typically conflicting objectives. Due to the complex nature of most real-life problems, only an approximation to such an optimal set can be obtained within reasonable (computing) time. To compare such approximations, and thereby the performance of multi-objective optimizers providing them, unary quality measures are usually applied. Among these, the hypervolume indicator (or S-metric) is of particular relevance due to its favorable properties. Moreover, this indicator has been successfully integrated into stochastic optimizers, such as evolutionary algorithms, where it serves as a guidance criterion for finding good approximations to the Pareto front. Recent results show that computing the hypervolume indicator can be seen as solving a specialized version of Klee's Measure Problem. In general, Klee's Measure Problem can be solved with O(n log n + n^d/2log n) comparisons for an input instance of size n in d dimensions; as of this writing, it is unknown whether a lower bound higher than Ω(n log n) can be proven. In this article, we derive a lower bound of Ω(nlog n) for the complexity of computing the hypervolume indicator in any number of dimensions d>1 by reducing the so-called UniformGap problem to it. For the three dimensional case, we also present a matching upper bound of O(nlog n) comparisons that is obtained by extending an algorithm for finding the maxima of a point set.

[128]

Nicola Beume, Boris Naujoks, and Michael T. M. Emmerich. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653–1669, 2007.
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[129]

Hans-Georg Beyer and Hans-Paul Schwefel. Evolution Strategies: A Comprehensive Introduction. Natural Computing, 1:3–52, 2002.
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[130]

Hans-Georg Beyer, Hans-Paul Schwefel, and Ingo Wegener. How to analyse evolutionary algorithms. Theoretical Computer Science, 287(1):101–130, 2002.
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[131]

Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Automatic Component-Wise Design of Multi-Objective Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 20(3):403–417, 2016.
[ bib | DOI | supplementary material ]

[132]

Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms. Evolutionary Computation, 26(4):621–656, 2018.
[ bib | DOI | supplementary material ]

Research on multi-objective evolutionary algorithms (MOEAs) has produced over the past decades a large number of algorithms and a rich literature on performance assessment tools to evaluate and compare them. Yet, newly proposed MOEAs are typically compared against very few, often a decade older MOEAs. One reason for this apparent contradiction is the lack of a common baseline for comparison, with each subsequent study often devising its own experimental scenario, slightly different from other studies. As a result, the state of the art in MOEAs is a disputed topic. This article reports a systematic, comprehensive evaluation of a large number of MOEAs that covers a wide range of experimental scenarios. A novelty of this study is the separation between the higher-level algorithmic components related to multi-objective optimization (MO), which characterize each particular MOEA, and the underlying parameters-such as evolutionary operators, population size, etc.-whose configuration may be tuned for each scenario. Instead of relying on a common or "default" parameter configuration that may be low-performing for particular MOEAs or scenarios and unintentionally biased, we tune the parameters of each MOEA for each scenario using automatic algorithm configuration methods. Our results confirm some of the assumed knowledge in the field, while at the same time they provide new insights on the relative performance of MOEAs for many-objective problems. For example, under certain conditions, indicator-based MOEAs are more competitive for such problems than previously assumed. We also analyze problem-specific features affecting performance, the agreement between performance metrics, and the improvement of tuned configurations over the default configurations used in the literature. Finally, the data produced is made publicly available to motivate further analysis and a baseline for future comparisons.

[133]

Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms. Evolutionary Computation, 28(2):195–226, 2020.
[ bib | DOI | supplementary material ]

A recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering (i) parameter tuning through automatic configuration, (ii) a wide range of different setups, and (iii) various performance metrics. Here, we automatically devise MOEAs with verified state-of-the-art performance for multi- and many-objective continuous optimization. Our work is based on two main considerations. The first is that high-performing algorithms can be obtained from a configurable algorithmic framework in an automated way. The second is that multiple performance metrics may be required to guide this automatic design process. In the first part of this work, we extend our previously proposed algorithmic framework, increasing the number of MOEAs, underlying evolutionary algorithms, and search paradigms that it comprises. These components can be combined following a general MOEA template, and an automatic configuration method is used to instantiate high-performing MOEA designs that optimize a given performance metric and present state-of-the-art performance. In the second part, we propose a multiobjective formulation for the automatic MOEA design, which proves critical for the context of many-objective optimization due to the disagreement of established performance metrics. Our proposed formulation leads to an automatically designed MOEA that presents state-of-the-art performance according to a set of metrics, rather than a single one.

[134]

Leonora Bianchi, Mauro Birattari, M. Manfrin, M. Mastrolilli, Luís Paquete, O. Rossi-Doria, and Tommaso Schiavinotto. Hybrid Metaheuristics for the Vehicle Routing Problem with Stochastic Demands. Journal of Mathematical Modelling and Algorithms, 5(1):91–110, 2006.
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[135]

Leonora Bianchi, Marco Dorigo, L. M. Gambardella, and Walter J. Gutjahr. A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2):239–287, 2009.
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[136]

M. Binois, D. Ginsbourger, and O. Roustant. Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. European Journal of Operational Research, 243(2):386–394, 2015.
[ bib | DOI ]

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob'ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob'ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Keywords: Attainment function, Expected Hypervolume Improvement, Kriging, Multi-objective optimization, Vorob'ev expectation

[137]

Mauro Birattari, Prasanna Balaprakash, Thomas Stützle, and Marco Dorigo. Estimation Based Local Search for Stochastic Combinatorial Optimization. INFORMS Journal on Computing, 20(4):644–658, 2008.
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[138]

Mauro Birattari, Paola Pellegrini, and Marco Dorigo. On the invariance of ant colony optimization. IEEE Transactions on Evolutionary Computation, 11(6):732–742, 2007.
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[139]

Mauro Birattari, M. Zlochin, and Marco Dorigo. Towards a theory of practice in metaheuristics design: A machine learning perspective. Theoretical Informatics and Applications, 40(2):353–369, 2006.
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[140]

Francesco Biscani, Dario Izzo, and Chit Hong Yam. A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation. Arxiv preprint arXiv:1004.3824, 2010.
[ bib | http ]

A software platform for global optimisation, called PaGMO, has been developed within the Advanced Concepts Team (ACT) at the European Space Agency, and was recently released as an open-source project. PaGMO is built to tackle high-dimensional global optimisation problems, and it has been successfully used to find solutions to real-life engineering problems among which the preliminary design of interplanetary spacecraft trajectories - both chemical (including multiple flybys and deep-space maneuvers) and low-thrust (limited, at the moment, to single phase trajectories), the inverse design of nano-structured radiators and the design of non-reactive controllers for planetary rovers. Featuring an arsenal of global and local optimisation algorithms (including genetic algorithms, differential evolution, simulated annealing, particle swarm optimisation, compass search, improved harmony search, and various interfaces to libraries for local optimisation such as SNOPT, IPOPT, GSL and NLopt), PaGMO is at its core a C++ library which employs an object-oriented architecture providing a clean and easily-extensible optimisation framework. Adoption of multi-threaded programming ensures the efficient exploitation of modern multi-core architectures and allows for a straightforward implementation of the island model paradigm, in which multiple populations of candidate solutions asynchronously exchange information in order to speed-up and improve the optimisation process. In addition to the C++ interface, PaGMO's capabilities are exposed to the high-level language Python, so that it is possible to easily use PaGMO in an interactive session and take advantage of the numerous scientific Python libraries available.

Keywords: PaGMO

[141]

Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, and Marius Thomas Lindauer. Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2):e1484, 2023.
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[142]

Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Thomas Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, and Joaquin Vanschoren. ASlib: A Benchmark Library for Algorithm Selection. Artificial Intelligence, 237:41–58, 2016.
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[143]

Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, and Zachary M. Jones. mlr: Machine Learning in R. Journal of Machine Learning Research, 17(170):1–5, 2016.
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[144]

Xavier Blasco, Juan M. Herrero, Javier Sanchis, and Manuel Martínez. A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20):3908–3924, 2008.
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[145]

Craig Blackmore, Oliver Ray, and Kerstin Eder. Automatically Tuning the GCC Compiler to Optimize the Performance of Applications Running on Embedded Systems. Arxiv preprint arXiv:1703.08228, 2017.
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[146]

María J. Blesa and Christian Blum. Finding edge-disjoint paths in networks by means of artificial ant colonies. Journal of Mathematical Modelling and Algorithms, 6(3):361–391, 2007.
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[147]

Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Robbert Reijnen, Yingqian Zhang, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel López-Ibáñez, and Ekhine Irurozki. The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems. Artificial Intelligence, 319:103918, 2023.
[ bib | DOI ]

This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the classical combinatorial optimization problems, with many variants inspired by real-world applications. This first competition asked the participants to develop algorithms to solve an orienteering problem with stochastic weights and time windows (OPSWTW). It focused on two learning approaches: surrogate-based optimization and deep reinforcement learning. In this paper, we describe the problem, the competition setup, and the winning methods, and give an overview of the results. The winning methods described in this work have advanced the state-of-the-art in using AI for stochastic routing problems. Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers. The simulator of the problem has been made open-source and can be used by other researchers as a benchmark for new learning-based methods. The instances and code for the competition are available at https://github.com/paulorocosta/ai-for-tsp-competition.

Keywords: AI for TSP competition, Travelling salesman problem, Routing problem, Stochastic combinatorial optimization, Surrogate-based optimization, Deep reinforcement learning

[148]

Christian Blum. Beam-ACO—Hybridizing Ant Colony Optimization with Beam Search: An Application to Open Shop Scheduling. Computers & Operations Research, 32(6):1565–1591, 2005.
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[149]

Christian Blum. Beam-ACO for simple assembly line balancing. INFORMS Journal on Computing, 20(4):618–627, 2008.
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[150]

Christian Blum, María J. Blesa, and Manuel López-Ibáñez. Beam search for the longest common subsequence problem. Computers & Operations Research, 36(12):3178–3186, 2009.
[ bib | DOI ]

The longest common subsequence problem is a classical string problem that concerns finding the common part of a set of strings. It has several important applications, for example, pattern recognition or computational biology. Most research efforts up to now have focused on solving this problem optimally. In comparison, only few works exist dealing with heuristic approaches. In this work we present a deterministic beam search algorithm. The results show that our algorithm outperforms the current state-of-the-art approaches not only in solution quality but often also in computation time.

[151]

Christian Blum, Borja Calvo, and María J. Blesa. FrogCOL and FrogMIS: New Decentralized Algorithms for Finding Large Independent Sets in Graphs. Swarm Intelligence, 9(2-3):205–227, 2015.
[ bib | DOI ]

Keywords: irace

[152]

Christian Blum and Marco Dorigo. The hyper-cube framework for ant colony optimization. IEEE Transactions on Systems, Man, and Cybernetics – Part B, 34(2):1161–1172, 2004.
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[153]

Christian Blum and Marco Dorigo. Search Bias in Ant Colony Optimization: On the Role of Competition-Balanced Systems. IEEE Transactions on Evolutionary Computation, 9(2):159–174, 2005.
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Christian Blum and M. Sampels. An Ant Colony Optimization Algorithm for Shop Scheduling Problems. Journal of Mathematical Modelling and Algorithms, 3(3):285–308, 2004.
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Christian Blum, M. Yábar Vallès, and María J. Blesa. An ant colony optimization algorithm for DNA sequencing by hybridization. Computers & Operations Research, 35(11):3620–3635, 2008.
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Andrea F. Bocchese, Chris Fawcett, Mauro Vallati, Alfonso E. Gerevini, and Holger H. Hoos. Performance robustness of AI planners in the 2014 International Planning Competition. AI Communications, 31(6):445–463, December 2018.
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Solver competitions have been used in many areas of AI to assess the current state of the art and guide future research and development. AI planning is no exception, and the International Planning Competition (IPC) has been frequently run for nearly two decades. Due to the organisational and computational burden involved in running these competitions, solvers are generally compared using a single homogeneous hardware and software environment for all competitors. To what extent does the specific choice of hardware and software environment have an effect on solver performance, and is that effect distributed equally across the competing solvers? In this work, we use the competing planners and benchmark instance sets from the 2014 IPC to investigate these two questions. We recreate the 2014 IPC Optimal and Agile tracks on two distinct hardware environments and eight distinct software environments. We show that solver performance varies significantly based on the hardware and software environment, and that this variation is not equal for all planners. Furthermore, the observed variation is sufficient to change the competition rankings, including the top-ranked planners for some tracks.

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This paper describes new simulated annealing (SA) algorithms for the resource-constrained project scheduling problem (RCPSP) and its multiple mode version (MRCPSP). The objective function considered is minimisation of the makespan. The conventional SA search scheme is replaced by a new design that takes into account the specificity of the solution space of project scheduling problems. For RCPSP, the search was based on an alternated activity and time incrementing process, and all parameters were set after preliminary statistical experiments done on test instances. For MRCPSP, we introduced an original approach using two embedded search loops alternating activity and mode neighbourhood exploration. The performance evaluation done on the benchmark instances available in the literature proved the efficiency of both adaptations that are currently among the most competitive algorithms for these problems.

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Karl Bringmann, Sergio Cabello, and Michael T. M. Emmerich. Maximum volume subset selection for anchored boxes. Arxiv preprint arXiv:1803.00849, 2018.
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Let B be a set of n axis-parallel boxes in R^d such that each box has a corner at the origin and the other corner in the positive quadrant of R^d, and let k be a positive integer. We study the problem of selecting k boxes in B that maximize the volume of the union of the selected boxes. This research is motivated by applications in skyline queries for databases and in multicriteria optimization, where the problem is known as the hypervolume subset selection problem. It is known that the problem can be solved in polynomial time in the plane, while the best known running time in any dimension d ≥3 is Ω(k). We show that: The problem is NP-hard already in 3 dimensions. In 3 dimensions, we break the bound Ω(k), by providing an n^O(√(k)) algorithm. For any constant dimension d, we present an efficient polynomial-time approximation scheme.

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Karl Bringmann and Tobias Friedrich. Approximating the Least Hypervolume Contributor: NP-Hard in General, But Fast in Practice. Theoretical Computer Science, 425:104–116, 2012.
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Karl Bringmann and Tobias Friedrich. An efficient algorithm for computing hypervolume contributions. Evolutionary Computation, 18(3):383–402, 2010.
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Karl Bringmann and Tobias Friedrich. Convergence of hypervolume-based archiving algorithms. IEEE Transactions on Evolutionary Computation, 18(5):643–657, 2014.
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Proof that all nondecreasing (μ+ λ) archiving algorithms with λ< μ are ineffective.

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One of the four papers that proposed BFGS.

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Dimo Brockhoff, Johannes Bader, Lothar Thiele, and Eckart Zitzler. Directed Multiobjective Optimization Based on the Weighted Hypervolume Indicator. Journal of Multi-Criteria Decision Analysis, 20(5-6):291–317, 2013.
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Recently, there has been a large interest in set-based evolutionary algorithms for multi objective optimization. They are based on the definition of indicators that characterize the quality of the current population while being compliant with the concept of Pareto-optimality. It has been shown that the hypervolume indicator, which measures the dominated volume in the objective space, enables the design of efficient search algorithms and, at the same time, opens up opportunities to express user preferences in the search by means of weight functions. The present paper contains the necessary theoretical foundations and corresponding algorithms to (i) select appropriate weight functions, to (ii) transform user preferences into weight functions and to (iii) efficiently evaluate the weighted hypervolume indicator through Monte Carlo sampling. The algorithm W-HypE, which implements the previous concepts, is introduced, and the effectiveness of the search, directed towards the user's preferred solutions, is shown using an extensive set of experiments including the necessary statistical performance assessment.

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Dimo Brockhoff, Tea Tušar, Dejan Tušar, Tobias Wagner, Nikolaus Hansen, and Anne Auger. Biobjective performance assessment with the COCO platform. Arxiv preprint arXiv:1605.01746, 2016.
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Dimo Brockhoff, Tobias Wagner, and Heike Trautmann. R2 indicator-based multiobjective search. Evolutionary Computation, 23(3):369–395, 2015.
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Dimo Brockhoff and Eckart Zitzler. Objective Reduction in Evolutionary Multiobjective Optimization: Theory and Applications. Evolutionary Computation, 17(2):135–166, 2009.
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Many-objective problems represent a major challenge in the field of evolutionary multiobjective optimization, in terms of search efficiency, computational cost, decision making, visualization, and so on. This leads to various research questions, in particular whether certain objectives can be omitted in order to overcome or at least diminish the difficulties that arise when many, that is, more than three, objective functions are involved. This study addresses this question from different perspectives. First, we investigate how adding or omitting objectives affects the problem characteristics and propose a general notion of conflict between objective sets as a theoretical foundation for objective reduction. Second, we present both exact and heuristic algorithms to systematically reduce the number of objectives, while preserving as much as possible of the dominance structure of the underlying optimization problem. Third, we demonstrate the usefulness of the proposed objective reduction method in the context of both decision making and search for a radar waveform application as well as for well-known test functions.

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C. G. Broyden. The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations. IMA Journal of Applied Mathematics, 6(1):76–90, March 1970.
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This paper presents a more detailed analysis of a class of minimization algorithms, which includes as a special case the DFP (Davidon-Fletcher-Powell) method, than has previously appeared. Only quadratic functions are considered but particular attention is paid to the magnitude of successive errors and their dependence upon the initial matrix. On the basis of this a possible explanation of some of the observed characteristics of the class is tentatively suggested.

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Josu Ceberio, Ekhine Irurozki, Alexander Mendiburu, and José A. Lozano. A distance-based ranking model estimation of distribution algorithm for the flowshop scheduling problem. IEEE Transactions on Evolutionary Computation, 18(2):286–300, 2014.
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The aim of this paper is two-fold. First, we introduce a novel general estimation of distribution algorithm to deal with permutation-based optimization problems. The algorithm is based on the use of a probabilistic model for permutations called the generalized Mallows model. In order to prove the potential of the proposed algorithm, our second aim is to solve the permutation flowshop scheduling problem. A hybrid approach consisting of the new estimation of distribution algorithm and a variable neighborhood search is proposed. Conducted experiments demonstrate that the proposed algorithm is able to outperform the state-of-the-art approaches. Moreover, from the 220 benchmark instances tested, the proposed hybrid approach obtains new best known results in 152 cases. An in-depth study of the results suggests that the successful performance of the introduced approach is due to the ability of the generalized Mallows estimation of distribution algorithm to discover promising regions in the search space.

Keywords: Estimation of distribution algorithms,Generalized Mallows model,Permutation flowshop scheduling problem,Permutations-based optimization problems

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Sara Ceschia and Andrea Schaerf. Modeling and solving the dynamic patient admission scheduling problem under uncertainty. Artificial Intelligence in Medicine, 56(3):199–205, 2012.
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Sara Ceschia, Andrea Schaerf, and Thomas Stützle. Local Search Techniques for a Routing-packing Problem. Computers and Industrial Engineering, 66(4):1138–1149, 2013.
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In this paper we consider the problem of finding the efficient frontier associated with the standard mean-variance portfolio optimisation model. We extend the standard model to include cardinality constraints that limit a portfolio to have a specified number of assets, and to impose limits on the proportion of the portfolio held in a given asset (if any of the asset is held). We illustrate the differences that arise in the shape of this efficient frontier when such constraints are present. We present three heuristic algorithms based upon genetic algorithms, tabu search and simulated annealing for finding the cardinality constrained efficient frontier. Computational results are presented for five data sets involving up to 225 assets. Scope and purpose The standard Markowitz mean-variance approach to portfolio selection involves tracing out an efficient frontier, a continuous curve illustrating the tradeoff between return and risk (variance). This frontier can be easily found via quadratic programming. This approach is well-known and widely applied. However, for practical purposes, it may be desirable to limit the number of assets in a portfolio, as well as imposing limits on the proportion of the portfolio devoted to any particular asset. If such constraints exist, the problem of finding the efficient frontier becomes much harder. This paper illustrates how, in the presence of such constraints, the efficient frontier becomes discontinuous. Three heuristic techniques are applied to the problem of finding this efficient frontier and computational results presented for a number of data sets which are made publicly available.

Keywords: Portfolio optimisation, CCMVPOP, Efficient frontier

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Yuning Chen, Jin-Kao Hao, and Fred Glover. A hybrid metaheuristic approach for the capacitated arc routing problem. European Journal of Operational Research, 553(1):25–39, 2016.
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Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.

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Christian Cintrano, Javier Ferrer, Manuel López-Ibáñez, and Enrique Alba. Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs. Evolutionary Computation, 31(1):31–51, 2023.
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In the traffic light scheduling problem the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with evolutionary operators is effective for this task due to the power of evolutionary operators in numerical optimization. In this paper, we further explore the hybridization of evolutionary operators and the elitist iterated racing of IRACE for the simulation-optimization of traffic light programs. We review previous works from the literature to find the evolutionary operators performing the best when facing this problem to propose new hybrid algorithms. We evaluate our approach over a realistic case study derived from the traffic network of Málaga (Spain) with 275 traffic lights that should be scheduled optimally. The experimental analysis reveals that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than the other algorithms when the budget of simulations is low. In contrast, IRACE performs better than the hybrids for high simulations budget, although the optimization time is much longer.

Keywords: irace, Simulation optimization, Uncertainty, Traffic light planning

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Kalyanmoy Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.
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Kalyanmoy Deb and Himanshu Jain. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4):577–601, 2014.
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Kalyanmoy Deb, Manikanth Mohan, and Shikhar Mishra. Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evolutionary Computation, 13(4):501–525, December 2005.
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Kalyanmoy Deb and Santosh Tiwari. Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. European Journal of Operational Research, 185(3):1062–1087, 2008.
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Kalyanmoy Deb, Ling Zhu, and Sandeep Kulkarni. Handling Multiple Scenarios in Evolutionary Multi-Objective Numerical Optimization. IEEE Transactions on Evolutionary Computation, 22(6):920–933, 2018.
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Solutions to most practical numerical optimization problems must be evaluated for their performance over a number of different loading or operating conditions, which we refer here as scenarios. Therefore, a meaningful and resilient optimal solution must be such that it remains feasible under all scenarios and performs close to an individual optimal solution corresponding to each scenario. Despite its practical importance, multi-scenario consideration has received a lukewarm attention, particularly in the context of multi-objective optimization. The usual practice is to optimize for the worst-case scenario. In this paper, we review existing methodologies in this direction and set our goal to suggest a new and potential population-based method for handling multiple scenarios by defining scenario-wise domination principle and scenario-wise diversity-preserving operators. To evaluate, the proposed method is applied to a number of numerical test problems and engineering design problems with a detail explanation of the obtained results and compared with an existing method. This first systematic evolutionary based multi-scenario, multiobjective, optimization study on numerical problems indicates that multiple scenarios can be handled in an integrated manner using an EMO framework to find a well-balanced compromise set of solutions to multiple scenarios and maintain a tradeoff among multiple objectives. In comparison to an existing serial multiple optimization approach, the proposed approach finds a set of compromised trade-off solutions simultaneously. An achievement of multi-objective trade-off and multi-scenario trade-off is algorithmically challenging, but due to its practical appeal, further research and application must be spent.

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Keywords: irace

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Proposed approximating the hypervolume using scalarizations

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Marcelo De Souza, Marcus Ritt, Manuel López-Ibáñez, and Leslie Pérez Cáceres. ACVIZ: A Tool for the Visual Analysis of the Configuration of Algorithms with irace. Operations Research Perspectives, 8:100186, 2021.
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This paper introduces acviz, a tool that helps to analyze the automatic configuration of algorithms with irace. It provides a visual representation of the configuration process, allowing users to extract useful information, e.g. how the configurations evolve over time. When test data is available, acviz also shows the performance of each configuration on the test instances. Using this visualization, users can analyze and compare the quality of the resulting configurations and observe the performance differences on training and test instances.

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Juan Esteban Diaz, Julia Handl, and Dong-Ling Xu. Evolutionary robust optimization in production planning: interactions between number of objectives, sample size and choice of robustness measure. Computers & Operations Research, 79:266–278, 2017.
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Keywords: Evolutionary multi-objective optimization, Production planning, Robust optimization, Simulation-based optimization, Uncertainty modelling

[349]

Juan Esteban Diaz, Julia Handl, and Dong-Ling Xu. Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system. European Journal of Operational Research, 266(3):976–989, 2018.
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Keywords: Genetic algorithms, Combinatorial optimization, Production planning, Simulation-based optimization, Uncertainty modelling

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Automatic configuration (AC) methods are increasingly used to tune and design optimisation algorithms for problems with multiple objectives. Most AC methods use unary quality indicators, which assign a single scalar value to an approximation to the Pareto front, to compare the performance of different optimisers. These quality indicators, however, imply preferences beyond Pareto-optimality that may differ from those of the decision maker (DM). Although it is possible to incorporate DM's preferences into quality indicators, e.g., by means of the weighted hypervolume indicator (HV^w), expressing preferences in terms of weight function is not always intuitive nor an easy task for a DM, in particular, when comparing the stochastic outcomes of several algorithm configurations. A more visual approach to compare such outcomes is the visualisation of their empirical attainment functions (EAFs) differences. This paper proposes using such visualisations as a way of eliciting information about regions of the objective space that are preferred by the DM. We present a method to convert the information about EAF differences into a HV^w that will assign higher quality values to approximation fronts that result in EAF differences preferred by the DM. We show that the resulting HV^w may be used by an AC method to guide the configuration of multi-objective optimisers according to the preferences of the DM. We evaluate the proposed approach on a well-known benchmark problem. Finally, we apply our approach to re-configuring, according to different DM's preferences, a multi-objective optimiser tackling a real-world production planning problem arising in the manufacturing industry.

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Several probabilistic models for subset choice have been proposed in the literature, for example, to explain approval voting data. We show that Marley et al.'s latent scale model is subsumed by Falmagne and Regenwetter's size-independent model, in the sense that every choice probability distribution generated by the former can also be explained by the latter. Our proof relies on the construction of a probabilistic ranking model which we label the “repeated insertion model”. This model is a special case of Marden's orthogonal contrast model class and, in turn, includes the classical Mallows φ-model as a special case. We explore its basic properties as well as its relationship to Fligner and Verducci's multistage ranking model.

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20th IFAC World Congress

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High sensitivity to initial conditions is generally viewed as a drawback of tree search methods because it leads to erratic behavior to be mitigated somehow. In this paper we investigate the opposite viewpoint and consider this behavior as an opportunity to exploit. Our working hypothesis is that erraticism is in fact just a consequence of the exponential nature of tree search that acts as a chaotic amplifier, so it is largely unavoidable. We propose a bet-and-run approach to actually turn erraticism to one's advantage. The idea is to make a number of short sample runs with randomized initial conditions, to bet on the "most promising" run selected according to certain simple criteria, and to bring it to completion. Computational results on a large testbed of mixed integer linear programs from the literature are presented, showing the potential of this approach even when embedded in a proof-of-concept implementation.

http://mat.tepper.cmu.edu/blog/?p=1695

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One of the four papers that proposed BFGS.

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John W. Fowler, Esma S. Gel, Murat Köksalan, Pekka Korhonen, Jon L. Marquis, and Jyrki Wallenius. Interactive evolutionary multi-objective optimization for quasi-concave preference functions. European Journal of Operational Research, 206(2):417–425, 2010.
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We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm. We periodically send solutions to the decision maker (DM) for her evaluation and use the resulting preference information to form preference cones consisting of inferior solutions. The cones allow us to implicitly rank solutions that the DM has not considered. This technique avoids assuming an exact form for the preference function, but does assume that the preference function is quasi-concave. This paper describes the genetic algorithm and demonstrates its performance on the multi-objective knapsack problem.

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The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.

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fully dynamic and online algorithm selection technique, with no separate training phase: all candidate algorithms are run in parallel, while a model incrementally learns their runtime distributions.

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Suppose that in a multicriteria linear programming problem among the given objective functions there are some which can be deleted without influencing the set E of all efficient solutions. Such objectives are said to be redundant. Introducing systems of objective functions which realize their individual optimum in a single vertex of the polyhedron generated by the restriction set, the notion of relative or absolute redundant objectives is defined. A theory which describes properties of absolute and relative redundant objectives is developed. A method for determining all the relative and absolute redundant objectives, based on this theory, is given. Illustrative examples demonstrate the procedure.

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After growing up together, and mostly growing apart in the second half of the 20th century, the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging on a shared view of the computational foundations of intelligence that promotes valuable cross-disciplinary exchanges on questions, methods, and results. We chart advances over the past several decades that address challenges of perception and action under uncertainty through the lens of computation. Advances include the development of representations and inferential procedures for large-scale probabilistic inference and machinery for enabling reflection and decisions about tradeoffs in effort, precision, and timeliness of computations. These tools are deployed toward the goal of computational rationality: identifying decisions with highest expected utility, while taking into consideration the costs of computation in complex real-world problems in which most relevant calculations can only be approximated. We highlight key concepts with examples that show the potential for interchange between computer science, cognitive science, and neuroscience.

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Proposed ExtraTrees

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One of the four papers that proposed BFGS.

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Zaiwu Gong, Ning Zhang, and Francisco Chiclana. The optimization ordering model for intuitionistic fuzzy preference relations with utility functions. Knowledge-Based Systems, 162:174–184, 2018.
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Intuitionistic fuzzy sets describe information from the three aspects of membership degree, non-membership degree and hesitation degree, which has more practical significance when uncertainty pervades qualitative decision problems. In this paper, we investigate the problem of ranking intuitionistic fuzzy preference relations (IFPRs) based on various non-linear utility functions. First, we transform IFPRs into their isomorphic interval-value fuzzy preference relations (IVFPRs), and utilise non-linear utility functions, such as parabolic, S-shaped, and hyperbolic absolute risk aversion, to fit the true value of a decision-maker's judgement. Ultimately, the optimization ordering models for the membership and non-membership of IVFPRs based on utility function are constructed, with objective function aiming at minimizing the distance deviation between the multiplicative consistency ideal judgment and the actual judgment, represented by utility function, subject to the decision-maker's utility constraints. The proposed models ensure that more factual and optimal ranking of alternative is acquired, avoiding information distortion caused by the operations of intervals. Second, by introducing a non-Archimedean infinitesimal, we establish the optimization ordering model for IFPRs with the priority of utility or deviation, which realises the goal of prioritising solutions under multi-objective programming. Subsequently, we verify that a close connection exists between the ranking for membership and non-membership degree IVFPRs. Comparison analyses with existing approaches are summarized to demonstrate that the proposed models have advantage in dealing with group decision making problems with IFPRs.

Special Issue on intelligent decision-making and consensus under uncertainty in inconsistent and dynamic environments

Keywords: Intuitionistic fuzzy preference relation, Utility function, Ranking, Multiplicative consistency, Non-archimedean infinitesimal

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Robert B. Gramacy and Herbert K. H. Lee. Bayesian Treed Gaussian Process Models With an Application to Computer Modeling. Journal of the American Statistical Association, 103:1119–1130, 2008.
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Alex Grasas, Angel A. Juan, and Helena Ramalhinho Lourenço. SimILS: A Simulation-based Extension of the Iterated Local Search Metaheuristic for Stochastic Combinatorial Optimization. Journal of Simulation, 10(1):69–77, 2016.
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M. Gravel, W. L. Price, and Caroline Gagné. Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic. European Journal of Operational Research, 143(1):218–229, 2002.
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John J. Grefenstette. Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1):122–128, 1986.
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Keywords: parameter tuning

[545]

Salvatore Greco, Milosz Kadziński, Vincent Mousseau, and Roman Slowiński. ELECTREGKMS: Robust ordinal regression for outranking methods. European Journal of Operational Research, 214(1):118–135, 2011.
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Salvatore Greco, Vincent Mousseau, and Roman Slowiński. Robust ordinal regression for value functions handling interacting criteria. European Journal of Operational Research, 239(3):711–730, 2014.
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David R. Grimes, Chris T. Bauch, and John P. A. Ioannidis. Modelling science trustworthiness under publish or perish pressure. Royal Society Open Science, 5:171511, 2018.
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Andrea Grosso, Federico Della Croce, and R. Tadei. An Enhanced Dynasearch Neighborhood for the Single-Machine Total Weighted Tardiness Scheduling Problem. Operations Research Letters, 32(1):68–72, 2004.
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Peter Groves, Basel Kayyali, David Knott, and Steve Van Kuiken. The "big data" revolution in healthcare. McKinsey Quarterly, 2, 2013.
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Benoît Groz and Silviu Maniu. Hypervolume subset selection with small subsets. Evolutionary Computation, 27(4):611–637, 2019.
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Viviane Grunert da Fonseca and Carlos M. Fonseca. A link between the multivariate cumulative distribution function and the hitting function for random closed sets. Statistics & Probability Letters, 57(2):179–182, 2002.
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Andreia P. Guerreiro, Carlos M. Fonseca, and Luís Paquete. The Hypervolume Indicator: Computational Problems and Algorithms. ACM Computing Surveys, 54(6):1–42, 2021.
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Andreia P. Guerreiro, Vasco Manquinho, and José Rui Figueira. Exact hypervolume subset selection through incremental computations. Computers & Operations Research, 136:105–471, December 2021.
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[555]

Gonzalo Guillén-Gosálbez. A novel MILP-based objective reduction method for multi-objective optimization: Application to environmental problems. Computers & Chemical Engineering, 35(8):1469–1477, 2011.
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Multi-objective optimization has recently emerged as a useful technique in sustainability analysis, as it can assist in the study of optimal trade-off solutions that balance several criteria. The main limitation of multi-objective optimization is that its computational burden grows in size with the number of objectives. This computational barrier is critical in environmental applications in which decision-makers seek to minimize simultaneously several environmental indicators of concern. With the aim to overcome this limitation, this paper introduces a systematic method for reducing the number of objectives in multi-objective optimization with emphasis on environmental problems. The approach presented relies on a novel mixed-integer linear programming formulation that minimizes the error of omitting objectives. We test the capabilities of this technique through two environmental problems of different nature in which we attempt to minimize a set of life cycle assessment impacts. Numerical examples demonstrate that certain environmental metrics tend to behave in a non-conflicting manner, which makes it possible to reduce the dimension of the problem without losing information.

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Odd Erik Gundersen, Yolanda Gil, and David W. Aha. On Reproducible AI: Towards Reproducible Research, Open Science, and Digital Scholarship in AI Publications. AI Magazine, 39(3):56–68, September 2018.
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The reproducibility guidelines can be found here: https://folk.idi.ntnu.no/odderik/reproducibility_guidelines.pdf and a short how-to can be found here: https://folk.idi.ntnu.no/odderik/reproducibility_guidelines_how_to.html

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Aldy Gunawan, Kien Ming Ng, and Kim Leng Poh. A Hybridized Lagrangian Relaxation and Simulated Annealing Method for the Course Timetabling Problem. Computers & Operations Research, 39(12):3074–3088, 2012.
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To the best of our knowledge, this paper describes the first ant colony optimization (ACO) approach applied to nurse scheduling, analyzing a dynamic regional problem which is currently under discussion at the Vienna hospital compound. Each day, pool nurses have to be assigned for the following days to public hospitals while taking into account a variety of soft and hard constraints regarding working date and time, working patterns, nurses qualifications, nurses and hospitals preferences, as well as costs. Extensive computational experiments based on a four week simulation period were used to evaluate three different scenarios varying the number of nurses and hospitals for six different hospitals demand intensities. The results of our simulations and optimizations reveal that the proposed ACO algorithm achieves highly significant improvements compared to a greedy assignment algorithm.

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In Operational Research practice there are almost always alternative paths that can be followed in the modeling and problem solving process. Path dependence refers to the impact of the path on the outcome of the process. The steps of the path include, e.g. forming the problem solving team, the framing and structuring of the problem, the choice of model, the order in which the different parts of the model are specified and solved, and the way in which data or preferences are collected. We identify and discuss seven possibly interacting origins or drivers of path dependence: systemic origins, learning, procedure, behavior, motivation, uncertainty, and external environment. We provide several ideas on how to cope with path dependence.

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We point out the need for Behavioral Operational Research (BOR) in advancing the practice of OR. So far, in OR behavioral phenomena have been acknowledged only in behavioral decision theory but behavioral issues are always present when supporting human problem solving by modeling. Behavioral effects can relate to the group interaction and communication when facilitating with OR models as well as to the possibility of procedural mistakes and cognitive biases. As an illustrative example we use well known system dynamics studies related to the understanding of accumulation. We show that one gets completely opposite results depending on the way the phenomenon is described and how the questions are phrased and graphs used. The results suggest that OR processes are highly sensitive to various behavioral effects. As a result, we need to pay attention to the way we communicate about models as they are being increasingly used in addressing important problems like climate change.

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The customer order scheduling problem (COSP) is defined as to determine the sequence of tasks to satisfy the demand of customers who order several types of products produced on a single machine. A setup is required whenever a product type is launched. The objective of the scheduling problem is to minimize the average customer order flow time. Since the customer order scheduling problem is known to be strongly NP-hard, we solve it using four major metaheuristics and compare the performance of these heuristics, namely, simulated annealing, genetic algorithms, tabu search, and ant colony optimization. These are selected to represent various characteristics of metaheuristics: nature-inspired vs. artificially created, population-based vs. local search, etc. A set of problems is generated to compare the solution quality and computational efforts of these heuristics. Results of the experimentation show that tabu search and ant colony perform better for large problems whereas simulated annealing performs best in small-size problems. Some conclusions are also drawn on the interactions between various problem parameters and the performance of the heuristics.

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Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features. Such models have important applications to algorithm analysis, portfolio-based algorithm selection, and the automatic configuration of parameterized algorithms. Over the past decade, a wide variety of techniques have been studied for building such models. Here, we describe extensions and improvements of existing models, new families of models, and—perhaps most importantly—a much more thorough treatment of algorithm parameters as model inputs. We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate these innovations through the largest empirical analysis of its kind, comparing to a wide range of runtime modelling techniques from the literature. Our experiments consider 11 algorithms and 35 instance distributions; they also span a very wide range of SAT, MIP, and TSP instances, with the least structured having been generated uniformly at random and the most structured having emerged from real industrial applications. Overall, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously.

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Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr, and Thomas Bäck. IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics. Evolutionary Computation, pp.  1–6, 2024.
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In this paper, we discuss various concepts of robustness for uncertain multi-objective optimization problems. We extend the concepts of flimsily, highly, and lightly robust efficiency and we collect different versions of minmax robust efficiency and concepts based on set order relations from the literature. Altogether, we compare and analyze ten different concepts and point out their relations to each other. Furthermore, we present reduction results for the class of objective-wise uncertain multi-objective optimization problems.

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Ekhine Irurozki, Borja Calvo, and José A. Lozano. Sampling and Learning Mallows and Generalized Mallows Models Under the Cayley Distance. Methodology and Computing in Applied Probability, 20(1):1–35, June 2016.
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[664]

Ekhine Irurozki, Borja Calvo, and José A. Lozano. PerMallows: An R Package for Mallows and Generalized Mallows Models. Journal of Statistical Software, 71, 2019.
[ bib | DOI ]

In this paper we present the R package PerMallows, which is a complete toolbox to work with permutations, distances and some of the most popular probability models for permutations: Mallows and the Generalized Mallows models. The Mallows model is an exponential location model, considered as analogous to the Gaussian distribution. It is based on the definition of a distance between permutations. The Generalized Mallows model is its best-known extension. The package includes functions for making inference, sampling and learning such distributions. The distances considered in PerMallows are Kendall's τ, Cayley, Hamming and Ulam.

Keywords: Cayley,Generalized Mallows,Hamming,Kendall's τ,Learning,Mallows,Permutation,R,Ranking,Sampling,Ulam

[665]

Ekhine Irurozki, Jesus Lobo, Aritz Perez, and Javier Del Ser. Rank aggregation for non-stationary data streams. Arxiv preprint arXiv:1910.08795 [stat.ML], 2020.
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Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, and Yusuke Nojima. How to specify a reference point in hypervolume calculation for fair performance comparison. Evolutionary Computation, 26(3):411–440, 2018.
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The authors describe a method for the quantitative study of anchoring effects in estimation tasks. A calibration group provides estimates of a set of uncertain quantities. Subjects in the anchored condition first judge whether a specified number (the anchor) is higher or lower than the true value before estimating each quantity. The anchors are set at predetermined percentiles of the distribution of estimates in the calibration group (15th and 85th percentiles in this study). This procedure permits the transformation of anchored estimates into percentiles in the calibration group, allows pooling of results across problems, and provides a natural measure of the size of the effect. The authors illustrate the method by a demonstration that the initial judgment of the anchor is susceptible to an anchoring-like bias and by an analysis of the relation between anchoring and subjective confidence.

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Shouyong Jiang, Juan Zou, Shengxiang Yang, and Xin Yao. Evolutionary Dynamic Multi-Objective Optimisation: A Survey. ACM Computing Surveys, 55(4), November 2022.
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Keywords: evolutionary algorithm, evolutionary dynamic multi-objective optimisation, dynamic environment, Multi-objective optimisation

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Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.

Keywords: Evolutionary computation,Expensive optimization problems,Machine learning,Meta-models,Model management,Surrogates

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Yaochu Jin, Handing Wang, Tinkle Chugh, Dan Guo, and Kaisa Miettinen. Data-Driven Evolutionary Optimization: An Overview and Case Studies. IEEE Transactions on Evolutionary Computation, 23(3):442–458, June 2019.
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Proposed EGO algorithm

Keywords: EGO

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Jorik Jooken, Pieter Leyman, Tony Wauters, and Patrick De Causmaecker. Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems. Computers & Operations Research, 150:106070, 2023.
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The electricity cost of pumping accounts for a large part of the total operating cost for water-supply networks. This study presents a method based on linear programming for determining an optimal (minimum cost) schedule of pumping on a 24-hr basis. Both unit and maximum demand electricity charges are considered. Account is taken of the relative efficiencies of the available pumps, the structure of the electricity tariff, the consumer-demand profile, and the hydraulic characteristics and operational constraints of the network. The use of extended-period simulation of the network operation in determining the parameters of the linearized network equations and constraints and in studying the optimized network operation is described. An application of the method to an existing network in the United Kingdom is presented, showing that considerable savings are possible. The method was found to be robust and with low computation-time requirements, and is therefore suitable for real-time implementation.

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Angel A. Juan, Javier Faulin, Scott E. Grasman, Markus Rabe, and Gonçalo Figueira. A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2:62–72, 2015.
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Keywords: Metaheuristics; Simulation; Combinatorial optimization; Stochastic problems

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Angel A. Juan, Helena R. Lourenço, Manuel Mateo, Rachel Luo, and Quim Castellà. Using Iterated Local Search for Solving the Flow-shop Problem: Parallelization, Parametrization, and Randomization Issues. International Transactions in Operational Research, 21(1):103–126, 2014.
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Elena A. Kabova, Jason C. Cole, Oliver Korb, Manuel López-Ibáñez, Adrian C. Williams, and Kenneth Shankland. Improved performance of crystal structure solution from powder diffraction data through parameter tuning of a simulated annealing algorithm. Journal of Applied Crystallography, 50(5):1411–1420, October 2017.
[ bib | DOI ]

Significant gains in the performance of the simulated annealing algorithm in the DASH software package have been realized by using the irace automatic configuration tool to optimize the values of three key simulated annealing parameters. Specifically, the success rate in finding the global minimum in intensity χ² space is improved by up to an order of magnitude. The general applicability of these revised simulated annealing parameters is demonstrated using the crystal structure determinations of over 100 powder diffraction datasets.

Keywords: crystal structure determination, powder diffraction, simulated annealing, parameter tuning, irace

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Jakob Kallestad, Ramin Hasibi, Ahmad Hemmati, and Kenneth Sörensen. A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems. European Journal of Operational Research, 309(1):446–468, August 2023.
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Keywords: Deep RL, hyper-heuristic, ALNS

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Qinma Kang, Hong He, and Jun Wei. An Effective Iterated Greedy Algorithm for Reliability-oriented Task Allocation in Distributed Computing Systems. Journal of Parallel and Distributed Computing, 73(8):1106–1115, 2013.
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Giorgos Karafotias, Mark Hoogendoorn, and Agoston E. Eiben. Parameter Control in Evolutionary Algorithms: Trends and Challenges. IEEE Transactions on Evolutionary Computation, 19(2):167–187, 2015.
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İbrahim Karahan and Murat Köksalan. A territory defining multiobjective evolutionary algorithms and preference incorporation. IEEE Transactions on Evolutionary Computation, 14(4):636–664, 2010.
[ bib | DOI ]

Keywords: TDEA

[721]

Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Patrick Meyer, Amir Mohammad Karimi-Mamaghan, and El-Ghazali Talbi. Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art. European Journal of Operational Research, 296(2):393–422, 2022.
[ bib | DOI ]

In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead meta-heuristics toward an efficient, effective, and robust search and improve their performance in terms of solution quality, convergence rate, and robustness. Since various integration methods with different purposes have been developed, there is a need to review the recent advances in using machine learning techniques to improve meta-heuristics. To the best of our knowledge, the literature is deprived of having a comprehensive yet technical review. To fill this gap, this paper provides such a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection, fitness evaluation, initialization, evolution, parameter setting, and cooperation. First, we describe the key concepts and preliminaries of each of these ways of integration. Then, the recent advances in each way of integration are reviewed and classified based on a proposed unified taxonomy. Finally, we provide a technical discussion on the advantages, limitations, requirements, and challenges of implementing each of these integration ways, followed by promising future research directions.

Keywords: Meta-heuristics, Machine learning, Combinatorial optimization problems, State-of-the-art

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Korhan Karabulut and Fatih M. Tasgetiren. A Variable Iterated Greedy Algorithm for the Traveling Salesman Problem with Time Windows. Information Sciences, 279:383–395, 2014.
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Joseph R. Kasprzyk, Shanthi Nataraj, Patrick M. Reed, and Robert J. Lempert. Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42:55–71, 2013.
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Keywords: scenario-based

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Joseph R. Kasprzyk, Patrick M. Reed, Gregory W. Characklis, and Brian R. Kirsch. Many-objective de Novo water supply portfolio planning under deep uncertainty. Environmental Modelling & Software, 34:87–104, 2012.
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Keywords: scenario-based

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Artem Kaznatcheev, David A. Cohen, and Peter Jeavons. Representing Fitness Landscapes by Valued Constraints to Understand the Complexity of Local Search. Journal of Artificial Intelligence Research, 69:1077–1102, 2020.
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Liangjun Ke, Claudia Archetti, and Zuren Feng. Ants can solve the team orienteering problem. Computers and Industrial Engineering, 54(3):648–665, 2008.
[ bib | DOI ]

The team orienteering problem (TOP) involves finding a set of paths from the starting point to the ending point such that the total collected reward received from visiting a subset of locations is maximized and the length of each path is restricted by a pre-specified limit. In this paper, an ant colony optimization (ACO) approach is proposed for the team orienteering problem. Four methods, i.e., the sequential, deterministic-concurrent and random-concurrent and simultaneous methods, are proposed to construct candidate solutions in the framework of ACO. We compare these methods according to the results obtained on well-known problems from the literature. Finally, we compare the algorithm with several existing algorithms. The results show that our algorithm is promising.

Keywords: Ant colony optimization, Ant system, Heuristics, Team orienteering problem

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Graham Kendall, Ruibin Bai, Jacek Blazewicz, Patrick De Causmaecker, Michel Gendreau, Robert John, Jiawei Li, Barry McCollum, Erwin Pesch, Rong Qu, Nasser Sabar, Greet Vanden Berghe, and Angelina Yee. Good Laboratory Practice for Optimization Research. Journal of the Operational Research Society, 67(4):676–689, 2016.
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[730]

Pascal Kerschke, Holger H. Hoos, Frank Neumann, and Heike Trautmann. Automated Algorithm Selection: Survey and Perspectives. Evolutionary Computation, 27(1):3–45, March 2019.
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B. W. Kernighan and S. Lin. An Efficient Heuristic Procedure for Partitioning Graphs. Bell Systems Technology Journal, 49(2):213–219, 1970.
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Pascal Kerschke and Heike Trautmann. Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation, 27(1):99–127, 2019.
[ bib | DOI ]

In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.

[733]

Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz, Heike Trautmann, and Michael T. M. Emmerich. Search Dynamics on Multimodal Multiobjective Problems. Evolutionary Computation, 27(4):577–609, 2019.
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Norbert L. Kerr. HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology Review, 2(3):196–217, August 1998.
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[735]

A. R. KhudaBukhsh, Lin Xu, Holger H. Hoos, and Kevin Leyton-Brown. SATenstein: Automatically Building Local Search SAT Solvers from Components. Artificial Intelligence, 232:20–42, 2016.
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[736]

Philip Kilby and Tommaso Urli. Fleet design optimisation from historical data using constraint programming and large neighbourhood search. Constraints, pp.  1–20, 2015.
[ bib | DOI ]

Keywords: F-race

[737]

Yeong-Dae Kim. Heuristics for Flowshop Scheduling Problems Minimizing Mean Tardiness. Journal of the Operational Research Society, 44(1):19–28, 1993.
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[738]

Youngmin Kim, Richard Allmendinger, and Manuel López-Ibáñez. Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art. Arxiv preprint arXiv:2101.09505 [cs.LG], 2020.
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Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e.g., breakage of a machine or equipment, or life threat). Although a comprehensive survey of safe reinforcement learning algorithms was published in 2015, a number of new algorithms have been proposed thereafter, and related works in active learning and in optimization were not considered. This paper reviews those algorithms from a number of domains including reinforcement learning, Gaussian process regression and classification, evolutionary algorithms, and active learning. We provide the fundamental concepts on which the reviewed algorithms are based and a characterization of the individual algorithms. We conclude by explaining how the algorithms are connected and suggestions for future research.

[739]

Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi. Bayesian Optimization with Approximate Set Kernels. Machine Learning, 2021.
[ bib | DOI ]

We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input. Because set inputs are permutation-invariant, traditional Gaussian process-based Bayesian optimization strategies which assume vector inputs can fall short. To address this, we develop a Bayesian optimization method with set kernel that is used to build surrogate functions. This kernel accumulates similarity over set elements to enforce permutation-invariance, but this comes at a greater computational cost. To reduce this burden, we propose two key components: (i) a more efficient approximate set kernel which is still positive-definite and is an unbiased estimator of the true set kernel with upper-bounded variance in terms of the number of subsamples, (ii) a constrained acquisition function optimization over sets, which uses symmetry of the feasible region that defines a set input. Finally, we present several numerical experiments which demonstrate that our method outperforms other methods.

[740]

J.-S. Kim, J.-H. Park, and D.-H. Lee. Iterated Greedy Algorithms to Minimize the Total Family Flow Time for Job-shop Scheduling with Job Families and Sequence-dependent Set-ups. Engineering Optimization, 49(10):1719–1732, 2017.
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Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. Arxiv preprint arXiv:1412.6980 [cs.LG], 2014.
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Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015 [2131]

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Scott Kirkpatrick and G. Toulouse. Configuration Space Analysis of Travelling Salesman Problems. Journal de Physique, 46(8):1277–1292, 1985.
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[743]

Scott Kirkpatrick. Optimization by Simulated Annealing: Quantitative Studies. Journal of Statistical Physics, 34(5-6):975–986, 1984.
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[744]

Scott Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by Simulated Annealing. Science, 220(4598):671–680, 1983.
[ bib | DOI ]

Proposed Simulated Annealing

[745]

Kathrin Klamroth, Sanaz Mostaghim, Boris Naujoks, Silvia Poles, Robin C. Purshouse, Günther Rudolph, Stefan Ruzika, Serpil Sayın, Margaret M. Wiecek, and Xin Yao. Multiobjective optimization for interwoven systems. Journal of Multi-Criteria Decision Analysis, 24(1-2):71–81, 2017.
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[746]

Anton J. Kleywegt, Alexander Shapiro, and Tito Homem-de-Mello. The Sample Average Approximation Method for Stochastic Discrete Optimization. SIAM Journal on Optimization, 12(2):479–502, 2002.
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[747]

Joshua D. Knowles. ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 10(1):50–66, 2006.
[ bib | DOI ]

Keywords: ParEGO, online, metamodel

[748]

Joshua D. Knowles. Closed-loop evolutionary multiobjective optimization. IEEE Computational Intelligence Magazine, 4:77–91, 2009.
[ bib | DOI ]

Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closed-loop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg's terminology), the “phenotypes” are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Well-known early work on artificial evolution — design engineering problems in fluid dynamics, and chemical plant process optimization — was carried out in this experimental mode. More recently, the closed-loop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closed-loop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work are described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) onchip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral resource constraints; expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, Design of Experiments, machine learning and global optimization are also relevant to aspects of the closed-loop problem, and may inspire futher development of multiobjective EAs.

[749]

Joshua D. Knowles and David Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172, 2000.
[ bib | DOI ]

Proposed PAES

[750]

Joshua D. Knowles and David Corne. Properties of an Adaptive Archiving Algorithm for Storing Nondominated Vectors. IEEE Transactions on Evolutionary Computation, 7(2):100–116, April 2003.
[ bib ]

Proposed to use S-metric (hypervolume metric) for environmental selection

Keywords: S-metric, hypervolume

[751]

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Gary A. Kochenberger, Fred Glover, Bahram Alidaee, and Cesar Rego. A unified modeling and solution framework for combinatorial optimization problems. OR Spektrum, 26(2):237–250, 2004.
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Gary A. Kochenberger, Jin-Kao Hao, Fred Glover, Mark Lewis, Zhipeng Lü, Haibo Wang, and Yang Wang. The unconstrained binary quadratic programming problem: a survey. Journal of Combinatorial Optimization, 28(1):58–81, 2014.
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[754]

Murat Köksalan. Multiobjective Combinatorial Optimization: Some Approaches. Journal of Multi-Criteria Decision Analysis, 15:69–78, 2009.
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[755]

Murat Köksalan and İbrahim Karahan. An Interactive Territory Defining Evolutionary Algorithm: iTDEA. IEEE Transactions on Evolutionary Computation, 14(5):702–722, October 2010.
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[756]

Rainer Kolisch and Sönke Hartmann. Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research, 174(1):23–37, October 2006.
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This paper considers heuristics for the well-known resource-constrained project scheduling problem (RCPSP). It provides an update of our survey which was published in 2000. We summarize and categorize a large number of heuristics that have recently been proposed in the literature. Most of these heuristics are then evaluated in a computational study and compared on the basis of our standardized experimental design. Based on the computational results we discuss features of good heuristics. The paper closes with some remarks on our test design and a summary of the recent developments in research on heuristics for the RCPSP.

Keywords: Computational evaluation, Heuristics, Project scheduling, Resource constraints

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Introduced the Quadratic Assignment Problem (QAP)

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In this paper we consider a simple sequential multicriterion decision making problem in which a decision maker has to accept or reject a series of multi-attributed outcomes. We show that using very simple programming techniques, a great deal of the decision making can be automated. The method might be applicable to situations in which a dealer is having to consider sequential offers in a trading market.

Keywords: machine decision making

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Choice behavior in an interactive multiple-criteria decision making environment is examined experimentally. A “free search” discrete visual interactive reference direction approach was used on a microcomputer by management students to solve two realistic and relevant multiple-criteria decision problems. The results revealed persistent patterns of intransitive choice behavior, and an unexpectedly rapid degree of convergence of the reference direction approach on a preferred solution. The results can be explained using Tversky' additive utility difference model and Kahneman-Tversky's prospect theory. The implications of the results for the design of interactive multiple-criteria decision procedures are discussed.

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Flip Korn, B.-U. Pagel, and Christos Faloutsos. On the “dimensionality curse” and the “self-similarity blessing”. IEEE Transactions on Knowledge and Data Engineering, 13(1):96–111, 2001.
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Spatial queries in high-dimensional spaces have been studied extensively. Among them, nearest neighbor queries are important in many settings, including spatial databases (Find the k closest cities) and multimedia databases (Find the k most similar images). Previous analyses have concluded that nearest-neighbor search is hopeless in high dimensions due to the notorious "curse of dimensionality". We show that this may be overpessimistic. We show that what determines the search performance (at least for R-tree-like structures) is the intrinsic dimensionality of the data set and not the dimensionality of the address space (referred to as the embedding dimensionality). The typical (and often implicit) assumption in many previous studies is that the data is uniformly distributed, with independence between attributes. However, real data sets overwhelmingly disobey these assumptions; rather, they typically are skewed and exhibit intrinsic ("fractal") dimensionalities that are much lower than their embedding dimension, e.g. due to subtle dependencies between attributes. We show how the Hausdorff and Correlation fractal dimensions of a data set can yield extremely accurate formulas that can predict the I/O performance to within one standard deviation on multiple real and synthetic data sets.

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P. Korošec, Jurij Šilc, and B. Robič. Solving the mesh-partitioning problem with an ant-colony algorithm. Parallel Computing, 30:785–801, 2004.
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Pekka Korhonen, Kari Silvennoinen, Jyrki Wallenius, and Anssi Öörni. Can a linear value function explain choices? An experimental study. European Journal of Operational Research, 219(2):360–367, June 2012.
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We investigate in a simple bi-criteria experimental study, whether subjects are consistent with a linear value function while making binary choices. Many inconsistencies appeared in our experiment. However, the impact of inconsistencies on the linearity vs. non-linearity of the value function was minor. Moreover, a linear value function seems to predict choices for bi-criteria problems quite well. This ability to predict is independent of whether the value function is diagnosed linear or not. Inconsistencies in responses did not necessarily change the original diagnosis of the form of the value function. Our findings have implications for the design and development of decision support tools for Multiple Criteria Decision Making problems.

Keywords: Binary choices, Inconsistency, Linear value function, Multiple criteria, Weights

[766]

Oliver Korb, Thomas Stützle, and Thomas E. Exner. An Ant Colony Optimization Approach to Flexible Protein–Ligand Docking. Swarm Intelligence, 1(2):115–134, 2007.
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Oliver Korb, Thomas Stützle, and Thomas E. Exner. Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS. Journal of Chemical Information and Modeling, 49(2):84–96, 2009.
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Oliver Korb, Peter Monecke, Gerhard Hessler, Thomas Stützle, and Thomas E. Exner. pharmACOphore: Multiple Flexible Ligand Alignment Based on Ant Colony Optimization. Journal of Chemical Information and Modeling, 50(9):1669–1681, 2010.
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A dynamic and visual “free-search” type of interactive procedure for multiple-objective linear programming is presented. The method enables a decision maker to freely search any part of the efficient frontier by controlling the speed and direction of motion. The objective function values are represented in numeric form and as bar graphs on a display. The method is implemented on an IBM PC/1 microcomputer and is illustrated using a multiple-objective linear-programming model for managing disposal of sewage sludge in the New York Bight. Some other applications are also briefly discussed.

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Lars Kotthoff. Algorithm Selection for Combinatorial Search Problems: A Survey. AI Magazine, 35(3):48–60, 2014.
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Timo Kötzing, Frank Neumann, Heiko Röglin, and Carsten Witt. Theoretical Analysis of Two ACO Approaches for the Traveling Salesman Problem. Swarm Intelligence, 6(1):1–21, 2012.
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Bioinspired algorithms, such as evolutionary algorithms and ant colony optimization, are widely used for different combinatorial optimization problems. These algorithms rely heavily on the use of randomness and are hard to understand from a theoretical point of view. This paper contributes to the theoretical analysis of ant colony optimization and studies this type of algorithm on one of the most prominent combinatorial optimization problems, namely the traveling salesperson problem (TSP). We present a new construction graph and show that it has a stronger local property than one commonly used for constructing solutions of the TSP. The rigorous runtime analysis for two ant colony optimization algorithms, based on these two construction procedures, shows that they lead to good approximation in expected polynomial time on random instances. Furthermore, we point out in which situations our algorithms get trapped in local optima and show where the use of the right amount of heuristic information is provably beneficial.

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Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown. Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA. Journal of Machine Learning Research, 17:1–5, 2016.
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Katharina Kowalski, Sigrid Stagl, Reinhard Madlener, and Ines Omann. Sustainable energy futures: Methodological challenges in combining scenarios and participatory multi-criteria analysis. European Journal of Operational Research, 197(3):1063–1074, 2009.
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Application of heuristic solution procedures to the practical problem of project scheduling has previously been studied by numerous researchers. However, there is little consensus about their findings, and the practicing manager is currently at a loss as to which scheduling rule to use. Furthermore, since no categorization process was developed, it is assumed that once a rule is selected it must be used throughout the whole project. This research breaks away from this tradition by providing a categorization process based on two powerful project summary measures. The first measure identifies the location of the peak of total resource requirements and the second measure identifies the rate of utilization of each resource type. The performance of the rules are classified according to values of these two measures, and it is shown that a rule introduced by this research performs significantly better on most categories of projects.

Keywords: project management, research and development

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A versatile and practical method of searching a parameter space is presented. Theoretical and experimental results illustrate the usefulness of the method for such problems as the experimental optimization of the performance of a system with a very general multipeak performance function when the only available information is noise-distributed samples of the function. At present, its usefulness is restricted to optimization with respect to one system parameter. The observations are taken sequentially; but, as opposed to the gradient method, the observation may be located anywhere on the parameter interval. A sequence of estimates of the location of the curve maximum is generated. The location of the next observation may be interpreted as the location of the most likely competitor (with the current best estimate) for the location of the curve maximum. A Brownian motion stochastic process is selected as a model for the unknown function, and the observations are interpreted with respect to the model. The model gives the results a simple intuitive interpretation and allows the use of simple but efficient sampling procedures. The resulting process possesses some powerful convergence properties in the presence of noise; it is nonparametric and, despite its generality, is efficient in the use of observations. The approach seems quite promising as a solution to many of the problems of experimental system optimization.

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Proposed ε-approx and ε-Pareto archivers

Keywords: archiving, ε-dominance, ε-approximation, ε-Pareto

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We extend and improve two existing methods of generating random correlation matrices, the onion method of Ghosh and Henderson [S. Ghosh, S.G. Henderson, Behavior of the norta method for correlated random vector generation as the dimension increases, ACM Transactions on Modeling and Computer Simulation (TOMACS) 13 (3) (2003) 276-294] and the recently proposed method of Joe [H. Joe, Generating random correlation matrices based on partial correlations, Journal of Multivariate Analysis 97 (2006) 2177-2189] based on partial correlations. The latter is based on the so-called D-vine. We extend the methodology to any regular vine and study the relationship between the multiple correlation and partial correlations on a regular vine. We explain the onion method in terms of elliptical distributions and extend it to allow generating random correlation matrices from the same joint distribution as the vine method. The methods are compared in terms of time necessary to generate 5000 random correlation matrices of given dimensions.

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Miqing Li. Is Our Archiving Reliable? Multiobjective Archiving Methods on “Simple” Artificial Input Sequences. ACM Transactions on Evolutionary Learning and Optimization, 1(3):1–19, 2021.
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Ke Li, Renzhi Chen, Guangtao Fu, and Xin Yao. Two-archive evolutionary algorithm for constrained multiobjective optimization. IEEE Transactions on Evolutionary Computation, 23(2):303–315, 2018.
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Miqing Li, Tao Chen, and Xin Yao. How to evaluate solutions in Pareto-based search-based software engineering? A critical review and methodological guidance. IEEE Transactions on Software Engineering, 48(5):1771–1799, 2020.
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Miqing Li, Crina Grosan, Shengxiang Yang, Xiaohui Liu, and Xin Yao. Multi-line distance minimization: A visualized many-objective test problem suite. IEEE Transactions on Evolutionary Computation, 22(1):61–78, 2018.
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Miqing Li, Manuel López-Ibáñez, and Xin Yao. Multi-Objective Archiving. IEEE Transactions on Evolutionary Computation, 28(3):696–717, 2023.
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Most multi-objective optimisation algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e.g., as the population in evolutionary computation). Over the last two decades, archiving, the process of comparing new solutions with previous ones and deciding how to update the archive/population, stands as an important issue in evolutionary multi-objective optimisation (EMO). This is evidenced by constant efforts from the community on developing various effective archiving methods, ranging from conventional Pareto-based methods to more recent indicator-based and decomposition-based ones. However, the focus of these efforts is on empirical performance comparison in terms of specific quality indicators; there is lack of systematic study of archiving methods from a general theoretical perspective. In this paper, we attempt to conduct a systematic overview of multi-objective archiving, in the hope of paving the way to understand archiving algorithms from a holistic perspective of theory and practice, and more importantly providing a guidance on how to design theoretically desirable and practically useful archiving algorithms. In doing so, we also present that archiving algorithms based on weakly Pareto compliant indicators (e.g., ε-indicator), as long as designed properly, can achieve the same theoretical desirables as archivers based on Pareto compliant indicators (e.g., hypervolume indicator). Such desirables include the property limit-optimal, the limit form of the possible optimal property that a bounded archiving algorithm can have with respect to the most general form of superiority between solution sets.

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Zhiyi Li, Mohammad Shahidehpour, Shay Bahramirad, and Amin Khodaei. Optimizing Traffic Signal Settings in Smart Cities. IEEE Transactions on Smart Grid, 3053(4):1–1, 2016.
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Traffic signals play a critical role in smart cities for mitigating traffic congestions and reducing the emission in metropolitan areas. This paper proposes a bi-level optimization framework to settle the optimal traffic signal setting problem. The upper-level problem determines the traffic signal settings to minimize the drivers' average travel time, while the lower-level problem aims for achieving the network equilibrium using the settings calculated at the upper level. Genetic algorithm is employed with the integration of microscopic-traffic-simulation based dynamic traffic assignment (DTA) to decouple the complex bi-level problem into tractable single-level problems which are solved sequentially. Case studies on a synthetic traffic network and a real-world traffic subnetwork are conducted to examine the effectiveness of the proposed model and relevant solution methods. Additional strategies are provided for the extension of the proposed model and the acceleration solution process in large-area traffic network applications.

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Xiaoping Li, Long Chen, Haiyan Xu, and Jatinder N. D. Gupta. Trajectory Scheduling Methods for Minimizing Total Tardiness in a Flowshop. Operations Research Perspectives, 2:13–23, 2015.
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Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. Journal of Machine Learning Research, 18(185):1–52, 2018.
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Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration non-stochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, our algorithm, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare our algorithm with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that our algorithm can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.

Keywords: racing

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Y. Li and W. Li. Adaptive Ant Colony Optimization Algorithm Based on Information Entropy: Foundation and Application. Fundamenta Informaticae, 77(3):229–242, 2007.
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Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao. Many-Objective Evolutionary Algorithms: A Survey. ACM Computing Surveys, 48(1):1–35, 2015.
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Bingdong Li, Ke Tang, Jinlong Li, and Xin Yao. Stochastic ranking algorithm for many-objective optimization based on multiple indicators. IEEE Transactions on Evolutionary Computation, 20(6):924–938, 2016.
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Miqing Li, Shengxiang Yang, and Xiaohui Liu. Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Transactions on Evolutionary Computation, 18(3):348–365, 2014.
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Proposed SDE indicator algorithm

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Miqing Li, Shengxiang Yang, and Xiaohui Liu. Pareto or non-Pareto: Bi-criterion evolution in multiobjective optimization. IEEE Transactions on Evolutionary Computation, 20(5):645–665, 2016.
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Miqing Li and Xin Yao. Quality Evaluation of Solution Sets in Multiobjective Optimisation: A Survey. ACM Computing Surveys, 52(2):1–38, 2019.
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[834]

Miqing Li and Xin Yao. Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization. arXiv preprint arXiv:1702.00477, 2017.
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Miqing Li and Xin Yao. What weights work for you? Adapting weights for any Pareto front shape in decomposition-based evolutionary multiobjective optimisation. Evolutionary Computation, 28(2):227–253, 2020.
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Hui Li and Qingfu Zhang. Multiobjective Optimization Problems with Complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2):284–302, 2009.
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Zhipan Li, Juan Zou, Shengxiang Yang, and Jinhua Zheng. A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization. Information Sciences, 574:413–430, 2021.
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Tianjun Liao, Doǧan Aydın, and Thomas Stützle. Artificial Bee Colonies for Continuous Optimization: Experimental Analysis and Improvements. Swarm Intelligence, 7(4):327–356, 2013.
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Tianjun Liao, Daniel Molina, Marco A. Montes de Oca, and Thomas Stützle. A Note on the Effects of Enforcing Bound Constraints on Algorithm Comparisons using the IEEE CEC'05 Benchmark Function Suite. Evolutionary Computation, 22(2):351–359, 2014.
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[840]

Tianjun Liao, Daniel Molina, and Thomas Stützle. Performance Evaluation of Automatically Tuned Continuous Optimizers on Different Benchmark Sets. Applied Soft Computing, 27:490–503, 2015.
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[841]

Tianjun Liao, Marco A. Montes de Oca, and Thomas Stützle. Computational results for an automatically tuned CMA-ES with increasing population size on the CEC'05 benchmark set. Soft Computing, 17(6):1031–1046, 2013.
[ bib | DOI ]

[842]

Tianjun Liao, Krzysztof Socha, Marco A. Montes de Oca, Thomas Stützle, and Marco Dorigo. Ant Colony Optimization for Mixed-Variable Optimization Problems. IEEE Transactions on Evolutionary Computation, 18(4):503–518, 2014.
[ bib ]

Keywords: ACOR

[843]

Tianjun Liao, Thomas Stützle, Marco A. Montes de Oca, and Marco Dorigo. A Unified Ant Colony Optimization Algorithm for Continuous Optimization. European Journal of Operational Research, 234(3):597–609, 2014.
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C.-J. Liao, C.-T. Tseng, and P. Luarn. A Discrete Version of Particle Swarm Optimization for Flowshop Scheduling Problems. Computers & Operations Research, 34(10):3099–3111, 2007.
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[845]

Arnaud Liefooghe, Fabio Daolio, Bilel Derbel, Sébastien Verel, Hernán E. Aguirre, and Kiyoshi Tanaka. Landscape-Aware Performance Prediction for Evolutionary Multi-objective Optimization. IEEE Transactions on Evolutionary Computation, 24(6):1063–1077, 2020.
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[846]

Arnaud Liefooghe, Jérémie Humeau, Salma Mesmoudi, Laetitia Jourdan, and El-Ghazali Talbi. On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems. Journal of Heuristics, 18(2):317–352, 2012.
[ bib | DOI ]

This paper discusses simple local search approaches for approximating the efficient set of multiobjective combinatorial optimization problems. We focus on algorithms defined by a neighborhood structure and a dominance relation that iteratively improve an archive of nondominated solutions. Such methods are referred to as dominance-based multiobjective local search. We first provide a concise overview of existing algorithms, and we propose a model trying to unify them through a fine-grained decomposition. The main problem-independent search components of dominance relation, solution selection, neighborhood exploration and archiving are largely discussed. Then, a number of state-of-the-art and original strategies are experimented on solving a permutation flowshop scheduling problem and a traveling salesman problem, both on a two- and a three-objective formulation. Experimental results and a statistical comparison are reported in the paper, and some directions for future research are highlighted.

[847]

Arnaud Liefooghe, Laetitia Jourdan, and El-Ghazali Talbi. A Software Framework Based on a Conceptual Unified Model for Evolutionary Multiobjective Optimization: ParadisEO-MOEO. European Journal of Operational Research, 209(2):104–112, 2011.
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Arnaud Liefooghe, Sébastien Verel, and Jin-Kao Hao. A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming. Applied Soft Computing, 16:10–19, 2014.
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[849]

Bojan Likar and Juš Kocijan. Predictive control of a gas–liquid separation plant based on a Gaussian process model. Computers & Chemical Engineering, 31(3):142–152, 2007.
[ bib | DOI ]

[850]

Marius Thomas Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, and Frank Hutter. SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research, 23:1–9, 2022.
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Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, and Torsten Schaub. AutoFolio: An Automatically Configured Algorithm Selector. Journal of Artificial Intelligence Research, 53:745–778, 2015.
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Marius Thomas Lindauer, Jan N. van Rijn, and Lars Kotthoff. The algorithm selection competitions 2015 and 2017. Artificial Intelligence, 272:86–100, 2019.
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Andrei Lissovoi and Carsten Witt. Runtime Analysis of Ant Colony Optimization on Dynamic Shortest Path Problems. Theoretical Computer Science, 561(Part A):73–85, 2015.
[ bib | DOI ]

A simple ACO algorithm called λ-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using λ ants per vertex helps in tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of dynamic shortest-path problems which make the optimum difficult to track are discussed. Experiments illustrate theoretical findings and conjectures.

[855]

J. D. C. Little, K. G. Murty, D. W. Sweeney, and C. Karel. An Algorithm for the Traveling Salesman Problem. Operations Research, 11:972–989, 1963.
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Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer, and Valerio Pascucci. Visualizing High-Dimensional Data: Advances in the Past Decade. IEEE Transactions on Visualization and Computer Graphics, 23(3), 2017.
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[857]

Jiyin Liu and Colin R. Reeves. Constructive and Composite Heuristic Solutions to the P//ΣCi Scheduling Problem. European Journal of Operational Research, 132(2):439–452, 2001.
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[858]

Yiping Liu, Gary G. Yen, and Dunwei Gong. A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. IEEE Transactions on Evolutionary Computation, 23(4):660–674, 2018.
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[859]

Marco Locatelli and Fabio Schoen. Random Linkage: a family of acceptance/rejection algorithms for global optimisation. Mathematical Programming, 85(2), 1999.
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Keywords: Multi-Level Single-Linkage (MLSL)

[860]

Andrea Lodi, Silvano Martello, and Michele Monaci. Two-dimensional packing problems: A survey. European Journal of Operational Research, 141(2):241–252, 2002.
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[861]

Andrea Lodi, Silvano Martello, and Daniele Vigo. Heuristic and metaheuristic approaches for a class of two-dimensional bin packing problems. INFORMS Journal on Computing, 11(4):345–357, 1999.
[ bib | DOI ]

[862]

Andrea Lodi, Silvano Martello, and Daniele Vigo. TSpack: a unified tabu search code for multi-dimensional bin packing problems. Annals of Operations Research, 131(1-4):203–213, 2004.
[ bib | DOI ]

[863]

Andrea Lodi and Giulia Zarpellon. On Learning and Branching: A Survey. TOP, 25:207–236, 2017.
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Jason D. Lohn, Gregory S. Hornby, and Derek S. Linden. Human-competitive Evolved Antennas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 22(3):235–247, 2008.
[ bib | DOI ]

Evolutionary optimization of antennas for NASA

[865]

Manuel López-Ibáñez and Christian Blum. Beam-ACO for the travelling salesman problem with time windows. Computers & Operations Research, 37(9):1570–1583, 2010.
[ bib | DOI ]

The travelling salesman problem with time windows is a difficult optimization problem that arises, for example, in logistics. This paper deals with the minimization of the travel-cost. For solving this problem, this paper proposes a Beam-ACO algorithm, which is a hybrid method combining ant colony optimization with beam search. In general, Beam-ACO algorithms heavily rely on accurate and computationally inexpensive bounding information for differentiating between partial solutions. This work uses stochastic sampling as a useful alternative. An extensive experimental evaluation on seven benchmark sets from the literature shows that the proposed Beam-ACO algorithm is currently a state-of-the-art technique for the travelling salesman problem with time windows when travel-cost optimization is concerned.

Keywords: Ant colony optimization, Travelling salesman problem with time windows, Hybridization

[866]

Manuel López-Ibáñez, Christian Blum, Jeffrey W. Ohlmann, and Barrett W. Thomas. The Travelling Salesman Problem with Time Windows: Adapting Algorithms from Travel-time to Makespan Optimization. Applied Soft Computing, 13(9):3806–3815, 2013.
[ bib | DOI | epub ]

[867]

Manuel López-Ibáñez, Jürgen Branke, and Luís Paquete. Reproducibility in Evolutionary Computation. Arxiv preprint arXiv:20102.03380 [cs.AI], 2021.
[ bib | http ]

Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we suggest a classification of different types of reproducibility that refines the badge system of the Association of Computing Machinery (ACM) adopted by TELO. We discuss, within the context of EC, the different types of reproducibility as well as the concepts of artifact and measurement, which are crucial for claiming reproducibility. We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.

Keywords: Evolutionary Computation, Reproducibility, Empirical study, Benchmarking

[868]

Manuel López-Ibáñez, Jürgen Branke, and Luís Paquete. Reproducibility in Evolutionary Computation. ACM Transactions on Evolutionary Learning and Optimization, 1(4):1–21, 2021.
[ bib | DOI | epub ]

Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we suggest a classification of different types of reproducibility that refines the badge system of the Association of Computing Machinery (ACM) adopted by TELO. We discuss, within the context of EC, the different types of reproducibility as well as the concepts of artifact and measurement, which are crucial for claiming reproducibility. We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.

Keywords: Evolutionary Computation, Reproducibility, Empirical study, Benchmarking

[869]

Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Leslie Pérez Cáceres, Thomas Stützle, and Mauro Birattari. The irace Package: Iterated Racing for Automatic Algorithm Configuration. Operations Research Perspectives, 3:43–58, 2016.
[ bib | DOI | supplementary material ]

[870]

Manuel López-Ibáñez, Marie-Eléonore Kessaci, and Thomas Stützle. Automatic Design of Hybrid Metaheuristics from Algorithmic Components. Submitted, 2017.
[ bib ]

[871]

Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. Hybrid Population-based Algorithms for the Bi-objective Quadratic Assignment Problem. Journal of Mathematical Modelling and Algorithms, 5(1):111–137, 2006.
[ bib | DOI ]

We present variants of an ant colony optimization (MO-ACO) algorithm and of an evolutionary algorithm (SPEA2) for tackling multi-objective combinatorial optimization problems, hybridized with an iterative improvement algorithm and the robust tabu search algorithm. The performance of the resulting hybrid stochastic local search (SLS) algorithms is experimentally investigated for the bi-objective quadratic assignment problem (bQAP) and compared against repeated applications of the underlying local search algorithms for several scalarizations. The experiments consider structured and unstructured bQAP instances with various degrees of correlation between the flow matrices. We do a systematic experimental analysis of the algorithms using outperformance relations and the attainment functions methodology to asses differences in the performance of the algorithms. The experimental results show the usefulness of the hybrid algorithms if the available computation time is not too limited and identify SPEA2 hybridized with very short tabu search runs as the most promising variant.

[872]

Manuel López-Ibáñez, Leslie Pérez Cáceres, and Thomas Stützle. irace: A Tool for the Automatic Configuration of Algorithms. International Federation of Operational Research Societies (IFORS) News, 14(2):30–32, June 2020.
[ bib | http ]

[873]

Manuel López-Ibáñez, T. Devi Prasad, and Ben Paechter. Ant Colony Optimisation for the Optimal Control of Pumps in Water Distribution Networks. Journal of Water Resources Planning and Management, ASCE, 134(4):337–346, 2008.
[ bib | DOI | epub ]

Reducing energy consumption of water distribution networks has never had more significance than today. The greatest energy savings can be obtained by careful scheduling of operation of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper a new explicit representation is presented. It is based on time controlled triggers, where the maximum number of pump switches is specified beforehand. In this representation a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules (search space) compared to the binary representation. Ant colony optimization (ACO) is a stochastic meta-heuristic for combinatorial optimization problems that is inspired by the foraging behavior of some species of ants. In this paper, an application of the ACO framework was developed for the optimal scheduling of pumps. The proposed representation was adapted to an ant colony Optimization framework and solved for the optimal pump schedules. Minimization of electrical cost was considered as the objective, while satisfying system constraints. Instead of using a penalty function approach for constraint violations, constraint violations were ordered according to their importance and solutions were ranked based on this order. The proposed approach was tested on a small test network and on a large real-world network. Results are compared with those obtained using a simple genetic algorithm based on binary representation and a hybrid genetic algorithm that uses level-based triggers.

[874]

Manuel López-Ibáñez, T. Devi Prasad, and Ben Paechter. Representations and Evolutionary Operators for the Scheduling of Pump Operations in Water Distribution Networks. Evolutionary Computation, 19(3):429–467, 2011.
[ bib | DOI ]

Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain less switches than the maximum. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations improves over the results obtained by a recent state-of-the-art Hybrid Genetic Algorithm for pump scheduling using level-controlled triggers.

[875]

Manuel López-Ibáñez and Thomas Stützle. An experimental analysis of design choices of multi-objective ant colony optimization algorithms. Swarm Intelligence, 6(3):207–232, 2012.
[ bib | DOI | supplementary material ]

[876]

Manuel López-Ibáñez and Thomas Stützle. The Automatic Design of Multi-Objective Ant Colony Optimization Algorithms. IEEE Transactions on Evolutionary Computation, 16(6):861–875, 2012.
[ bib | DOI ]

Multi-objective optimization problems are problems with several, typically conflicting criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondominated solutions rather than a single one. Various ant colony optimization (ACO) algorithms have been proposed in recent years for solving such problems. These multi-objective ACO (MOACO) algorithms exhibit different design choices for dealing with the particularities of the multi-objective context. This paper proposes a formulation of algorithmic components that suffices to describe most MOACO algorithms proposed so far. This formulation also shows that existing MOACO algorithms often share equivalent design choices but they are described in different terms. Moreover, this formulation is synthesized into a flexible algorithmic framework, from which not only existing MOACO algorithms may be instantiated, but also combinations of components that were never studied in the literature. In this sense, this paper goes beyond proposing a new MOACO algorithm, but it rather introduces a family of MOACO algorithms. The flexibility of the proposed MOACO framework facilitates the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that the automatically configured MOACO framework outperforms the MOACO algorithms that inspired the framework itself. This paper is also among the first to apply automatic algorithm configuration techniques to multi-objective algorithms.

[877]

Manuel López-Ibáñez and Thomas Stützle. Automatically Improving the Anytime Behaviour of Optimisation Algorithms. European Journal of Operational Research, 235(3):569–582, 2014.
[ bib | DOI | supplementary material ]

Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled also as a set of mutually nondominated, bi-objective points. Using this model, we propose to combine an automatic configuration tool and the hypervolume measure, which assigns a single quality measure to a nondominated set. This allows us to improve the anytime behaviour of optimisation algorithms by means of automatically finding algorithmic configurations that produce the best nondominated sets. Moreover, the recently proposed weighted hypervolume measure is used here to incorporate the decision-maker's preferences into the automatic tuning procedure. We report on the improvements reached when applying the proposed method to two relevant scenarios: (i) the design of parameter variation strategies for MAX-MIN Ant System and (ii) the tuning of the anytime behaviour of SCIP, an open-source mixed integer programming solver with more than 200 parameters.

[878]

Eunice López-Camacho, Hugo Terashima-Marin, Peter Ross, and Gabriela Ochoa. A unified hyper-heuristic framework for solving bin packing problems. Expert Systems with Applications, 41(15):6876–6889, 2014.
[ bib | DOI ]

[879]

Manuel López-Ibáñez, Diederick Vermetten, Johann Dreo, and Carola Doerr. Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms. IEEE Transactions on Evolutionary Computation, 2025.
[ bib | DOI ]

A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime. In this work, we consider an alternative approach, based on the empirical attainment function (EAF) and we show that the target-based ECDF is an approximation of the EAF. We argue that the EAF has several advantages over the target-based ECDF. In particular, it does not require defining a priori quality targets per function, captures performance differences more precisely, and enables the use of additional summary statistics that enrich the analysis. We also show that the average area over the convergence curves is a simpler-to-calculate, but equivalent, measure of anytime performance. To facilitate the accessibility of the EAF, we integrate a module to compute it into the IOHanalyzer platform. Finally, we illustrate the use of the EAF via synthetic examples and via the data available for the BBOB suite.

Pre-print: https://doi.org/10.48550/arXiv.2404.02031

Keywords: EAF-based ECDF

[880]

Samir Loudni and Patrice Boizumault. Combining VNS with constraint programming for solving anytime optimization problems. European Journal of Operational Research, 191:705–735, 2008.
[ bib | DOI ]

[881]

Helena R. Lourenço. Job-Shop Scheduling: Computational Study of Local Search and Large-Step Optimization Methods. European Journal of Operational Research, 83(2):347–364, 1995.
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[882]

Antonio Lova and Pilar Tormos. Analysis of Scheduling Schemes and Heuristic Rules Performance in Resource-Constrained Multiproject Scheduling. Annals of Operations Research, 102(1-4):263–286, February 2001.
[ bib | DOI ]

Frequently, the availability of resources assigned to a project is limited and not sufficient to execute all the concurrent activities. In this situation, decision making about their schedule is necessary. Many times this schedule supposes an increase in the project completion time. Additionally, companies commonly manage various projects simultaneously, sharing a pool of renewable resources. Given these resource constraints, we often can only apply heuristic methods to solve the scheduling problem. In this work the effect of the schedule generation schemes - serial or parallel - and priority rules - MINLFT, MINSLK, MAXTWK, SASP or FCFS - with two approaches - multi-project and single-project - are analysed. The time criteria considered are the mean project delay and the multiproject duration increase. Through an extensive computational study, results show that with the parallel scheduling generation scheme and the multi-project approach the project manager can obtain a good multiproject schedule with the time criterion selected: minimising mean project delay or minimising multiproject duration increase. New heuristics - based on priority rules with a two-phase approach - that outperform classical ones are proposed to minimise mean project delay with a multi-project approach. Finally, the best heuristics analysed are evaluated together with a representative sample of commercial project management software.

Keywords: Combinatorics, heuristic based on priority rules, Multiproject scheduling, Operation Research/Decision Theory, Project management, project management software, Resource allocation, Theory of Computation

[883]

Antonio Lova, Pilar Tormos, Mariamar Cervantes, and Federico Barber. An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes. International Journal of Production Economics, 117(2):302–316, 2009.
[ bib | DOI ]

Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) aims at finding the start times and execution modes for the activities of a project that optimize a given objective function while verifying a set of precedence and resource constraints. In this paper, we focus on this problem and develop a hybrid Genetic Algorithm (MM-HGA) to solve it. Its main contributions are the mode assignment procedure, the fitness function and the use of a very efficient improving method. Its performance is demonstrated by extensive computational results obtained on a set of standard instances and against the best currently available algorithms.

Keywords: genetic algorithm, multi-mode resource-constrained project scheduling

[884]

Manuel Lozano, Fred Glover, Carlos García-Martínez, Francisco J. Rodríguez, and Rafael Martí. Tabu Search with Strategic Oscillation for the Quadratic Minimum Spanning Tree. IIE Transactions, 46(4):414–428, 2014.
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[885]

Manuel Lozano, Daniel Molina, and Carlos García-Martínez. Iterated Greedy for the Maximum Diversity Problem. European Journal of Operational Research, 214(1):31–38, 2011.
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[886]

Zhipeng Lü, Fred Glover, and Jin-Kao Hao. A hybrid metaheuristic approach to solving the UBQP problem. European Journal of Operational Research, 207(3):1254–1262, 2010.
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[887]

Andrew Lucas. Ising formulations of many NP problems. Frontiers in Physics, 2:5, 2014.
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[888]

M. Lundy and A. Mees. Convergence of an Annealing Algorithm. Mathematical Programming, 34(1):111–124, 1986.
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Thibaut Lust and Jacques Teghem. Two-phase Pareto local search for the biobjective traveling salesman problem. Journal of Heuristics, 16(3):475–510, 2010.
[ bib | DOI ]

In this work, we present a method, called Two-Phase Pareto Local Search, to find a good approximation of the efficient set of the biobjective traveling salesman problem. In the first phase of the method, an initial population composed of a good approximation of the extreme supported efficient solutions is generated. We use as second phase a Pareto Local Search method applied to each solution of the initial population. We show that using the combination of these two techniques: good initial population generation plus Pareto Local Search gives better results than state-of-the-art algorithms. Two other points are introduced: the notion of ideal set and a simple way to produce near-efficient solutions of multiobjective problems, by using an efficient single-objective solver with a data perturbation technique.

[890]

Thibaut Lust and Jacques Teghem. The multiobjective multidimensional knapsack problem: a survey and a new approach. Arxiv preprint arXiv:1007.4063, 2010. Published as [891].
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[891]

Thibaut Lust and Jacques Teghem. The multiobjective multidimensional knapsack problem: a survey and a new approach. International Transactions in Operational Research, 19(4):495–520, 2012.
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[892]

Thibaut Lust and Andrzej Jaszkiewicz. Speed-up techniques for solving large-scale biobjective TSP. Computers & Operations Research, 37(3):521–533, 2010.
[ bib | DOI ]

Keywords: Multiobjective combinatorial optimization, Hybrid metaheuristics, TSP, Local search, Speed-up techniques

[893]

C. von Lücken, Benjamín Barán, and Carlos Brizuela. A survey on multi-objective evolutionary algorithms for many-objective problems. Computational Optimization and Applications, 58(3):707–756, 2014.
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[894]

Laurens van der Maaten and Geoffrey Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86):2579–2605, 2008.
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[895]

Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling. Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents. Journal of Artificial Intelligence Research, 61(1):523–562, January 2018.
[ bib ]

The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-pro_le success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open.

[896]

Sam Madden. From Databases to Big Data. IEEE Internet Computing, 16(3), 2012.
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[897]

M. Mahdavi, M. Fesanghary, and E. Damangir. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188(2):1567–1579, 2007.
[ bib | DOI ]

This paper develops an Improved harmony search (IHS) algorithm for solving optimization problems. IHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented. The IHS algorithm has been successfully applied to various benchmarking and standard engineering optimization problems. Numerical results reveal that the proposed algorithm can find better solutions when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems.

Keywords: Global optimization, Heuristics, Harmony search algorithm, Mathematical programming

[898]

Guilherme B. Mainieri and Débora P. Ronconi. New heuristics for total tardiness minimization in a flexible flowshop. Optimization Letters, pp.  1–20, 2012.
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Holger R. Maier, Angus R. Simpson, Aaron C. Zecchin, Wai Kuan Foong, Kuang Yeow Phang, Hsin Yeow Seah, and Chan Lim Tan. Ant Colony Optimization for Design of Water Distribution Systems. Journal of Water Resources Planning and Management, ASCE, 129(3):200–209, May / June 2003.
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Sri Srinivasa Raju M, Rammohan Mallipeddi, and Kedar Nath Das. A twin-archive guided decomposition based multi/many-objective evolutionary algorithm. Swarm and Evolutionary Computation, 71:101082, 2022.
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[901]

Katherine M. Malan and Andries Engelbrecht. A survey of techniques for characterising fitness landscapes and some possible ways forward. Information Sciences, 241:148–163, 2013.
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[902]

R. M. Males, R. M. Clark, P. J. Wehrman, and W. E. Gateset. Algorithm for mixing problems in water systems. Journal of Hydraulic Engineering, ASCE, 111(2):206–219, 1985.
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[903]

Vittorio Maniezzo. Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem. INFORMS Journal on Computing, 11(4):358–369, 1999.
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[904]

Vittorio Maniezzo and A. Carbonaro. An ANTS Heuristic for the Frequency Assignment Problem. Future Generation Computer Systems, 16(8):927–935, 2000.
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[905]

Vittorio Maniezzo and Alberto Colorni. The Ant System Applied to the Quadratic Assignment Problem. IEEE Transactions on Knowledge and Data Engineering, 11(5):769–778, 1999.
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E. Q. V. Martins. On a multicritera shortest path problem. European Journal of Operational Research, 16:236–245, 1984.
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[907]

R. T. Marler and J. S. Arora. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6):369–395, April 2004.
[ bib | DOI ]

Discusses a priori (scalarized) methods.

[908]

Raul Martín-Santamaría, Sergio Cavero, Alberto Herrán, Abraham Duarte, and J. Manuel Colmenar. A Practical Methodology for Reproducible Experimentation: An Application to the Double-Row Facility Layout Problem. Evolutionary Computation, pp.  1–35, November 2023.
[ bib | DOI ]

Keywords: irace

[909]

D. Martens, M. De Backer, R. Haesen, J. Vanthienen, M. Snoeck, and B. Baesens. Classification With Ant Colony Optimization. IEEE Transactions on Evolutionary Computation, 11(5):651–665, 2007.
[ bib ]

[910]

Raul Martín-Santamaría, Manuel López-Ibáñez, Thomas Stützle, and J. Manuel Colmenar. On the automatic generation of metaheuristic algorithms for combinatorial optimization problems. European Journal of Operational Research, 318(3):740–751, 2024.
[ bib | DOI ]

Metaheuristic algorithms have become one of the preferred approaches for solving optimization problems. Finding the best metaheuristic for a given problem is often difficult due to the large number of available approaches and possible algorithmic designs. Moreover, high-performing metaheuristics often combine general-purpose and problem-specific algorithmic components. We propose here an approach for automatically designing metaheuristics using a flexible framework of algorithmic components, from which algorithms are instantiated and evaluated by an automatic configuration method. The rules for composing algorithmic components are defined implicitly by the properties of each algorithmic component, in contrast to previous proposals, which require a handwritten algorithmic template or grammar. As a result, extending our framework with additional components, even problem-specific or user-defined ones, automatically updates the design space. Furthermore, since the generated algorithms are made up of components, they can be easily interpreted. We provide an implementation of our proposal and demonstrate its benefits by outperforming previous research in three distinct problems from completely different families: a facility layout problem, a vehicle routing problem and a clustering problem.

Keywords: irace

[911]

Hugues Marchand, Alexander Martin, Robert Weismantel, and Laurence Wolsey. Cutting planes in integer and mixed integer programming. Discrete Applied Mathematics, 123(1–3):397–446, 2002.
[ bib ]

[912]

O. Maron and A. W. Moore. The Racing Algorithm: Model Selection for Lazy Learners. Artificial Intelligence Research, 11(1–5):193–225, 1997.
[ bib | DOI ]

[913]

Olivier Martin and S. W. Otto. Partitioning of Unstructured Meshes for Load Balancing. Concurrency: Practice and Experience, 7(4):303–314, 1995.
[ bib ]

[914]

Olivier Martin and S. W. Otto. Combining Simulated Annealing with Local Search Heuristics. Annals of Operations Research, 63:57–75, 1996.
[ bib ]

[915]

Olivier Martin, S. W. Otto, and E. W. Felten. Large-Step Markov Chains for the Traveling Salesman Problem. Complex Systems, 5(3):299–326, 1991.
[ bib ]

[916]

Olivier Martin, S. W. Otto, and E. W. Felten. Large-step Markov Chains for the TSP Incorporating Local Search Heuristics. Operations Research Letters, 11(4):219–224, 1992.
[ bib ]

[917]

Rafael Martí, Gerhard Reinelt, and Abraham Duarte. A Benchmark Library and a Comparison of Heuristic Methods for the Linear Ordering Problem. Computational Optimization and Applications, 51(3):1297–1317, 2012.
[ bib ]

[918]

Raul Martín-Santamaría, Jesús Sánchez-Oro, S. Pérez-Peló, and Abraham Duarte. Strategic oscillation for the balanced minimum sum-of-squares clustering problem. Information Sciences, 585:529–542, 2022.
[ bib | DOI ]

[919]

Silvano Martello and Paolo Toth. Lower bounds and reduction procedures for the bin packing problem. Discrete Applied Mathematics, 28(1):59–70, 1990.
[ bib | DOI ]

[920]

Silvano Martello and Daniele Vigo. Exact solution of the two-dimensional finite bin packing problem. Management Science, 44(3):388–399, 1998.
[ bib | DOI ]

[921]

Abu S. Masud and C. L. Hwang. Interactive Sequential Goal Programming. Journal of the Operational Research Society, 32(5):391–400, May 1981.
[ bib | DOI ]

This paper introduces a new solution method based on Goal Programming for Multiple Objective Decision Making (MODM) problems. The method, called Interactive Sequential Goal Programming (ISGP), combines and extends the attractive features of both Goal Programming and interactive solution approaches for MODM problems. ISGP is applicable to both linear and non-linear problems. It uses existing single objective optimization techniques and, hence, available computer codes utilizing these techniques can be adapted for use in ISGP. The non-dominance of the "best-compromise" solution is assured. The information required from the decision maker in each iteration is simple. The proposed method is illustrated by solving a nutrition problem.

[922]

Franco Mascia, Manuel López-Ibáñez, Jérémie Dubois-Lacoste, and Thomas Stützle. Grammar-Based Generation of Stochastic Local Search Heuristics through Automatic Algorithm Configuration Tools. Computers & Operations Research, 51:190–199, 2014.
[ bib | DOI ]

[923]

Franco Mascia, Paola Pellegrini, Thomas Stützle, and Mauro Birattari. An Analysis of Parameter Adaptation in Reactive Tabu Search. International Transactions in Operational Research, 21(1):127–152, 2014.
[ bib ]

[924]

Renaud Masson, Thibaut Vidal, Julien Michallet, Puca Huachi Vaz Penna, Vinicius Petrucci, Anand Subramanian, and Hugues Dubedout. An Iterated Local Search Heuristic for Multi-capacity Bin Packing and Machine Reassignment Problems. Expert Systems with Applications, 40(13):5266–5275, 2013.
[ bib ]

[925]

Yazid Mati, Stéphane Dauzère-Pèrés, and Chams Lahlou. A General Approach for Optimizing Regular Criteria in the Job-shop Scheduling Problem. European Journal of Operational Research, 212(1):33–42, 2011.
[ bib ]

[926]

Atanu Mazumdar, Manuel López-Ibáñez, Tinkle Chugh, and Kaisa Miettinen. Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems. Evolutionary Computation, 31(4):375–399, 2023.
[ bib | DOI ]

For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data and an optimizer, e.g. a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to provide uncertainty information. However, building GPRs becomes computationally expensive when the size of the dataset is large. Using sparse GPRs reduces the computational cost of building the surrogates. However, sparse GPRs are not tailored to solve offline data-driven MOPs, where good accuracy of the surrogates is needed near Pareto optimal solutions. Treed GPR (TGPR-MO) surrogates for offline data-driven MOPs with continuous decision variables are proposed in this paper. The proposed surrogates first split the decision space into subregions using regression trees and build GPRs sequentially in regions close to Pareto optimal solutions in the decision space to accurately approximate tradeoffs between the objective functions. TGPR-MO surrogates are computationally inexpensive because GPRs are built only in a smaller region of the decision space utilizing a subset of the data. The TGPR-MO surrogates were tested on distance-based visualizable problems with various data sizes, sampling strategies, numbers of objective functions, and decision variables. Experimental results showed that the TGPR-MO surrogates are computationally cheaper and can handle datasets of large size. Furthermore, TGPR-MO surrogates produced solutions closer to Pareto optimal solutions compared to full GPRs and sparse GPRs.

Keywords: Gaussian processes, Kriging, Regression trees, Metamodelling, Surrogate, Pareto optimality

[927]

Ross M. McConnell, Kurt Mehlhorn, Stefan Näher, and Pascal Schweitzer. Certifying algorithms. Computer Science Review, 5(2):119–161, 2011.
[ bib | DOI ]

A certifying algorithm is an algorithm that produces, with each output, a certificate or witness (easy-to-verify proof) that the particular output has not been compromised by a bug. A user of a certifying algorithm inputs x, receives the output y and the certificate w, and then checks, either manually or by use of a program, that w proves that y is a correct output for input x. In this way, he/she can be sure of the correctness of the output without having to trust the algorithm. We put forward the thesis that certifying algorithms are much superior to non-certifying algorithms, and that for complex algorithmic tasks, only certifying algorithms are satisfactory. Acceptance of this thesis would lead to a change of how algorithms are taught and how algorithms are researched. The widespread use of certifying algorithms would greatly enhance the reliability of algorithmic software. We survey the state of the art in certifying algorithms and add to it. In particular, we start a theory of certifying algorithms and prove that the concept is universal.

Keywords: Algorithms, Software reliability, Certification

[928]

G. McCormick and R. S. Powell. Optimal Pump Scheduling in Water Supply Systems with Maximum Demand Charges. Journal of Water Resources Planning and Management, ASCE, 129(5):372–379, 2003.
[ bib | DOI | epub ]

Keywords: water supply; pumps; Markov processes; cost optimal control

[929]

G. McCormick and R. S. Powell. Derivation of near-optimal pump schedules for water distribution by simulated annealing. Journal of the Operational Research Society, 55(7):728–736, July 2004.
[ bib | DOI ]

The scheduling of pumps for clean water distribution is a partially discrete non-linear problem with many variables. The scheduling method described in this paper typically produces costs within 1% of a linear program-based solution, and can incorporate realistic non-linear costs that may be hard to incorporate in linear programming formulations. These costs include pump switching and maximum demand charges. A simplified model is derived from a standard hydraulic simulator. An initial schedule is produced by a descent method. Two-stage simulated annealing then produces solutions in a few minutes. Iterative recalibration ensures that the solution agrees closely with the results from a full hydraulic simulation.

[930]

James McDermott. When and Why Metaheuristics Researchers can Ignore "No Free Lunch" Theorems. SN Computer Science, 1(60):1–18, 2020.
[ bib | DOI ]

[931]

Catherine C. McGeoch. Analyzing Algorithms by Simulation: Variance Reduction Techniques and Simulation Speedups. ACM Computing Surveys, 24(2):195–212, 1992.
[ bib | DOI ]

Although experimental studies have been widely applied to the investigation of algorithm performance, very little attention has been given to experimental method in this area. This is unfortunate, since much can be done to improve the quality of the data obtained; often, much improvement may be needed for the data to be useful. This paper gives a tutorial discussion of two aspects of good experimental technique: the use of variance reduction techniques and simulation speedups in algorithm studies. In an illustrative study, application of variance reduction techniques produces a decrease in variance by a factor 1000 in one case, giving a dramatic improvement in the precision of experimental results. Furthermore, the complexity of the simulation program is improved from Θ(m n/H_n) to Θ(m + n log n) (where m is typically much larger than n), giving a much faster simulation program and therefore more data per unit of computation time. The general application of variance reduction techniques is also discussed for a variety of algorithm problem domains.

Keywords: experimental analysis of algorithms, move-to-front rule, self-organizing sequential search, statistical analysis of algorithms, transpose rule, variance reduction techniques

[932]

Catherine C. McGeoch. Toward an Experimental Method for Algorithm Simulation. INFORMS Journal on Computing, 8(1):1–15, 1996.
[ bib | DOI ]

[933]

Michael D. McKay, Richard J. Beckman, and W. J. Conover. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2):239–245, 1979.
[ bib | DOI ]

Two types of sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies. These plans are shown to be improvements over simple random sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.

[934]

Russell McKenna, Valentin Bertsch, Kai Mainzer, and Wolf Fichtner. Combining local preferences with multi-criteria decision analysis and linear optimization to develop feasible energy concepts in small communities. European Journal of Operational Research, 268(3):1092–1110, 2018.
[ bib ]

[935]

Robert I. Mckay, Nguyen Xuan Hoai, Peter Alexander Whigham, Yin Shan, and Michael O'Neill. Grammar-based Genetic Programming: A Survey. Genetic Programming and Evolvable Machines, 11(3-4):365–396, September 2010.
[ bib | DOI ]

[936]

Klaus Meer. Simulated annealing versus Metropolis for a TSP instance. Information Processing Letters, 104(6):216–219, 2007.
[ bib ]

[937]

Gábor Melis, Chris Dyer, and Phil Blunsom. On the State of the Art of Evaluation in Neural Language Models. Arxiv preprint arXiv:1807.02811, 2017.
[ bib | http ]

[938]

M. T. Melo, S. Nickel, and F. Saldanha-da Gama. Facility location and supply chain management: A review. European Journal of Operational Research, 196(2):401–412, 2009.
[ bib | DOI ]

[939]

Ole J. Mengshoel. Understanding the role of noise in stochastic local search: Analysis and experiments. Artificial Intelligence, 172(8):955–990, 2008.
[ bib ]

[940]

Juan-Julián Merelo and Carlos Cotta. Building bridges: the role of subfields in metaheuristics. SIGEVOlution, 1(4):9–15, 2006.
[ bib | DOI ]

[941]

Peter Merz and Bernd Freisleben. Memetic Algorithms for the Traveling Salesman Problem. Complex Systems, 13(4):297–345, 2001.
[ bib ]

[942]

Peter Merz and Bernd Freisleben. Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem. IEEE Transactions on Evolutionary Computation, 4(4):337–352, 2000.
[ bib ]

[943]

Peter Merz and Kengo Katayama. Memetic algorithms for the unconstrained binary quadratic programming problem. BioSystems, 78(1):99–118, 2004.
[ bib | DOI ]

[944]

D. Merkle and Martin Middendorf. Ant Colony Optimization with Global Pheromone Evaluation for Scheduling a Single Machine. Applied Intelligence, 18(1):105–111, 2003.
[ bib ]

[945]

D. Merkle and Martin Middendorf. Modeling the Dynamics of Ant Colony Optimization. Evolutionary Computation, 10(3):235–262, 2002.
[ bib ]

[946]

D. Merkle, Martin Middendorf, and Hartmut Schmeck. Ant Colony Optimization for Resource-Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation, 6(4):333–346, 2002.
[ bib ]

[947]

Peter Merz and Bernd Freisleben. Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming. Journal of Heuristics, 8(2):197–213, 2002.
[ bib | DOI ]

[948]

Rafael G. Mesquita, Ricardo M. A. Silva, Carlos A. B. Mello, and Péricles B. C. Miranda. Parameter tuning for document image binarization using a racing algorithm. Expert Systems with Applications, 42(5):2593–2603, 2015.
[ bib | DOI ]

Keywords: irace

[949]

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. Teller, and E. Teller. Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics, 21:1087–1092, 1953.
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[950]

Nicolas Meuleau and Marco Dorigo. Ant Colony Optimization and Stochastic Gradient Descent. Artificial Life, 8(2):103–121, 2002.
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[951]

Laurent Meunier, Herilalaina Rakotoarison, Pak-Kan Wong, Baptiste Rozière, Jérémy Rapin, Olivier Teytaud, Antoine Moreau, and Carola Doerr. Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking. Arxiv preprint arXiv:2010.04542, 2020.
[ bib | DOI ]

Keywords: Nevergrad, NGOpt

[952]

Laurent Meunier, Herilalaina Rakotoarison, Pak-Kan Wong, Baptiste Rozière, Jérémy Rapin, Olivier Teytaud, Antoine Moreau, and Carola Doerr. Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking. IEEE Transactions on Evolutionary Computation, 26(3):490–500, 2022.
[ bib | DOI ]

Keywords: nevergrad, NGOpt

[953]

R. M'Hallah. An iterated local search variable neighborhood descent hybrid heuristic for the total earliness tardiness permutation flow shop. International Journal of Production Research, 52(13):3802–3819, 2014.
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[954]

Zbigniew Michalewicz, Dipankar Dasgupta, Rodolphe G. Le Riche, and Marc Schoenauer. Evolutionary algorithms for constrained engineering problems. Computers and Industrial Engineering, 30(4):851–870, 1996.
[ bib | DOI ]

[955]

Julien Michallet, Christian Prins, Farouk Yalaoui, and Grégoire Vitry. Multi-start Iterated Local Search for the Periodic Vehicle Routing Problem with Time Windows and Time Spread Constraints on Services. Computers & Operations Research, 41:196–207, 2014.
[ bib ]

[956]

Kaisa Miettinen. Survey of methods to visualize alternatives in multiple criteria decision making problems. OR Spectrum, 36(1):3–37, 2014.
[ bib | DOI ]

[957]

Kaisa Miettinen, Petri Eskelinen, Francisco Ruiz, and Mariano Luque. NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point. European Journal of Operational Research, 206(2):426–434, October 2010.
[ bib | DOI ]

Most interactive methods developed for solving multiobjective optimization problems sequentially generate Pareto optimal or nondominated vectors and the decision maker must always allow impairment in at least one objective function to get a new solution. The NAUTILUS method proposed is based on the assumptions that past experiences affect decision makers' hopes and that people do not react symmetrically to gains and losses. Therefore, some decision makers may prefer to start from the worst possible objective values and to improve every objective step by step according to their preferences. In NAUTILUS, starting from the nadir point, a solution is obtained at each iteration which dominates the previous one. Although only the last solution will be Pareto optimal, the decision maker never looses sight of the Pareto optimal set, and the search is oriented so that (s)he progressively focusses on the preferred part of the Pareto optimal set. Each new solution is obtained by minimizing an achievement scalarizing function including preferences about desired improvements in objective function values. NAUTILUS is specially suitable for avoiding undesired anchoring effects, for example in negotiation support problems, or just as a means of finding an initial Pareto optimal solution for any interactive procedure. An illustrative example demonstrates how this new method iterates.

Keywords: Reference point methods, Interactive methods, Multiple objective programming, Pareto optimality, Preference information

[958]

Kaisa Miettinen, Jyri Mustajoki, and T. J. Stewart. Interactive multiobjective optimization with NIMBUS for decision making under uncertainty. OR Spectrum, 36(1):39–56, 2014.
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[959]

R. B. Millar and M. J. Anderson. Remedies for pseudoreplication. Fisheries Research, 70(2–3):397–407, 2004.
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[960]

George A. Miller. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2):81–97, 1956.
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[961]

Steven Minton. Automatically configuring constraint satisfaction programs: A case study. Constraints, 1(1):7–43, 1996.
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[962]

Gerardo Minella, Rubén Ruiz, and M. Ciavotta. A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem. INFORMS Journal on Computing, 20(3):451–471, 2008.
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[963]

Giovanni Misitano, Bekir Afsar, Giomara Larraga, and Kaisa Miettinen. Towards explainable interactive multiobjective optimization: R-XIMO. Autonomous Agents and Multi-Agent Systems, 36(42), 2022.
[ bib | DOI ]

[964]

Alfonsas Misevičius and Dovilė Kuznecovaitė. Investigating some strategies for construction of initial populations in genetic algorithms. Computational Science and Techniques, 5(1):560–573, 2018.
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[965]

Alfonsas Misevičius. Genetic Algorithm Hybridized with Ruin and Recreate Procedure: Application to the Quadratic Assignment Problem. Knowledge-Based Systems, 16(5–6):261–268, 2003.
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[966]

Alfonsas Misevičius. A modified simulated annealing algorithm for the quadratic assignment problem. Informatica, 14(4):497–514, 2003.
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[967]

P. Mitra, C. A. Murthy, and S. K. Pal. Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3):301–312, 2002.
[ bib | DOI ]

[968]

Alfonsas Misevičius, Dovilė Kuznecovaitė, and Jūratė Platužienė. Some Further Experiments with Crossover Operators for Genetic Algorithms. Informatica, 29(3):499–516, 2018.
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[969]

Nenad Mladenović and Pierre Hansen. Variable Neighborhood Search. Computers & Operations Research, 24(11):1097–1100, 1997.
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[970]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529, 2015.
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[971]

Jonas Močkus, Vytautas Tiesis, and Antanas Zilinskas. The application of bayesian methods for seeking the extremum. Towards global optimization, pp.  117–129, 1978.
[ bib ]

Proposed Bayesian optimization (but later than [2314])

[972]

Julián Molina, Luis V. Santana, Alfredo G. Hernández-Díaz, Carlos A. Coello Coello, and Rafael Caballero. g-Dominance: Reference point based dominance for Multiobjective Metaheuristics. European Journal of Operational Research, 197(2):685–692, September 2009.
[ bib | DOI ]

Proposed g-NSGA-II

[973]

Marco A. Montes de Oca, Doǧan Aydın, and Thomas Stützle. An Incremental Particle Swarm for Large-Scale Continuous Optimization Problems: An Example of Tuning-in-the-loop (Re)Design of Optimization Algorithms. Soft Computing, 15(11):2233–2255, 2011.
[ bib | DOI ]

[974]

Alysson Mondoro, Dan M. Frangopol, and Liang Liu. Multi-criteria robust optimization framework for bridge adaptation under climate change. Structural Safety, 74:14–23, 2018.
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[975]

Roberto Montemanni, L. M. Gambardella, A. E. Rizzoli, and A. V. Donati. Ant colony system for a dynamic vehicle routing problem. Journal of Combinatorial Optimization, 10:327–343, 2005.
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[976]

James Montgomery, Marcus Randall, and Tim Hendtlass. Solution bias in ant colony optimisation: Lessons for selecting pheromone models. Computers & Operations Research, 35(9):2728–2749, 2008.
[ bib | DOI ]

[977]

Elizabeth Montero, María-Cristina Riff, and Bertrand Neveu. A Beginner's Buide to Tuning Methods. Applied Soft Computing, 17:39–51, 2014.
[ bib | DOI ]

[978]

Marco A. Montes de Oca, Thomas Stützle, Mauro Birattari, and Marco Dorigo. Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm. IEEE Transactions on Evolutionary Computation, 13(5):1120–1132, 2009.
[ bib | DOI ]

[979]

Nicolas Monmarché, G. Venturini, and M. Slimane. On how pachycondyla apicalis ants suggest a new search algorithm. Future Generation Computer Systems, 16(8):937–946, 2000.
[ bib ]

[980]

Peter D. Morgan. Simulation of an adaptive behavior mechanism in an expert decision-maker. IEEE Transactions on Systems, Man, and Cybernetics, 23(1):65–76, 1993.
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[981]

J. N. Morse. Reducing the size of the nondominated set: Pruning by clustering. Computers & Operations Research, 7(1-2):55–66, 1980.
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[982]

Mouad Morabit, Guy Desaulniers, and Andrea Lodi. Machine-learning–based column selection for column generation. Transportation Science, 55(4):815–831, 2021.
[ bib ]

Keywords: graph neural networks

[983]

Sara Morin, Caroline Gagné, and Marc Gravel. Ant colony optimization with a specialized pheromone trail for the car-sequencing problem. European Journal of Operational Research, 197(3):1185–1191, 2009.
[ bib | DOI ]

This paper studies the learning process in an ant colony optimization algorithm designed to solve the problem of ordering cars on an assembly line (car-sequencing problem). This problem has been shown to be NP-hard and evokes a great deal of interest among practitioners. Learning in an ant algorithm is achieved by using an artificial pheromone trail, which is a central element of this metaheuristic. Many versions of the algorithm are found in literature, the main distinction among them being the management of the pheromone trail. Nevertheless, few of them seek to perfect learning by modifying the internal structure of the trail. In this paper, a new pheromone trail structure is proposed that is specifically adapted to the type of constraints in the car-sequencing problem. The quality of the results obtained when solving three sets of benchmark problems is superior to that of the best solutions found in literature and shows the efficiency of the specialized trail.

Keywords: Ant colony optimization,Car-sequencing problem,Pheromone trail,Scheduling

[984]

A. M. Mora, Juan-Julián Merelo, Juan Luis Jiménez Laredo, C. Millan, and J. Torrecillas. CHAC, a MOACO algorithm for computation of bi-criteria military unit path in the battlefield: Presentation and first results. International Journal of Intelligent Systems, 24(7):818–843, 2009.
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[985]

Max D. Morris and Toby J. Mitchell. Exploratory designs for computational experiments. Journal of Statistical Planning and Inference, 43(3):381–402, 1995.
[ bib | DOI ]

Keywords: Bayesian prediction

[986]

Pablo Moscato and José F. Fontanari. Stochastic Versus Deterministic Update in Simulated Annealing. Physics Letters A, 146(4):204–208, 1990.
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[987]

John Mote, Ishwar Murthy, and David L. Olson. A parametric approach to solving bicriterion shortest path problems. European Journal of Operational Research, 53(1):81–92, 1991.
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[988]

John Mote, David L. Olson, and M. A. Venkataramanan. A comparative multiobjective programming study. Mathematical and Computer Modelling, 10(10):719–729, 1988.
[ bib | DOI ]

The purpose of this study was to systematically evaluate a number of multiobjective programming concepts relative to reflection of utility, assurance of nondominated solutions and practicality for larger problems using conventional software. In the problem used, the nonlinear simulated DM utility function applied resulted in a nonextreme point solution. Very often, the preferred solution could end up being an extreme point solution, in which case the techniques relying upon LP concepts would work as well if not better than utilizing constrained objective attainments. The point is that there is no reason to expect linear or near linear utility.

Keywords: artificial DM, interactive

[989]

Sébastien Mouthuy, Yves Deville, and Pascal van Hentenryck. Constraint-based Very Large-Scale Neighborhood Search. Constraints, 17(2):87–122, 2012.
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[990]

Lucien Mousin, Marie-Eléonore Kessaci, and Clarisse Dhaenens. Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem. Arxiv preprint arXiv:1903.09035, 2019.
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[991]

Noura Al Moubayed, Andrei Petrovski, and John McCall. D²MOPSO: MOPSO based on decomposition and dominance with archiving using crowding distance in objective and solution spaces. Evolutionary Computation, 22(1):47–77, 2014.
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[992]

Vincent Mousseau and Roman Slowiński. Inferring an ELECTRE TRI model from assignment examples. Journal of Global Optimization, 12(2):157–174, 1998.
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[993]

Christian L. Müller and Ivos F. Sbalzarini. Energy Landscapes of Atomic Clusters as Black Box Optimization Benchmarks. Evolutionary Computation, 20(4):543–573, 2012.
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[994]

H. Mühlenbein and D. Schlierkamp-Voosen. Predictive models for the breeder genetic algorithm. Evolutionary Computation, 1(1):25–49, 1993.
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Keywords: crossover, intermediate, line

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Mario A. Muñoz and Kate Smith-Miles. Generating New Space-Filling Test Instances for Continuous Black-Box Optimization. Evolutionary Computation, 28(3):379–404, September 2020.
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[996]

Mario A. Muñoz, Yuan Sun, Michael Kirley, and Saman K. Halgamuge. Algorithm selection for black-box continuous optimization problems: a survey on methods and challenges. Information Sciences, 317:224–245, 2015.
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Mario A. Muñoz, Laura Villanova, Davaatseren Baatar, and Kate Smith-Miles. Instance Spaces for Machine Learning Classification. Machine Learning, 107(1):109–147, 2018.
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[998]

Yuichi Nagata and Shigenobu Kobayashi. A Powerful Genetic Algorithm Using Edge Assembly Crossover for the Traveling Salesman Problem. INFORMS Journal on Computing, 25(2):346–363, 2013.
[ bib | DOI ]

This paper presents a genetic algorithm (GA) for solving the traveling salesman problem (TSP). To construct a powerful GA, we use edge assembly crossover (EAX) and make substantial enhancements to it: (i) localization of EAX together with its efficient implementation and (ii) the use of a local search procedure in EAX to determine good combinations of building blocks of parent solutions for generating even better offspring solutions from very high-quality parent solutions. In addition, we develop (iii) an innovative selection model for maintaining population diversity at a negligible computational cost. Experimental results on well-studied TSP benchmarks demonstrate that the proposed GA outperforms state-of-the-art heuristic algorithms in finding very high-quality solutions on instances with up to 200,000 cities. In contrast to the state-of-the-art TSP heuristics, which are all based on the Lin-Kernighan (LK) algorithm, our GA achieves top performance without using an LK-based algorithm.

Keywords: TSP, EAX, evolutionary algorithms

[999]

Marcelo S. Nagano, Fernando L. Rossi, and Nádia J. Martarelli. High-performing heuristics to minimize flowtime in no-idle permutation flowshop. Engineering Optimization, 51(2):185–198, 2019.
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Yuichi Nagata and David Soler. A New Genetic Algorithm for the Asymmetric TSP. Expert Systems with Applications, 39(10):8947–8953, 2012.
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Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, and Dirk Sudholt. On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements? Algorithmica, 81(2):858–885, 2019.
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Yang Nan, Ke Shang, Hisao Ishibuchi, and Linjun He. Reverse strategy for non-dominated archiving. IEEE Access, 8:119458–119469, 2020.
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Kaname Narukawa, Yu Setoguchi, Yuki Tanigaki, Markus Olhofer, Bernhard Sendhoff, and Hisao Ishibuchi. Preference representation using Gaussian functions on a hyperplane in evolutionary multi-objective optimization. Soft Computing, 20(7):2733–2757, July 2016.
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[1004]

John Nash and Ravi Varadhan. Unifying Optimization Algorithms to Aid Software System Users: optimx for R. Journal of Statistical Software, 43(9):1–14, 2011.
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Keywords: NEH heuristic

[1006]

Antonio J. Nebro, Manuel López-Ibáñez, José García-Nieto, and Carlos A. Coello Coello. On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis. Swarm Intelligence, 18:105–139, 2024.
[ bib | DOI ]

Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of potential MOPSOs; and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. AutoMOPSO is publicly available as part of the jMetal framework.

[1007]

Antonio J. Nebro, F. Luna, Enrique Alba, Bernabé Dorronsoro, Juan J. Durillo, and A. Beham. AbYSS: Adapting Scatter Search to Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 12(4):439–457, August 2008.
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[1008]

F. Nerri and Carlos Cotta. Memetic algorithms and memetic computing optimization: A literature review. Swarm and Evolutionary Computation, 2:1–14, 2012.
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[1009]

Frank Neumann, Dirk Sudholt, and Carsten Witt. Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intelligence, 3(1):35–68, 2009.
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[1010]

Frank Neumann and Carsten Witt. Runtime Analysis of a Simple Ant Colony Optimization Algorithm. Electronic Colloquium on Computational Complexity (ECCC), 13(084), 2006.
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Allen Newell and Herbert A. Simon. Computer Science as Empirical Inquiry: Symbols and Search. Communications of the ACM, 19(3):113–126, March 1976.
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Computer science is the study of the phenomena surrounding computers. The founders of this society understood this very well when they called themselves the Association for Computing Machinery. The machine-not just the hardware, but the programmed, living machine-is the organism we study.

Keywords: cognition, Turing, search, problem solving, symbols, heuristics, list processing, computer science, artificial intelligence, science, empirical

[1012]

Viet-Phuong Nguyen, Christian Prins, and Caroline Prodhon. A Multi-start Iterated Local Search with Tabu List and Path Relinking for the Two-echelon Location-routing Problem. Engineering Applications of Artificial Intelligence, 25(1):56–71, 2012.
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[1013]

Anh-Tuan Nguyen, Sigrid Reiter, and Philippe Rigo. A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113:1043–1058, 2014.
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[1014]

Trung Thanh Nguyen, Shengxiang Yang, and Jürgen Branke. Evolutionary Dynamic Optimization: A Survey of the State of the Art. Swarm and Evolutionary Computation, 6:1–24, 2012.
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[1015]

Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. Genetic Programming for Evolving Due-Date Assignment Models in Job Shop Environments. Evolutionary Computation, 22(1):105–138, 2014.
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[1016]

Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming. IEEE Transactions on Evolutionary Computation, 18(2):193–208, 2014.
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[1017]

Peter Nightingale, Özguür Akgün, Ian P. Gent, Christopher Jefferson, Ian Miguel, and Patrick Spracklen. Automatically Improving Constraint Models in Savile Row. Artificial Intelligence, 251:35–61, 2017.
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[1018]

Chao Ning and Fengqi You. Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming. Computers & Chemical Engineering, 125:434–448, 2019.
[ bib | DOI ]

This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learning-based data-driven multistage optimization with a learning-while-optimizing scheme are presented.

Keywords: Data-driven optimization, Decision making under uncertainty, Big data, Machine learning, Deep learning

[1019]

Naoki Nishimura, Kotaro Tanahashi, Koji Suganuma, Masamichi J. Miyama, and Masayuki Ohzeki. Item listing optimization for e-commerce websites based on diversity. Frontiers in Computer Science, 1:2, 2019.
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Keywords: Quantum Annealing

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Vilas Nitivattananon, Elaine C. Sadowski, and Rafael G. Quimpo. Optimization of Water Supply System Operation. Journal of Water Resources Planning and Management, ASCE, 122(5):374–384, September / October 1996.
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Bruno Nogueira, Rian G. S. Pinheiro, and Anand Subramanian. A Hybrid Iterated Local Search Heuristic for the Maximum Weight Independent Set Problem. Optimization Letters, 12(3):567–583, 2018.
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[1022]

B. A. Nosek, G. Alter, G. C. Banks, D. Borsboom, S. D. Bowman, S. J. Breckler, S. Buck, C. D. Chambers, G. Chin, G. Christensen, M. Contestabile, A. Dafoe, E. Eich, J. Freese, R. Glennerster, D. Goroff, D. P. Green, B. Hesse, M. Humphreys, J. Ishiyama, D. Karlan, A. Kraut, A. Lupia, P. Mabry, T. Madon, N. Malhotra, E. Mayo-Wilson, M. McNutt, E. Miguel, E. L. Paluck, U. Simonsohn, C. Soderberg, B. A. Spellman, J. Turitto, G. VandenBos, S. Vazire, E. J. Wagenmakers, R. Wilson, and T. Yarkoni. Promoting an open research culture. Science, 348(6242):1422–1425, June 2015.
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[1023]

Brian A. Nosek, Charles R. Ebersole, Alexander C. DeHaven, and David T. Mellor. The Preregistration Revolution. Proceedings of the National Academy of Sciences, 115(11):2600–2606, March 2018.
[ bib | DOI ]

Progress in science relies in part on generating hypotheses with existing observations and testing hypotheses with new observations. This distinction between postdiction and prediction is appreciated conceptually but is not respected in practice. Mistaking generation of postdictions with testing of predictions reduces the credibility of research findings. However, ordinary biases in human reasoning, such as hindsight bias, make it hard to avoid this mistake. An effective solution is to define the research questions and analysis plan before observing the research outcomes–a process called preregistration. Preregistration distinguishes analyses and outcomes that result from predictions from those that result from postdictions. A variety of practical strategies are available to make the best possible use of preregistration in circumstances that fall short of the ideal application, such as when the data are preexisting. Services are now available for preregistration across all disciplines, facilitating a rapid increase in the practice. Widespread adoption of preregistration will increase distinctiveness between hypothesis generation and hypothesis testing and will improve the credibility of research findings.

[1024]

Yaghout Nourani and Bjarne Andresen. A Comparison of Simulated Annealing Cooling Strategies. Journal of Physics A, 31(41):8373–8385, 1998.
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Eugeniusz Nowicki and Czeslaw Smutnicki. A Fast Taboo Search Algorithm for the Job Shop Problem. Management Science, 42(6):797–813, 1996.
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Eugeniusz Nowicki and Czeslaw Smutnicki. A fast tabu search algorithm for the permutation flow-shop problem. European Journal of Operational Research, 91(1):160–175, 1996.
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Open Science Collaboration. Estimating the reproducibility of psychological science. Science, 349(6251):aac4716, 2015.
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[1028]

Gabriela Ochoa and Nadarajen Veerapen. Mapping the global structure of TSP fitness landscapes. Journal of Heuristics, 24(3):265–294, 2018.
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Angelo Oddi, Amadeo Cesta, Nicola Policella, and Stephen F. Smith. Combining Variants of Iterative Flattening Search. Engineering Applications of Artificial Intelligence, 21(5):683–690, 2008.
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Angelo Oddi, Amadeo Cesta, Nicola Policella, and Stephen F. Smith. Iterative Flattening Search for Resource Constrained Scheduling. Journal of Intelligent Manufacturing, 21(1):17–30, 2010.
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F. A. Ogbu and David K. Smith. The Application of the Simulated Annealing Algorithm to the Solution of the n/m/C Max Flowshop Problem. Computers & Operations Research, 17(3):243–253, 1990.
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Jeffrey W. Ohlmann and Barrett W. Thomas. A Compressed-Annealing Heuristic for the Traveling Salesman Problem with Time Windows. INFORMS Journal on Computing, 19(1):80–90, 2007.
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[1033]

Pietro S. Oliveto, Jun He, and Xin Yao. Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results. International Journal of Automation and Computing, 4(3):281–293, 2007.
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Pietro S. Oliveto and Carsten Witt. Improved time complexity analysis of the Simple Genetic Algorithm. Theoretical Computer Science, 605:21–41, 2015.
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David L. Olson. Review of Empirical Studies in Multiobjective Mathematical Programming: Subject Reflection of Nonlinear Utility and Learning. Decision Sciences, 23(1):1–20, 1992.
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Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade-offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade-offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision-making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade-offs of value. Support of nonlinear utility and enhancing decision-maker learning are considered.

Keywords: Decision Analysis, Human Information Processing, Linear Programming

[1036]

Roland Olsson and Arne Løkketangen. Using Automatic Programming to Generate State-of-the-art Algorithms for Random 3-SAT. Journal of Heuristics, 19(5):819–844, 2013.
[ bib | DOI ]

Uses evolution but it is not genetic programming, nor grammatical evolution.

[1037]

Mihai Oltean. Evolving Evolutionary Algorithms Using Linear Genetic Programming. Evolutionary Computation, 13(3):387–410, 2005.
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[1038]

Michael O'Neill and Conor Ryan. Grammatical Evolution. IEEE Transactions on Evolutionary Computation, 5(4):349–358, 2001.
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Lindell E. Ormsbee and Kevin E. Lansey. Optimal Control of Water Supply Pumping Systems. Journal of Water Resources Planning and Management, ASCE, 120(2):237–252, 1994.
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Lindell E. Ormsbee and Srinivasa L. Reddy. Nonlinear Heuristic for Pump Operations. Journal of Water Resources Planning and Management, ASCE, 121(4):302–309, July / August 1995.
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Jeffrey E. Orosz and Sheldon H. Jacobson. Analysis of Static Simulated Annealing Algorithms. Journal of Optimization Theory and Applications, 115(1):165–182, 2002.
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Ibrahim H. Osman and Chris N. Potts. Simulated Annealing for Permutation Flow-Shop Scheduling. Omega, 17(6):551–557, 1989.
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P. S. Ow and T. E. Morton. Filtered Beam Search in Scheduling. International Journal of Production Research, 26:297–307, 1988.
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Proposed beam search

[1045]

Gül Özerol and Esra Karasakal. Interactive outranking approaches for multicriteria decision-making problems with imprecise information. Journal of the Operational Research Society, 59:1253–1268, 2007.
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Manfred Padberg and Giovanni Rinaldi. A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems. SIAM Review, 33(1):60–100, 1991.
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Federico Pagnozzi and Thomas Stützle. Speeding up Local Search for the Insert Neighborhood in the Weighted Tardiness Permutation Flowshop Problem. Optimization Letters, 11:1283–1292, 2017.
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[1048]

Federico Pagnozzi and Thomas Stützle. Automatic Design of Hybrid Stochastic Local Search Algorithms for Permutation Flowshop Problems. European Journal of Operational Research, 276:409–421, 2019.
[ bib | DOI ]

Stochastic local search methods are at the core of many effective heuristics for tackling different permutation flowshop problems (PFSPs). Usually, such algorithms require a careful, manual algorithm engineering effort to reach high performance. An alternative to the manual algorithm engineering is the automated design of effective SLS algorithms through building flexible algorithm frameworks and using automatic algorithm configuration techniques to instantiate high-performing algorithms. In this paper, we automatically generate new high-performing algorithms for some of the most widely studied variants of the PFSP. More in detail, we (i) developed a new algorithm framework, EMILI, that implements algorithm-specific and problem-specific building blocks; (ii) define the rules of how to compose algorithms from the building blocks; and (iii) employ an automatic algorithm configuration tool to search for high performing algorithm configurations. With these ingredients, we automatically generate algorithms for the PFSP with the objectives makespan, total completion time and total tardiness, which outperform the best algorithms obtained by a manual algorithm engineering process.

Keywords: EMILI

[1049]

Federico Pagnozzi and Thomas Stützle. Evaluating the impact of grammar complexity in automatic algorithm design. International Transactions in Operational Research, pp.  1–26, 2020.
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[1050]

Federico Pagnozzi and Thomas Stützle. Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints. Operations Research Perspectives, 8:100180, 2021.
[ bib | DOI ]

Automatic design of stochastic local search algorithms has been shown to be very effective in generating algorithms for the permutation flowshop problem for the most studied objectives including makespan, flowtime and total tardiness. The automatic design system uses a configuration tool to combine algorithmic components following a set of rules defined as a context-free grammar. In this paper we use the same system to tackle two of the most studied additional constraints for these objectives: sequence dependent setup times and no-idle constraint. Additional components have been added to adapt the system to the new problems while keeping intact the grammar structure and the experimental setup. The experiments show that the generated algorithms outperform the state of the art in each case.

[1051]

Alberto Pajares, Xavier Blasco, Juan Manuel Herrero, and Miguel A. Martínez. A Comparison of Archiving Strategies for Characterization of Nearly Optimal Solutions under Multi-Objective Optimization. Mathematics, 9(9):999, 2021.
[ bib | DOI ]

In a multi-objective optimization problem, in addition to optimal solutions, multimodal and/or nearly optimal alternatives can also provide additional useful information for the decision maker. However, obtaining all nearly optimal solutions entails an excessive number of alternatives. Therefore, to consider the nearly optimal solutions, it is convenient to obtain a reduced set, putting the focus on the potentially useful alternatives. These solutions are the alternatives that are close to the optimal solutions in objective space, but which differ significantly in the decision space. To characterize this set, it is essential to simultaneously analyze the decision and objective spaces. One of the crucial points in an evolutionary multi-objective optimization algorithm is the archiving strategy. This is in charge of keeping the solution set, called the archive, updated during the optimization process. The motivation of this work is to analyze the three existing archiving strategies proposed in the literature (ArchiveUpdateP_Q,εD_xy, Archive_nevMOGA, and targetSelect) that aim to characterize the potentially useful solutions. The archivers are evaluated on two benchmarks and in a real engineering example. The contribution clearly shows the main differences between the three archivers. This analysis is useful for the design of evolutionary algorithms that consider nearly optimal solutions.

Keywords: multi-objective optimization; nearly optimal solutions; non-epsilon dominance; multimodality; decision space diversity; archiving strategy; evolutionary algorithm; non-linear parametric identification

[1052]

Daniel Palhazi Cuervo, Peter Goos, Kenneth Sörensen, and Emely Arráiz. An Iterated Local Search Algorithm for the Vehicle Routing Problem with Backhauls. European Journal of Operational Research, 237(2):454–464, 2014.
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Gintaras Palubeckis. Iterated tabu search for the unconstrained binary quadratic optimization problem. Informatica, 17(2):279–296, 2006.
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[1054]

Quan-Ke Pan and Rubén Ruiz. Local Search Methods for the Flowshop Scheduling Problem with Flowtime Minimization. European Journal of Operational Research, 222(1):31–43, 2012.
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[1055]

Quan-Ke Pan and Rubén Ruiz. A Comprehensive Review and Evaluation of Permutation Flowshop Heuristics to Minimize Flowtime. Computers & Operations Research, 40(1):117–128, 2013.
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[1056]

Quan-Ke Pan, Rubén Ruiz, and Pedro Alfaro-Fernández. Iterated Search Methods for Earliness and Tardiness Minimization in Hybrid Flowshops with Due Windows. Computers & Operations Research, 80:50–60, 2017.
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Quan-Ke Pan, Mehmet Fatih Tasgetiren, and Yun-Chia Liang. A Discrete Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem. Computers and Industrial Engineering, 55(4):795 – 816, 2008.
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Quan-Ke Pan, Ling Wang, and Bao-Hua Zhao. An improved iterated greedy algorithm for the no-wait flow shop scheduling problem with makespan criterion. International Journal of Advanced Manufacturing Technology, 38(7-8):778–786, 2008.
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[1059]

Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359, 2009.
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Luís Paquete, Tommaso Schiavinotto, and Thomas Stützle. On Local Optima in Multiobjective Combinatorial Optimization Problems. Annals of Operations Research, 156:83–97, 2007.
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In this article, local optimality in multiobjective combinatorial optimization is used as a baseline for the design and analysis of two iterative improvement algorithms. Both algorithms search in a neighborhood that is defined on a collection of sets of feasible solutions and their acceptance criterion is based on outperformance relations. Proofs of the soundness and completeness of these algorithms are given.

Keywords: Pareto local search, PLS

[1061]

Luís Paquete and Thomas Stützle. A study of stochastic local search algorithms for the biobjective QAP with correlated flow matrices. European Journal of Operational Research, 169(3):943–959, 2006.
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[1062]

Luís Paquete and Thomas Stützle. Design and analysis of stochastic local search for the multiobjective traveling salesman problem. Computers & Operations Research, 36(9):2619–2631, 2009.
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[1063]

S. N. Parragh, Karl F. Doerner, Richard F. Hartl, and Xavier Gandibleux. A heuristic two-phase solution approach for the multi-objective dial-a-ride problem. Networks, 54(4):227–242, 2009.
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Rebecca Parsons and Mark Johnson. A Case Study in Experimental Design Applied to Genetic Algorithms with Applications to DNA Sequence Assembly. American Journal of Mathematical and Management Sciences, 17(3-4):369–396, 1997.
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[1065]

Moon-Won Park and Yeong-Dae Kim. A systematic procedure for setting parameters in simulated annealing algorithms. Computers & Operations Research, 25(3):207–217, 1998.
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[1066]

R. S. Parpinelli, H. S. Lopes, and A. A. Freitas. Data Mining with an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computation, 6(4):321–332, 2002.
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Terence J. Parr and Russell W. Quong. ANTLR: A predicated-LL (k) parser generator. Software — Practice & Experience, 25(7):789–810, 1995.
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R. O. Parreiras and J. A. Vascocelos. A multiplicative version of PROMETHEE II applied to multiobjective optimization problems. European Journal of Operational Research, 183:729–740, 2007.
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Gerald Paul. Comparative performance of tabu search and simulated annealing heuristics for the quadratic assignment problem. Operations Research Letters, 38(6):577–581, 2010.
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[1070]

Judea Pearl. The seven tools of causal inference, with reflections on machine learning. Communications of the ACM, 62(3):54–60, 2019.
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Martín Pedemonte, Sergio Nesmachnow, and Héctor Cancela. A survey on parallel ant colony optimization. Applied Soft Computing, 11(8):5181–5197, 2011.
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Paola Pellegrini, Mauro Birattari, and Thomas Stützle. A Critical Analysis of Parameter Adaptation in Ant Colony Optimization. Swarm Intelligence, 6(1):23–48, 2012.
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[1073]

Paola Pellegrini, L. Castelli, and R. Pesenti. Metaheuristic algorithms for the simultaneous slot allocation problem. IET Intelligent Transport Systems, 6(4):453–462, December 2012.
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[1074]

Paola Pellegrini, Franco Mascia, Thomas Stützle, and Mauro Birattari. On the Sensitivity of Reactive Tabu Search to its Meta-parameters. Soft Computing, 18(11):2177–2190, 2014.
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[1075]

Puca Huachi Vaz Penna, Anand Subramanian, and Luiz Satoru Ochi. An Iterated Local Search Heuristic for the Heterogeneous Fleet Vehicle Routing Problem. Journal of Heuristics, 19(2):201–232, 2013.
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[1076]

Jeffrey M. Perkel. Challenge to scientists: does your ten-year-old code still run? Nature, 584:556–658, 2020.
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Keywords: reproducibility; software engineering; ReScience C; Ten Years Reproducibility Challenge; code reusability

[1077]

Leslie Pérez Cáceres, Manuel López-Ibáñez, and Thomas Stützle. Ant colony optimization on a limited budget of evaluations. Swarm Intelligence, 9(2-3):103–124, 2015.
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[1078]

Matias Péres, Germán Ruiz, Sergio Nesmachnow, and Ana C. Olivera. Multiobjective evolutionary optimization of traffic flow and pollution in Montevideo, Uruguay. Applied Soft Computing, 70:472–485, 2018.
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Keywords: Multiobjective evolutionary algorithms,Pollution,Simulation,Traffic flow

[1079]

A. Pessoa, E. Uchoa, M. Aragão, and R. Rodrigues. Exact Algorithm over an Arc-time-indexed formulation for Parallel Machine Scheduling Problems. Mathematical Programming Computation, 2(3–4):259–290, 2010.
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Gilles Pesant, Michel Gendreau, Jean-Yves Potvin, and J.-M. Rousseau. An Exact Constraint Logic Programming Algorithm for the Traveling Salesman Problem with Time Windows. Transportation Science, 32:12–29, 1998.
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Charles W. Petit. Touched by nature: putting evolution to work on the assembly line. U.S. News & World Report, 125(4):43–45, July 1998.
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Evolutionary optimization of turbine design of the Boeing 777 GE

[1082]

Anastassios E. Petropoulos, Eugene P. Bonfiglio, Daniel J. Grebow, Try Lam, Jeffrey S. Parker, Juan Arrieta, Damon F. Landau, Rodney L. Anderson, Eric D. Gustafson, Gregory J. Whiffen, Paul A. Finlayson, and Jon A. Sims. GTOC5: Results from Jet Propulsion Lab. Acta Futura, 8:21–27, 2014.
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We present the methods and results of the Jet Propulsion Laboratory team in the 5th Global Trajectory Optimization Competition. Our broad-search strategy utilized several recently developed phase-free metrics for rapidly narrowing the search options. Two different, adaptive, branch-and-prune strategies were employed to build up asteroid sequences using a rendezvous-flyby-rendezvous building block, with a robust local optimizer in the loop. The best of these sequences were refined end-to-end using the same direct optimizer, to yield the winning 18-point, 18-asteroid solution.

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Marek Petrik and Shlomo Zilberstein. Learning parallel portfolios of algorithms. Annals of Mathematics and Artificial Intelligence, 48(1):85–106, 2006.
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Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché Buc, Emily Fox, and Hugo Larochelle. Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). Arxiv preprint arXiv:2003.12206 [cs.LG], 2020.
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David Pisinger and Stefan Ropke. A General Heuristic for Vehicle Routing Problems. Computers & Operations Research, 34(8):2403–2435, 2007.
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Rapeepan Pitakaso, Christian Almeder, Karl F. Doerner, and Richard F. Hartl. Combining exact and population-based methods for the Constrained Multilevel Lot Sizing Problem. International Journal of Production Research, 44(22):4755–4771, 2006.
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Rapeepan Pitakaso, Christian Almeder, Karl F. Doerner, and Richard F. Hartl. A Max-Min Ant System for unconstrained multi-level lot-sizing problems. Computers & Operations Research, 34(9):2533–2552, 2007.
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In this paper, we present an ant-based algorithm for solving unconstrained multi-level lot-sizing problems called ant system for multi-level lot-sizing algorithm (ASMLLS). We apply a hybrid approach where we use ant colony optimization in order to find a good lot-sizing sequence, i.e. a sequence of the different items in the product structure in which we apply a modified Wagner-Whitin algorithm for each item separately. Based on the setup costs each ant generates a sequence of items. Afterwards a simple single-stage lot-sizing rule is applied with modified setup costs. This modification of the setup costs depends on the position of the item in the lot-sizing sequence, on the items which have been lot-sized before, and on two further parameters, which are tried to be improved by a systematic search. For small-sized problems ASMLLS is among the best algorithms, but for most medium- and large-sized problems it outperforms all other approaches regarding solution quality as well as computational time.

Keywords: Ant colony optimization, Material requirements planning, Multi-level lot-sizing, Wagner-Whitin algorithm

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Daniel Porumbel, Gilles Goncalves, Hamid Allaoui, and Tienté Hsu. Iterated Local Search and Column Generation to solve Arc-Routing as a Permutation Set-Covering Problem. European Journal of Operational Research, 256(2):349–367, 2017.
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Juan Porta, Jorge Parapar, Ramón Doallo, Vasco Barbosa, Inés Santé, Rafael Crecente, and Carlos Díaz. A Population-based Iterated Greedy Algorithm for the Delimitation and Zoning of Rural Settlements. Computers, Environment and Urban Systems, 39:12–26, 2013.
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Marcelo Prates, Pedro H. C. Avelar, Henrique Lemos, Luis C. Lamb, and Moshe Y. Vardi. Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):4731–4738, July 2019.
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Keywords: Benchmarking, Two-variable non-parametric tests, Evolutionary algorithms, Dominance, Stochastic optimization, Numerical optimization, Mann-Whitney test

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Yutao Qi, Xiaoliang Ma, Fang Liu, Licheng Jiao, Jianyong Sun, and Jianshe Wu. MOEA/D with adaptive weight adjustment. Evolutionary Computation, 22(2):231–264, 2014.
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David Garzón Ramos and Mauro Birattari. Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors. Applied Sciences, 10(13):4654, 2020.
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Juan-Manuel Ramos-Pérez, Gara Miranda, Eduardo Segredo, Coromoto León, and Casiano Rodríguez-León. Application of Multi-Objective Evolutionary Algorithms for Planning Healthy and Balanced School Lunches. Mathematics, 9(1):80, December 2021.
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A multi-objective formulation of the Menu Planning Problem, which is termed the Multi-objective Menu Planning Problem, is presented herein. Menu planning is of great interest in the health field due to the importance of proper nutrition in today's society, and particularly, in school canteens. In addition to considering the cost of the meal plan as the classic objective to be minimized, we also introduce a second objective aimed at minimizing the degree of repetition of courses and food groups that a particular meal plan consists of. The motivation behind this particular multi-objective formulation is to offer a meal plan that is not only affordable but also varied and balanced from a nutritional standpoint. The plan is designed for a given number of days and ensures that the specific nutritional requirements of school-age children are satisfied. The main goal of the current work is to demonstrate the multi-objective nature of the said formulation, through a comprehensive experimental assessment carried out over a set of multi-objective evolutionary algorithms applied to different instances. At the same time, we are also interested in validating the multi-objective formulation by performing quantitative and qualitative analyses of the solutions attained when solving it. Computational results show the multi-objective nature of the said formulation, as well as that it allows suitable meal plans to be obtained.

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Keywords: irace, SMAC, GGA, REVAC, VRP

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Keywords: artificial DM, interactive

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Patrick M. Reed, David Hadka, Jonathan D. Herman, Joseph R. Kasprzyk, and Joshua B. Kollat. Evolutionary multiobjective optimization in water resources: The past, present, and future. Advances in Water Resources, 51:438–456, 2013.
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Tao Chen, Miqing Li, and Xin Yao. Standing on the shoulders of giants: Seeding search-based multi-objective optimization with prior knowledge for software service composition. Information and Software Technology, 114:155–175, 2019.
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Frederik Rehbach, Martin Zaefferer, Andreas Fischbach, Günther Rudolph, and Thomas Bartz-Beielstein. Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm. IEEE Transactions on Evolutionary Computation, 26(6):1365–1379, 2022.
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Marc Reimann, Karl F. Doerner, and Richard F. Hartl. D-ants: Savings based ants divide and conquer the vehicle routing problems. Computers & Operations Research, 31(4):563–591, 2004.
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Marc Reimann and Marco Laumanns. Savings based ant colony optimization for the capacitated minimum spanning tree problem. Computers & Operations Research, 33(6):1794–1822, 2006.
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The problem of connecting a set of client nodes with known demands to a root node through a minimum cost tree network, subject to capacity constraints on all links is known as the capacitated minimum spanning tree (CMST) problem. As the problem is NP-hard, we propose a hybrid ant colony optimization (ACO) algorithm to tackle it heuristically. The algorithm exploits two important problem characteristics: (i) the CMST problem is closely related to the capacitated vehicle routing problem (CVRP), and (ii) given a clustering of client nodes that satisfies capacity constraints, the solution is to find a MST for each cluster, which can be done exactly in polynomial time. Our ACO exploits these two characteristics of the CMST by a solution construction originally developed for the CVRP. Given the CVRP solution, we then apply an implementation of Prim's algorithm to each cluster to obtain a feasible CMST solution. Results from a comprehensive computational study indicate the efficiency and effectiveness of the proposed approach.

Keywords: Ant colony Optimization, Capacitated minimum spanning tree problem

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Zhi-Gang Ren, Zu-Ren Feng, Liang-Jun Ke, and Zhao-Jun Zhang. New Ideas for Applying Ant Colony Optimization to the Set Covering Problem. Computers and Industrial Engineering, 58(4):774–784, 2010.
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Keywords: anchoring bias, best-worst method, cognitive bias, MADM, multi-attribute weighting, SMART, Swing

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The problem of selecting an effective algorithm arises in a wide variety of situations. This chapter starts with a discussion on abstract models: the basic model and associated problems, the model with selection based on features, and the model with variable performance criteria. One objective of this chapter is to explore the applicability of the approximation theory to the algorithm selection problem. There is an intimate relationship here and that the approximation theory forms an appropriate base upon which to develop a theory of algorithm selection methods. The approximation theory currently lacks much of the necessary machinery for the algorithm selection problem. There is a need to develop new results and apply known techniques to these new circumstances. The final pages of this chapter form a sort of appendix, which lists 15 specific open problems and questions in this area. There is a close relationship between the algorithm selection problem and the general optimization theory. This is not surprising since the approximation problem is a special form of the optimization problem. Most realistic algorithm selection problems are of moderate to high dimensionality and thus one should expect them to be quite complex. One consequence of this is that most straightforward approaches (even well-conceived ones) are likely to lead to enormous computations for the best selection. The single most important part of the solution of a selection problem is the appropriate choice of the form for selection mapping. It is here that theories give the least guidance and that the art of problem solving is most crucial.

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Juan Carlos Rivera, H. Murat Afsar, and Christian Prins. A Multistart Iterated Local Search for the Multitrip Cumulative Capacitated Vehicle Routing Problem. Computational Optimization and Applications, 61(1):159–187, 2015.
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Lucía Rivadeneira, Jian-Bo Yang, and Manuel López-Ibáñez. Predicting tweet impact using a novel evidential reasoning prediction method. Expert Systems with Applications, 169:114400, May 2021.
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This study presents a novel evidential reasoning (ER) prediction model called MAKER-RIMER to examine how different features embedded in Twitter posts (tweets) can predict the number of retweets achieved during an electoral campaign. The tweets posted by the two most voted candidates during the official campaign for the 2017 Ecuadorian Presidential election were used for this research. For each tweet, five features including type of tweet, emotion, URL, hashtag, and date are identified and coded to predict if tweets are of either high or low impact. The main contributions of the new proposed model include its suitability to analyse tweet datasets based on likelihood analysis of data. The model is interpretable, and the prediction process relies only on the use of available data. The experimental results show that MAKER-RIMER performed better, in terms of misclassification error, when compared against other predictive machine learning approaches. In addition, the model allows observing which features of the candidates' tweets are linked to high and low impact. Tweets containing allusions to the contender candidate, either with positive or negative connotations, without hashtags, and written towards the end of the campaign, were persistently those with the highest impact. URLs, on the other hand, is the only variable that performs differently for the two candidates in terms of achieving high impact. MAKER-RIMER can provide campaigners of political parties or candidates with a tool to measure how features of tweets are predictors of their impact, which can be useful to tailor Twitter content during electoral campaigns.

Keywords: Evidential reasoning rule,Belief rule-based inference,Maximum likelihood data analysis,Twitter,Retweet,Prediction

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Keywords: Combinatorial Black-box Expensive

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Stefan Ropke and David Pisinger. An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problme with Time Windows. Transportation Science, 40(4):455–472, 2006.
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Brian C. Ross. Mutual Information between Discrete and Continuous Data Sets. PLoS One, 9(2):1–5, February 2014.
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Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with “binning” when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen-Shannon divergence of two or more data sets.

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Isaac Rudich, Quentin Cappart, and Louis-Martin Rousseau. Improved Peel-and-Bound: Methods for Generating Dual Bounds with Multivalued Decision Diagrams. Journal of Artificial Intelligence Research, 77:1489–1538, August 2023.
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Ana Belén Ruiz, Rubén Saborido, and Mariano Luque. A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm. Journal of Global Optimization, 62(1):101–129, May 2015.
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When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the region of interest of the Pareto optimal front. In this paper, we suggest a preference-based EMO algorithm called weighting achievement scalarizing function genetic algorithm (WASF-GA), which considers the preferences of the decision maker (DM) expressed by means of a reference point. The main purpose of WASF-GA is to approximate the region of interest of the Pareto optimal front determined by the reference point, which contains the Pareto optimal objective vectors that obey the preferences expressed by the DM in the best possible way. The proposed approach is based on the use of an achievement scalarizing function (ASF) and on the classification of the individuals into several fronts. At each generation of WASF-GA, this classification is done according to the values that each solution takes on the ASF for the reference point and using different weight vectors. These vectors of weights are selected so that the vectors formed by their inverse components constitute a well-distributed representation of the weight vectors space. The efficiency and usefulness of WASF-GA is shown in several test problems in comparison to other preference-based EMO algorithms. Regarding a metric based on the hypervolume, we can say that WASF-GA has outperformed the other algorithms considered in most of the problems.

Proposed WASF-GA

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Rubén Ruiz and Thomas Stützle. A Simple and Effective Iterated Greedy Algorithm for the Permutation Flowshop Scheduling Problem. European Journal of Operational Research, 177(3):2033–2049, 2007.
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Rubén Ruiz and Thomas Stützle. An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. European Journal of Operational Research, 187(3):1143 – 1159, 2008.
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N. R. Sabar, M. Ayob, Graham Kendall, and R. Qu. Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems. IEEE Transactions on Evolutionary Computation, 17(6):840–861, 2013.
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N. R. Sabar, M. Ayob, Graham Kendall, and R. Qu. A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems. IEEE Transactions on Cybernetics, 45(2):217–228, 2015.
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N. R. Sabar, M. Ayob, Graham Kendall, and R. Qu. Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems. IEEE Transactions on Evolutionary Computation, 19(3):309–325, 2015.
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Matthieu Sacher, Régis Duvigneau, Olivier Le Maitre, Mathieu Durand, Elisa Berrini, Frédéric Hauville, and Jacques-André Astolfi. A classification approach to efficient global optimization in presence of non-computable domains. Structural and Multidisciplinary Optimization, 58(4):1537–1557, 2018.
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Proposed EGO-LS-SVM

Keywords: Safe optimization; CMA-ES, Gaussian processes; Least-Squares Support Vector Machine

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Pramod J. Sadalage and Martin Fowler. NoSQL distilled. AddisonWesley Professional, 2012.
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Marcela Samà, Paola Pellegrini, Andrea D'Ariano, Joaquin Rodriguez, and Dario Pacciarelli. Ant colony optimization for the real-time train routing selection problem. Transportation Research Part B: Methodological, 85:89–108, 2016.
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Keywords: irace

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Alejandro Santiago, Bernabé Dorronsoro, Antonio J. Nebro, Juan J. Durillo, Oscar Castillo, and Héctor J. Fraire. A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME. Information Sciences, 471:233–251, 2019.
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Keywords: Multi-objective optimization, density estimation, evolutionary algorithm, adaptive algorithm, fuzzy logic, spatial spread deviation

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Javier Sánchez, Manuel Galán, and Enrique Rubio. Applying a traffic lights evolutionary optimization technique to a real case: “Las Ramblas” area in Santa Cruz de Tenerife. IEEE Transactions on Evolutionary Computation, 12(1):25–40, 2008.
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Keywords: Cellular automata, Combinatorial optimization, Genetic algorithms, Microscopic traffic simulator, Traffic lights optimization

[1173]

J. J. Sánchez-Medina, M. J. Galán-Moreno, and E. Rubio-Royo. Traffic Signal Optimization in “La Almozara” District in Saragossa Under Congestion Conditions, Using Genetic Algorithms, Traffic Microsimulation, and Cluster Computing. IEEE Transactions on Intelligent Transportation Systems, 11(1):132–141, March 2010.
[ bib | DOI ]

Keywords: cellular automata; genetic algorithms; road traffic;traffic light programming;urban traffic congestion

[1174]

Nathan Sankary and Avi Ostfeld. Stochastic Scenario Evaluation in Evolutionary Algorithms Used for Robust Scenario-Based Optimization. Water Resources Research, 54(4):2813–2833, 2018.
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Alberto Santini, Stefan Ropke, and Lars Magnus Hvattum. A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic. Journal of Heuristics, 24:783–815, 2018.
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[1177]

René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, and Marius Thomas Lindauer. DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. Arxiv preprint arXiv:2206.03493 [cs.LG], 2022.
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[1178]

Martin W. P. Savelsbergh. Local search in routing problems with time windows. Annals of Operations Research, 4(1):285–305, December 1985.
[ bib | DOI ]

We develop local search algorithms for routing problems with time windows. The presented algorithms are based on thek-interchange concept. The presence of time windows introduces feasibility constraints, the checking of which normally requires O(N) time. Our method reduces this checking effort to O(1) time. We also consider the problem of finding initial solutions. A complexity result is given and an insertion heuristic is described.

[1179]

Dhish Kumar Saxena, João A. Duro, Anish Tiwari, Kalyanmoy Deb, and Qingfu Zhang. Objective Reduction in Many-Objective Optimization: Linear and Nonlinear Algorithms. IEEE Transactions on Evolutionary Computation, 17(1):77–99, 2013.
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[1180]

Michael Schilde, Karl F. Doerner, Richard F. Hartl, and Guenter Kiechle. Metaheuristics for the bi-objective orienteering problem. Swarm Intelligence, 3(3):179–201, 2009.
[ bib | DOI ]

In this paper, heuristic solution techniques for the multi-objective orienteering problem are developed. The motivation stems from the problem of planning individual tourist routes in a city. Each point of interest in a city provides different benefits for different categories (e.g., culture, shopping). Each tourist has different preferences for the different categories when selecting and visiting the points of interests (e.g., museums, churches). Hence, a multi-objective decision situation arises. To determine all the Pareto optimal solutions, two metaheuristic search techniques are developed and applied. We use the Pareto ant colony optimization algorithm and extend the design of the variable neighborhood search method to the multi-objective case. Both methods are hybridized with path relinking procedures. The performances of the two algorithms are tested on several benchmark instances as well as on real world instances from different Austrian regions and the cities of Vienna and Padua. The computational results show that both implemented methods are well performing algorithms to solve the multi-objective orienteering problem.

[1181]

Martin Schlüter, Jose A. Egea, and Julio R. Banga. Extended ant colony optimization for non-convex mixed integer nonlinear programming. Computers & Operations Research, 36(7):2217–2229, 2009.
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[1182]

Oliver Schütze, X. Esquivel, A. Lara, and Carlos A. Coello Coello. Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 16(4):504–522, 2012.
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[1184]

Mark Schillinger, Benjamin Hartmann, Patric Skalecki, Mona Meister, Duy Nguyen-Tuong, and Oliver Nelles. Safe active learning and safe Bayesian optimization for tuning a PI-controller. IFAC-PapersOnLine, 50(1):5967–5972, 2017.
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[1185]

Julie R. Schames, Richard H. Henchman, Jay S. Siegel, Christoph A. Sotriffer, Haihong Ni, and J. Andrew McCammon. Discovery of a Novel Binding Trench in HIV Integrase. Journal of Medicinal Chemistry, 47(8):1879–1881, 2004.
[ bib | DOI ]

Evolutionary optimization of the first clinically approved anti-viral drug for HIV

[1186]

Oliver Schütze, Carlos Hernández, El-Ghazali Talbi, Jian-Qiao Sun, Yousef Naranjani, and F-R Xiong. Archivers for the representation of the set of approximate solutions for MOPs. Journal of Heuristics, 25:71–105, 2019.
[ bib | DOI ]

Keywords: archiving, nearly optimality, epsilon-dominance, epsilon-approximation, hausdorff convergence

[1187]

Jeffrey C. Schank and Thomas J. Koehnle. Pseudoreplication is a pseudoproblem. Journal of Comparative Psychology, 123(4):421–433, 2009.
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Oliver Schütze, A. Lara, and Carlos A. Coello Coello. On the Influence of the Number of Objectives on the Hardness of a Multiobjective Optimization Problem. IEEE Transactions on Evolutionary Computation, 15(4):444–455, 2011.
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Oliver Schütze, Marco Laumanns, Carlos A. Coello Coello, Michael Dellnitz, and El-Ghazali Talbi. Convergence of stochastic search algorithms to finite size Pareto set approximations. Journal of Global Optimization, 41(4):559–577, 2008.
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Oliver Schütze, Marco Laumanns, Emilia Tantar, Carlos A. Coello Coello, and El-Ghazali Talbi. Computing gap free Pareto front approximations with stochastic search algorithms. Evolutionary Computation, 18(1):65–96, 2010.
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Gerhard Schrimpf, Johannes Schneider, Hermann Stamm-Wilbrandt, and Gunter Dueck. Record Breaking Optimization Results Using the Ruin and Recreate Principle. Journal of Computational Physics, 159(2):139–171, 2000.
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Marie Schmidt, Anita Schöbel, and Lisa Thom. Min-ordering and max-ordering scalarization methods for multi-objective robust optimization. European Journal of Operational Research, 275(2):446–459, 2019.
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Eric Schulz, Maarten Speekenbrink, and Andreas Krause. A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology, 85:1–16, August 2018.
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[1195]

Tommaso Schiavinotto and Thomas Stützle. The Linear Ordering Problem: Instances, Search Space Analysis and Algorithms. Journal of Mathematical Modelling and Algorithms, 3(4):367–402, 2004.
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Tommaso Schiavinotto and Thomas Stützle. A Review of Metrics on Permutations for Search Space Analysis. Computers & Operations Research, 34(10):3143–3153, 2007.
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Tom Schrijvers, Guido Tack, Pieter Wuille, Horst Samulowitz, and Peter J. Stuckey. Search Combinators. Constraints, 18(2):269–305, 2013.
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Oliver Schütze, Massimiliano Vasile, and Carlos A. Coello Coello. Computing the Set of Epsilon-Efficient Solutions in Multiobjective Space Mission Design. Journal of Aerospace Computing, Information, and Communication, 8(3):53–70, 2011.
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Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. A survey of methods for automated algorithm configuration. Journal of Artificial Intelligence Research, 75:425–487, 2022.
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Pauli Virtanen et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17:261–272, 2020.
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[1202]

David F. Shanno. Conditioning of Quasi-Newton Methods for Function Minimization. Mathematics of Computation, 24(111):647–656, 1970.
[ bib ]

One of the four papers that proposed BFGS.

Keywords: BFGS

[1203]

Seyed Mahdi Shavarani, Manuel López-Ibáñez, and Richard Allmendinger. Detecting Hidden and Irrelevant Objectives in Interactive Multi-Objective Optimization. IEEE Transactions on Evolutionary Computation, 28(2):544–557, 2023.
[ bib | DOI ]

Evolutionary multi-objective optimization algorithms (EMOAs) typically assume that all objectives that are relevant to the decision-maker (DM) are optimized by the EMOA. In some scenarios, however, there are irrelevant objectives that are optimized by the EMOA but ignored by the DM, as well as, hidden objectives that the DM considers when judging the utility of solutions but are not optimized. This discrepancy between the EMOA and the DM's preferences may impede the search for the most-preferred solution and waste resources evaluating irrelevant objectives. Research on objective reduction has focused so far on the structure of the problem and correlations between objectives and neglected the role of the DM. We formally define here the concepts of irrelevant and hidden objectives and propose methods for detecting them, based on uni-variate feature selection and recursive feature elimination, that use the preferences already elicited when a DM interacts with a ranking-based interactive EMOA (iEMOA). We incorporate the detection methods into an iEMOA capable of dynamically switching the objectives being optimized. Our experiments show that this approach can efficiently identify which objectives are relevant to the DM and reduce the number of objectives being optimized, while keeping and often improving the utility, according to the DM, of the best solution found.

[1204]

Seyed Mahdi Shavarani, Manuel López-Ibáñez, and Joshua D. Knowles. On Benchmarking Interactive Evolutionary Multi-Objective Algorithms. IEEE Transactions on Evolutionary Computation, 28(4):1084–1098, 2023.
[ bib | DOI ]

We carry out a detailed performance assessment of two interactive evolutionary multi-objective algorithms (EMOAs) using a machine decision maker that enables us to repeat experiments and study specific behaviours modeled after human decision makers (DMs). Using the same set of benchmark test problems as in the original papers on these interactive EMOAs (in up to 10 objectives), we bring to light interesting effects when we use a machine DM based on sigmoidal utility functions that have support from the psychology literature (replacing the simpler utility functions used in the original papers). Our machine DM enables us to go further and simulate human biases and inconsistencies as well. Our results from this study, which is the most comprehensive assessment of multiple interactive EMOAs so far conducted, suggest that current well-known algorithms have shortcomings that need addressing. These results further demonstrate the value of improving the benchmarking of interactive EMOAs

[1205]

Babooshka Shavazipour, Manuel López-Ibáñez, and Kaisa Miettinen. Visualizations for Decision Support in Scenario-based Multiobjective Optimization. Information Sciences, 578:1–21, 2021.
[ bib | DOI | supplementary material ]

We address challenges of decision problems when managers need to optimize several conflicting objectives simultaneously under uncertainty. We propose visualization tools to support the solution of such scenario-based multiobjective optimization problems. Suitable graphical visualizations are necessary to support managers in understanding, evaluating, and comparing the performances of management decisions according to all objectives in all plausible scenarios. To date, no appropriate visualization has been suggested. This paper fills this gap by proposing two visualization methods: a novel extension of empirical attainment functions for scenarios and an adapted version of heatmaps. They help a decision-maker in gaining insight into realizations of trade-offs and comparisons between objective functions in different scenarios. Some fundamental questions that a decision-maker may wish to answer with the help of visualizations are also identified. Several examples are utilized to illustrate how the proposed visualizations support a decision-maker in evaluating and comparing solutions to be able to make a robust decision by answering the questions. Finally, we validate the usefulness of the proposed visualizations in a real-world problem with a real decision-maker. We conclude with guidelines regarding which of the proposed visualizations are best suited for different problem classes.

[1206]

Weishi Shao, Dechang Pi, and Zhongshi Shao. Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion. Applied Soft Computing, 54:164–182, 2017.
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[1207]

Weishi Shao, Dechang Pi, and Zhongshi Shao. A hybrid discrete teaching-learning based meta-heuristic for solving no-idle flow shop scheduling problem with total tardiness criterion. Computers & Operations Research, 94:89–105, 2018.
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[1208]

Ke Shang, Tianye Shu, Hisao Ishibuchi, Yang Nan, and Lie Meng Pang. Benchmarking large-scale subset selection in evolutionary multi-objective optimization. Information Sciences, 622:755–770, 2023.
[ bib | DOI ]

[1209]

Babooshka Shavazipour and T. J. Stewart. Multi-objective optimisation under deep uncertainty. Operational Research, September 2019.
[ bib | DOI ]

This paper presents a scenario-based Multi-Objective structure to handle decision problems under deep uncertainty. Most of the decisions in real-life problems need to be made in the absence of complete knowledge about the consequences of the decision and/or are characterised by uncertainties about the future which is unpredictable. These uncertainties are almost impossible to reduce by gathering more information and are not statistical in nature. Therefore, classical probability-based approaches, such as stochastic programming, do not address these problems; as they require a correctly-defined complete sample space, strong assumptions (e.g. normality), or both. The proposed method extends the concept of two-stage stochastic programming with recourse to address the capability of dealing with deep uncertainty through the use of scenario planning rather than statistical expectation. In this research, scenarios are used as a dimension of preference to avoid problems relating to the assessment and use of probabilities under deep uncertainty. Such scenario-based thinking involved a multi-objective representation of performance under different future conditions as an alternative to expectation. To the best of our knowledge, this is the first attempt of performing a multi-criteria evaluation under deep uncertainty through a structured optimisation model. The proposed structure replacing probabilities (in dynamic systems with deep uncertainties) by aspirations within a goal programming structure. In fact, this paper also proposes an extension of the goal programming paradigm to deal with deep uncertainty. Furthermore, we will explain how this structure can be modelled, implemented, and solved by Goal Programming using some simple, but not trivial, examples. Further discussion and comparisons with some popular existing methods will also provided to highlight the superiorities of the proposed structure.

[1210]

Babooshka Shavazipour, Jonas Stray, and T. J. Stewart. Sustainable planning in sugar-bioethanol supply chain under deep uncertainty: A case study of South African sugarcane industry. Computers & Chemical Engineering, 143:107091, 2020.
[ bib | DOI ]

In this paper, the strategic planning of sugar-bioethanol supply chains (SCs) under deep uncertainty has been addressed by applying a two-stage scenario-based multiobjective optimisation methodology. In practice, the depth of uncertainty is very high, potential outcomes are not precisely enumerable, and probabilities of outcomes are not properly definable. To date, no appropriate framework has been suggested for dealing with deep uncertainty in supply chain management and energy-related problems. This study is the first try to fills this gap. Particularly, the sustainability of the whole infrastructure of the sugar-bioethanol SCs is analysed in such a way that the final solutions are sustainable, robust and adaptable for a broad range of plausible futures. Three objectives are considered in this problem under six uncertain parameters. A case study of South African sugarcane industry is utilised to study and examine the proposed model. The results prove the economic profitability and sustainability of the project.

Keywords: Supply chain management, Multi-objective optimisation, Deep uncertainty, Scenario planning, Renewable energy,

[1211]

B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and Nando de Freitas. Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104(1):148–175, 2016.
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[1212]

Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE, 104(1):148–175, 2016.
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[1213]

Ofer M. Shir and Thomas Bäck. Niching with derandomized evolution strategies in artificial and real-world landscapes. Natural Computing, 8(1):171–196, 2009.
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[1214]

Abolfazl Shirazi, Josu Ceberio, and José A. Lozano. Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions. Progress in Aerospace Sciences, 102:76–98, October 2018.
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[1215]

David Shilane, Jarno Martikainen, Sandrine Dudoit, and Seppo J. Ovaska. A general framework for statistical performance comparison of evolutionary computation algorithms. Information Sciences, 178(14):2870–2879, 2008.
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[1216]

Michael D. Shields and Jiaxin Zhang. The generalization of Latin hypercube sampling. Reliability Engineering & System Safety, 148:96–108, 2016.
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[1217]

A. Shmygelska and Holger H. Hoos. An Ant Colony Optimisation Algorithm for the 2D and 3D Hydrophobic Polar Protein Folding Problem. BMC Bioinformatics, 6:30, 2005.
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[1218]

Moisés Silva-Muñoz, Carlos Contreras-Bolton, Carlos Rey, and Victor Parada. Automatic generation of a hybrid algorithm for the maximum independent set problem using genetic programming. Applied Soft Computing, p.  110474, 2023.
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[1219]

Moisés Silva-Muñoz, Alberto Franzin, and Hughes Bersini. Automatic configuration of the Cassandra database using irace. PeerJ Computer Science, 7:e634, 2021.
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[1220]

Paulo Vitor Silvestrin and Marcus Ritt. An Iterated Tabu Search for the Multi-compartment Vehicle Routing Problem. Computers & Operations Research, 81:192–202, 2017.
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[1221]

Marcos Melo Silva, Anand Subramanian, and Luiz Satoru Ochi. An Iterated Local Search Heuristic for the Split Delivery Vehicle Routing Problem. Computers & Operations Research, 53:234–249, 2015.
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[1222]

Olivier Simonin, François Charpillet, and Eric Thierry. Revisiting wavefront construction with collective agents: an approach to foraging. Swarm Intelligence, 9(2):113–138, 2014.
[ bib | DOI ]

Keywords: irace

[1223]

Kevin Sim, Emma Hart, and Ben Paechter. A Lifelong Learning Hyper-heuristic Method for Bin Packing. Evolutionary Computation, 23(1):37–67, 2015.
[ bib | DOI ]

[1224]

Joseph P. Simmons, Leif D. Nelson, and Uri Simonsohn. False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 2011.
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Proposed the term p-hacking

[1225]

Herbert A. Simon and Allen Newell. Heuristic Problem Solving: The Next Advance in Operations Research. Operations Research, 6(1):1–10, 1958.
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[1226]

Joseph P. Simmons, Robyn A. LeBoeuf, and Leif D. Nelson. The effect of accuracy motivation on anchoring and adjustment: Do people adjust from provided anchors? Journal of Personality and Social Psychology, 99(6):917–932, 2010.
[ bib | DOI ]

Increasing accuracy motivation (e.g., by providing monetary incentives for accuracy) often fails to increase adjustment away from provided anchors, a result that has led researchers to conclude that people do not effortfully adjust away from such anchors. We challenge this conclusion. First, we show that people are typically uncertain about which way to adjust from provided anchors and that this uncertainty often causes people to believe that they have initially adjusted too far away from such anchors (Studies 1a and 1b). Then, we show that although accuracy motivation fails to increase the gap between anchors and final estimates when people are uncertain about the direction of adjustment, accuracy motivation does increase anchor-estimate gaps when people are certain about the direction of adjustment, and that this is true regardless of whether the anchors are provided or self-generated (Studies 2, 3a, 3b, and 5). These results suggest that people do effortfully adjust away from provided anchors but that uncertainty about the direction of adjustment makes that adjustment harder to detect than previously assumed. This conclusion has important theoretical implications, suggesting that currently emphasized distinctions between anchor types (self-generated vs. provided) are not fundamental and that ostensibly competing theories of anchoring (selective accessibility and anchoring-and-adjustment) are complementary.

[1227]

Herbert A. Simon. A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1):99–118, 1955.
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[1228]

Hemant Kumar Singh, Amitay Isaacs, and Tapabrata Ray. A Pareto Corner Search Evolutionary Algorithm and Dimensionality Reduction in Many-Objective Optimization Problems. IEEE Transactions on Evolutionary Computation, 15(4):539–556, 2011.
[ bib | DOI ]

Many-objective optimization refers to the optimization problems containing large number of objectives, typically more than four. Non-dominance is an inadequate strategy for convergence to the Pareto front for such problems, as almost all solutions in the population become non-dominated, resulting in loss of convergence pressure. However, for some problems, it may be possible to generate the Pareto front using only a few of the objectives, rendering the rest of the objectives redundant. Such problems may be reducible to a manageable number of relevant objectives, which can be optimized using conventional multiobjective evolutionary algorithms (MOEAs). For dimensionality reduction, most proposals in the paper rely on analysis of a representative set of solutions obtained by running a conventional MOEA for a large number of generations, which is computationally overbearing. A novel algorithm, Pareto corner search evolutionary algorithm (PCSEA), is introduced in this paper, which searches for the corners of the Pareto front instead of searching for the complete Pareto front. The solutions obtained using PCSEA are then used for dimensionality reduction to identify the relevant objectives. The potential of the proposed approach is demonstrated by studying its performance on a set of benchmark test problems and two engineering examples. While the preliminary results obtained using PCSEA are promising, there are a number of areas that need further investigation. This paper provides a number of useful insights into dimensionality reduction and, in particular, highlights some of the roadblocks that need to be cleared for future development of algorithms attempting to use few selected solutions for identifying relevant objectives

[1229]

Marcos Singer and Michael L. Pinedo. A Computational Study of Branch and Bound Techniques for Minimizing the Total Weighted Tardiness in Job Shops. IIE Transactions, 30(2):109–118, 1998.
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[1230]

Ankur Sinha, Dhish Kumar Saxena, Kalyanmoy Deb, and Ashutosh Tiwari. Using objective reduction and interactive procedure to handle many-objective optimization problems. Applied Soft Computing, 13(1):415–427, 2013.
[ bib | DOI ]

A number of practical optimization problems are posed as many-objective (more than three objectives) problems. Most of the existing evolutionary multi-objective optimization algorithms, which target the entire Pareto-front are not equipped to handle many-objective problems. Though there have been copious efforts to overcome the challenges posed by such problems, there does not exist a generic procedure to effectively handle them. This paper presents a simplify and solve framework for handling many-objective optimization problems. In that, a given problem is simplified by identification and elimination of the redundant objectives, before interactively engaging the decision maker to converge to the most preferred solution on the Pareto-optimal front. The merit of performing objective reduction before interacting with the decision maker is two fold. Firstly, the revelation that certain objectives are redundant, significantly reduces the complexity of the optimization problem, implying lower computational cost and higher search efficiency. Secondly, it is well known that human beings are not efficient in handling several factors (objectives in the current context) at a time. Hence, simplifying the problem a priori addresses the fundamental issue of cognitive overload for the decision maker, which may help avoid inconsistent preferences during the different stages of interactive engagement. The implementation of the proposed framework is first demonstrated on a three-objective problem, followed by its application on two real-world engineering problems.

Keywords: Evolutionary algorithms, Evolutionary multi- and many-objective optimization, Multi-criteria decision making, Machine learning, Interactive optimization

[1231]

Hemant Kumar Singh, Kalyan Shankar Bhattacharjee, and Tapabrata Ray. Distance-based subset selection for benchmarking in evolutionary multi/many-objective optimization. IEEE Transactions on Evolutionary Computation, 23(5):904–912, 2019.
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[1232]

Aymen Sioud and Caroline Gagné. Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times. European Journal of Operational Research, 264(1):66–73, 2018.
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[1233]

Ben G. Small, Barry W. McColl, Richard Allmendinger, Jürgen Pahle, Gloria López-Castejón, Nancy J. Rothwell, Joshua D. Knowles, Pedro Mendes, David Brough, and Douglas B. Kell. Efficient discovery of anti-inflammatory small-molecule combinations using evolutionary computing. Nature Chemical Biology, 7(12):902–908, 2011.
[ bib ]

[1234]

Kate Smith-Miles, Davaatseren Baatar, Brendan Wreford, and Rhyd M. R. Lewis. Towards Objective Measures of Algorithm Performance across Instance Space. Computers & Operations Research, 45:12–24, 2014.
[ bib | DOI ]

This paper tackles the difficult but important task of objective algorithm performance assessment for optimization. Rather than reporting average performance of algorithms across a set of chosen instances, which may bias conclusions, we propose a methodology to enable the strengths and weaknesses of different optimization algorithms to be compared across a broader instance space. The results reported in a recent Computers and Operations Research paper comparing the performance of graph coloring heuristics are revisited with this new methodology to demonstrate (i) how pockets of the instance space can be found where algorithm performance varies significantly from the average performance of an algorithm; (ii) how the properties of the instances can be used to predict algorithm performance on previously unseen instances with high accuracy; and (iii) how the relative strengths and weaknesses of each algorithm can be visualized and measured objectively.

Keywords: Algorithm selection; Instance Space Analysis; Graph coloring; Heuristics; Performance prediction

[1235]

Kate Smith-Miles and Simon Bowly. Generating New Test Instances by Evolving in Instance Space. Computers & Operations Research, 63:102–113, 2015.
[ bib | DOI ]

Our confidence in the future performance of any algorithm, including optimization algorithms, depends on how carefully we select test instances so that the generalization of algorithm performance on future instances can be inferred. In recent work, we have established a methodology to generate a 2-d representation of the instance space, comprising a set of known test instances. This instance space shows the similarities and differences between the instances using measurable features or properties, and enables the performance of algorithms to be viewed across the instance space, where generalizations can be inferred. The power of this methodology is the insights that can be generated into algorithm strengths and weaknesses by examining the regions in instance space where strong performance can be expected. The representation of the instance space is dependent on the choice of test instances however. In this paper we present a methodology for generating new test instances with controllable properties, by filling observed gaps in the instance space. This enables the generation of rich new sets of test instances to support better the understanding of algorithm strengths and weaknesses. The methodology is demonstrated on graph colouring as a case study.

Keywords: Benchmarking; Evolving instances; Graph colouring; Instance space; Test instances

[1236]

Kate Smith-Miles, Jeffrey Christiansen, and Mario A. Muñoz. Revisiting Where Are the Hard Knapsack Problems? Via Instance Space Analysis. Computers & Operations Research, 128:105184, 2021.
[ bib | DOI ]

Keywords: 0-1 Knapsack problem; Algorithm portfolios; Algorithm selection; Instance difficulty; Instance generation; Instance Space Analysis; Performance evaluation

[1237]

Kate Smith-Miles and Leo Lopes. Measuring instance difficulty for combinatorial optimization problems. Computers & Operations Research, 39:875–889, 2012.
[ bib ]

[1238]

Kate Smith-Miles and Mario A. Muñoz. Instance Space Analysis for Algorithm Testing: Methodology and Software Tools. ACM Computing Surveys, 55(12), March 2023.
[ bib | DOI ]

Instance Space Analysis (ISA) is a recently developed methodology to (a) support objective testing of algorithms and (b) assess the diversity of test instances. Representing test instances as feature vectors, the ISA methodology extends Rice's 1976 Algorithm Selection Problem framework to enable visualization of the entire space of possible test instances, and gain insights into how algorithm performance is affected by instance properties. Rather than reporting algorithm performance on average across a chosen set of test problems, as is standard practice, the ISA methodology offers a more nuanced understanding of the unique strengths and weaknesses of algorithms across different regions of the instance space that may otherwise be hidden on average. It also facilitates objective assessment of any bias in the chosen test instances and provides guidance about the adequacy of benchmark test suites. This article is a comprehensive tutorial on the ISA methodology that has been evolving over several years, and includes details of all algorithms and software tools that are enabling its worldwide adoption in many disciplines. A case study comparing algorithms for university timetabling is presented to illustrate the methodology and tools.

Keywords: test instance diversity, benchmarking, timetabling, Algorithm footprints, MATLAB, software as a service, meta-heuristics, algorithm selection, meta-learning

[1239]

Kate Smith-Miles. Cross-disciplinary Perspectives on Meta-learning for Algorithm Selection. ACM Computing Surveys, 41(1):1–25, 2008.
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[1240]

Krzysztof Socha and Christian Blum. An ant colony optimization algorithm for continuous optimization: An application to feed-forward neural network training. Neural Computing & Applications, 16(3):235–247, 2007.
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[1241]

Krzysztof Socha and Marco Dorigo. Ant Colony Optimization for Continuous Domains. European Journal of Operational Research, 185(3):1155–1173, 2008.
[ bib | DOI ]

Proposed ACOR (ACOR)

Keywords: ACOR

[1242]

Christine Solnon. Ants Can Solve Constraint Satisfaction Problems. IEEE Transactions on Evolutionary Computation, 6(4):347–357, 2002.
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Zhenshou Song, Handing Wang, Cheng He, and Yaochu Jin. A Kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization. IEEE Transactions on Evolutionary Computation, 25(6):1013–1027, 2021.
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Kenneth Sörensen. Metaheuristics—the metaphor exposed. International Transactions in Operational Research, 22(1):3–18, 2015.
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Kenneth Sörensen, Florian Arnold, and Daniel Palhazi Cuervo. A critical analysis of the “improved Clarke and Wright savings algorithm”. International Transactions in Operational Research, 26(1):54–63, 2017.
[ bib | DOI ]

Keywords: reproducibility, vehicle routing

[1248]

Jorge A. Soria-Alcaraz, Gabriela Ochoa, Marco A. Sotelo-Figeroa, and Edmund K. Burke. A Methodology for Determining an Effective Subset of Heuristics in Selection Hyper-heuristics. European Journal of Operational Research, 260:972–983, 2017.
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Marcelo De Souza, Marcus Ritt, and Manuel López-Ibáñez. Capping Methods for the Automatic Configuration of Optimization Algorithms. Computers & Operations Research, 139:105615, 2022.
[ bib | DOI | supplementary material ]

Automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. Configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the running time of the algorithm, many configurators implement capping methods to discard poor configurations early. Such methods are not directly applicable to optimization problems, when the objective is to optimize the cost of the best solution found, given a predefined running time limit. We propose new capping methods for the automatic configuration of optimization algorithms. They use the previous executions to determine a performance envelope, which is used to evaluate new executions and cap those that do not satisfy the envelope conditions. We integrate the capping methods into the irace configurator and evaluate them on different optimization scenarios. Our results show that the proposed methods can save from about 5% to 78% of the configuration effort, while finding configurations of the same quality. Based on the computational analysis, we identify two conservative and two aggressive methods, that save an average of about 20% and 45% of the configuration effort, respectively. We also provide evidence that capping can help to better use the available budget in scenarios with a configuration time limit.

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Daniel A. Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. Journal of the ACM, 51(3):385–463, 2004.
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Keywords: branch-and-bound, multi-mode resource-constrained project scheduling, project scheduling

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Arno Sprecher, Rainer Kolisch, and Andreas Drexl. Semi-active, active, and non-delay schedules for the resource-constrained project scheduling problem. European Journal of Operational Research, 80(1):94–102, 1995.
[ bib | DOI ]

We consider the resource-constrained project scheduling problem (RCPSP). The focus of the paper is on a formal definition of semi-active, active, and non-delay schedules. Traditionally these schedules establish basic concepts within the job shop scheduling literature. There they are usually defined in a rather informal way which does not create any substantial problems. Using these concepts in the more general RCPSP without giving a formal definition may cause serious problems. After providing a formal definition of semi-active, active, and non-delay schedules for the RCPSP we outline some of these problems occurring within the disjunctive arc concept.

Keywords: active schedules, Branch-and-bound methods, non-delay schedules, Resource-constrained project scheduling, Semi-active schedules

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Keywords: machine decision-making

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T. J. Stewart. Goal programming and cognitive biases in decision-making. Journal of the Operational Research Society, 56(10):1166–1175, 2005.
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Keywords: machine decision making

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T. J. Stewart, Simon French, and Jesus Rios. Integrating multicriteria decision analysis and scenario planning: Review and extension. Omega, 41(4):679–688, 2013.
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Keywords: Multicriteria decision analysis

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Helena Stegherr, Michael Heider, and Jörg Hähner. Classifying Metaheuristics: Towards a unified multi-level classification system. Natural Computing, 2020.
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Sarah Steiner and Tomasz Radzik. Computing all efficient solutions of the biobjective minimum spanning tree problem. Computers & Operations Research, 35(1):198–211, 2008.
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Daniel H. Stolfi and Enrique Alba. Red Swarm: Reducing travel times in smart cities by using bio-inspired algorithms. Applied Soft Computing, 24:181–195, 2014.
[ bib | DOI ]

This article presents an innovative approach to solve one of the most relevant problems related to smart mobility: the reduction of vehicles' travel time. Our original approach, called Red Swarm, suggests a potentially customized route to each vehicle by using several spots located at traffic lights in order to avoid traffic jams by using {V2I} communications. That is quite different from other existing proposals, as it deals with real maps and actual streets, as well as several road traffic distributions. We propose an evolutionary algorithm (later efficiently parallelized) to optimize our case studies which have been imported from OpenStreetMap into {SUMO} as they belong to a real city. We have also developed a Rerouting Algorithm which accesses the configuration of the Red Swarm and communicates the route chosen to vehicles, using the spots (via WiFi link). Moreover, we have developed three competing algorithms in order to compare their results to those of Red Swarm and have observed that Red Swarm not only achieved the best results, but also outperformed the experts' solutions in a total of 60 scenarios tested, with up to 19% shorter travel times.

Keywords: Evolutionary algorithm,Road traffic,Smart city,Smart mobility,Traffic light,WiFi connections

[1265]

Victoria Stodden, Marcia McNutt, David H. Bailey, Ewa Deelman, Yolanda Gil, Brooks Hanson, Michael A. Heroux, John P. A. Ioannidis, and Michela Taufer. Enhancing reproducibility for computational methods. Science, 354(6317):1240–1241, December 2016.
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Rainer Storn and Kenneth Price. Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4):341–359, 1997.
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Proposed differential evolution

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Victoria Stodden, Jennifer Seiler, and Zhaokun Ma. An empirical analysis of journal policy effectiveness for computational reproducibility. Proceedings of the National Academy of Sciences, 115(11):2584–2589, March 2018.
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Philip N. Strenski and Scott Kirkpatrick. Analysis of Finite Length Annealing Schedules. Algorithmica, 6(1-6):346–366, 1991.
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Patrycja Strycharczuk, Manuel López-Ibáñez, Georgina Brown, and Adrian Leemann. General Northern English: Exploring regional variation in the North of England with machine learning. Frontiers in Artificial Intelligence, 3(48), 2020.
[ bib | DOI ]

In this paper, we present a novel computational approach to the analysis of accent variation. The case study is dialect leveling in the North of England, manifested as reduction of accent variation across the North and emergence of General Northern English (GNE), a pan-regional standard accent associated with middle-class speakers. We investigated this instance of dialect leveling using random forest classification, with audio data from a crowd-sourced corpus of 105 urban, mostly highly-educated speakers from five northern UK cities: Leeds, Liverpool, Manchester, Newcastle upon Tyne, and Sheffield. We trained random forest models to identify individual northern cities from a sample of other northern accents, based on first two formant measurements of full vowel systems. We tested the models using unseen data. We relied on undersampling, bagging (bootstrap aggregation) and leave-one-out cross-validation to address some challenges associated with the data set, such as unbalanced data and relatively small sample size. The accuracy of classification provides us with a measure of relative similarity between different pairs of cities, while calculating conditional feature importance allows us to identify which input features (which vowels and which formants) have the largest influence in the prediction. We do find a considerable degree of leveling, especially between Manchester, Leeds and Sheffield, although some differences persist. The features that contribute to these differences most systematically are typically not the ones discussed in previous dialect descriptions. We propose that the most systematic regional features are also not salient, and as such, they serve as sociolinguistic regional indicators. We supplement the random forest results with a more traditional variationist description of by-city vowel systems, and we use both sources of evidence to inform a description of the vowels of General Northern English.

Keywords: vowels, accent features, dialect leveling, Random forest (bagging), Feature selecion

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Thomas Stützle. Iterated Local Search for the Quadratic Assignment Problem. European Journal of Operational Research, 174(3):1519–1539, 2006.
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Thomas Stützle and Marco Dorigo. A Short Convergence Proof for a Class of ACO Algorithms. IEEE Transactions on Evolutionary Computation, 6(4):358–365, 2002.
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Thomas Stützle and Holger H. Hoos. Max-Min Ant System. Future Generation Computer Systems, 16(8):889–914, 2000.
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Zhaopin Su, Guofu Zhang, Feng Yue, Dezhi Zhan, Miqing Li, Bin Li, and Xin Yao. Enhanced Constraint Handling for Reliability-Constrained Multiobjective Testing Resource Allocation. IEEE Transactions on Evolutionary Computation, 25(3):537–551, 2021.
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Anand Subramanian and Maria Battarra. An Iterated Local Search Algorithm for the Travelling Salesman Problem with Pickups and Deliveries. Journal of the Operational Research Society, 64(3):402–409, 2013.
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Anand Subramanian, Maria Battarra, and Chris N. Potts. An Iterated Local Search Heuristic for the Single Machine Total Weighted Tardiness Scheduling Problem with Sequence-dependent Setup Times. International Journal of Production Research, 52(9):2729–2742, 2014.
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Yanan Sui, Vincent Zhuang, Joel W. Burdick, and Yisong Yue. Stagewise Safe Bayesian Optimization with Gaussian Processes. Arxiv preprint arXiv:1806.07555, 2018. Published as [2608].
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Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative of the optimal decision and also safe. For example, we value both efficacy and comfort in medical therapy, and efficiency and safety in robotic control. We consider this problem of optimizing an unknown utility function with absolute feedback or preference feedback subject to unknown safety constraints. We develop an efficient safe Bayesian optimization algorithm, StageOpt, that separates safe region expansion and utility function maximization into two distinct stages. Compared to existing approaches which interleave between expansion and optimization, we show that StageOpt is more efficient and naturally applicable to a broader class of problems. We provide theoretical guarantees for both the satisfaction of safety constraints as well as convergence to the optimal utility value. We evaluate StageOpt on both a variety of synthetic experiments, as well as in clinical practice. We demonstrate that StageOpt is more effective than existing safe optimization approaches, and is able to safely and effectively optimize spinal cord stimulation therapy in our clinical experiments.

Keywords: Safe Optimization, StageOpt

[1277]

Yanan Sun, Gary G. Yen, and Zhang Yi. IGD Indicator-based Evolutionary Algorithm for Many-objective Optimization Problems. IEEE Transactions on Evolutionary Computation, 23(2):173–187, 2019.
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A. Suppapitnarm, K. A. Seffen, G. T. Parks, and P. J. Clarkson. A simulated annealing algorithm for multiobjective optimization. Engineering Optimization, 33(1):59–85, 2000.
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Johan A. K. Suykens and Joos Vandewalle. Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 9(3):293–300, 1999.
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Keywords: LS-SVM

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Jerry Swan, Steven Adriaensen, Adam D. Barwell, Kevin Hammond, and David R. White. Extending the “Open-Closed Principle” to Automated Algorithm Configuration. Evolutionary Computation, 27(1):173–193, 2019.
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Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee, Kevin Hammond, Colin G. Johnson, Ahmed Kheiri, Faustyna Krawiec, Juan-Julián Merelo, Leandro L. Minku, Ender Özcan, Gisele Pappa, Pablo García-Sánchez, Kenneth Sörensen, Stefan Voß, Markus Wagner, and David R. White. Metaheuristics “In the Large”. European Journal of Operational Research, 297(2):393–406, March 2022.
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Jerry Swan, John R. Woodward, Ender Özcan, Graham Kendall, and Edmund K. Burke. Searching the Hyper-heuristic Design Space. Cognitive Computation, 6(1):66–73, March 2014.
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Harold Szu and Ralph Hartley. Fast Simulated Annealing. Physics Letters A, 122(3):157–162, 1987.
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El-Ghazali Talbi. A Taxonomy of Hybrid Metaheuristics. Journal of Heuristics, 8(5):541–564, 2002.
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Kar Yan Tam. A Simulated Annealing Algorithm for Allocating Space to Manufacturing Cells. International Journal of Production Research, 30(1):63–87, 1992.
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This paper presents a review of the current literature on the branch of multi-criteria decision modelling known as Goal Programming (GP). The result of our indepth investigations of the two main GP methods, lexicographic and weighted GP together with their distinct application areas is reported. Some guidelines to the scope of GP as an application tool are given and methods of determining which problem areas are best suited to the different GP approaches are proposed. The correlation between the method of assigning weights and priorities and the standard of the results is also ascertained.

Keywords: Goal Programming, lexicographic, weighted

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Shunji Tanaka and Mituhiko Araki. An Exact Algorithm for the Single-machine Total Weighted Tardiness Problem with Sequence-dependent Setup Times. Computers & Operations Research, 40(1):344–352, 2013.
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Ryoji Tanabe and Hisao Ishibuchi. An easy-to-use real-world multi-objective optimization problem suite. Applied Soft Computing, 89:106078, 2020.
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Proposed the RE benchmark suite

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Ryoji Tanabe, Hisao Ishibuchi, and Akira Oyama. Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios. IEEE Access, 5:19597–19619, 2017.
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compared a number of MOEAs using a wide range of numbers of objectives and stopping criteria, with and without archivers; unbounded archive

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Lixin Tang and Xianpeng Wang. Iterated local search algorithm based on very large-scale neighborhood for prize-collecting vehicle routing problem. International Journal of Advanced Manufacturing Technology, 29(11):1246–1258, 2006.
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M. Fatih Tasgetiren, Yun-Chia Liang, Mehmet Sevkli, and Gunes Gencyilmaz. A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European Journal of Operational Research, 177(3):1930–1947, 2007.
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M. Fatih Tasgetiren, Quan-Ke Pan, Ponnuthurai N. Suganthan, and Ozge Buyukdagli. A variable iterated greedy algorithm with differential evolution for the no-idle permutation flowshop scheduling problem. Computers & Operations Research, 40(7):1729–1743, 2013.
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Joc Cing Tay and Nhu Binh Ho. Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers and Industrial Engineering, 54(3):453 – 473, 2008.
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Cristina Teixeira, José Covas, Thomas Stützle, and António Gaspar-Cunha. Engineering an Efficient Two-Phase Local Search for the Co-Rotating Twin-Screw Configuration Problem. International Transactions in Operational Research, 18(2):271–291, 2011.
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Cristina Teixeira, José Covas, Thomas Stützle, and António Gaspar-Cunha. Multi-Objective Ant Colony Optimization for Solving the Twin-Screw Extrusion Configuration Problem. Engineering Optimization, 44(3):351–371, 2012.
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Cristina Teixeira, José Covas, Thomas Stützle, and António Gaspar-Cunha. Hybrid Algorithms for the Twin-Screw Extrusion Configuration Problem. Applied Soft Computing, 23:298–307, 2014.
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Fitsum Teklu, Agachai Sumalee, and David Watling. A Genetic Algorithm Approach for Optimizing Traffic Control Signals Considering Routing. Computer-Aided Civil and Infrastructure Engineering, 22(1):31–43, January 2007.
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J. Teo and Hussein A. Abbass. Automatic generation of controllers for embodied legged organisms: A Pareto evolutionary multi-objective approach. Evolutionary Computation, 12(3):355–394, 2004.
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Kei Terayama, Masato Sumita, Ryo Tamura, and Koji Tsuda. Black-Box Optimization for Automated Discovery. Accounts of Chemical Research, 54(6):1334–1346, March 2021.
[ bib | DOI ]

In chemistry and materials science, researchers and engineers discover, design, and optimize chemical compounds or materials with their professional knowledge and techniques. At the highest level of abstraction, this process is formulated as black-box optimization. For instance, the trial-and-error process of synthesizing various molecules for better material properties can be regarded as optimizing a black-box function describing the relation between a chemical formula and its properties. Various black-box optimization algorithms have been developed in the machine learning and statistics communities. Recently, a number of researchers have reported successful applications of such algorithms to chemistry. They include the design of photofunctional molecules and medical drugs, optimization of thermal emission materials and high Li-ion conductive solid electrolytes, and discovery of a new phase in inorganic thin films for solar cells.There are a wide variety of algorithms available for black-box optimization, such as Bayesian optimization, reinforcement learning, and active learning. Practitioners need to select an appropriate algorithm or, in some cases, develop novel algorithms to meet their demands. It is also necessary to determine how to best combine machine learning techniques with quantum mechanics- and molecular mechanics-based simulations, and experiments. In this Account, we give an overview of recent studies regarding automated discovery, design, and optimization based on black-box optimization. The Account covers the following algorithms: Bayesian optimization to optimize the chemical or physical properties, an optimization method using a quantum annealer, best-arm identification, gray-box optimization, and reinforcement learning. In addition, we introduce active learning and boundless objective-free exploration, which may not fall into the category of black-box optimization.Data quality and quantity are key for the success of these automated discovery techniques. As laboratory automation and robotics are put forward, automated discovery algorithms would be able to match human performance at least in some domains in the near future.

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Patrick Thibodeau. Machine-based decision-making is coming. Computer World, November 2011. Last accessed: 15 January 2014.
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Lothar Thiele, Kaisa Miettinen, Pekka Korhonen, and Julián Molina. A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization. Evolutionary Computation, 17(3):411–436, 2009.
[ bib | DOI ]

Abstract In this paper, we discuss the idea of incorporating preference information into evolutionary multi-objective optimization and propose a preference-based evolutionary approach that can be used as an integral part of an interactive algorithm. One algorithm is proposed in the paper. At each iteration, the decision maker is asked to give preference information in terms of his or her reference point consisting of desirable aspiration levels for objective functions. The information is used in an evolutionary algorithm to generate a new population by combining the fitness function and an achievement scalarizing function. In multi-objective optimization, achievement scalarizing functions are widely used to project a given reference point into the Pareto optimal set. In our approach, the next population is thus more concentrated in the area where more preferred alternatives are assumed to lie and the whole Pareto optimal set does not have to be generated with equal accuracy. The approach is demonstrated by numerical examples.

[1309]

Ye Tian, Ran Cheng, Xingyi Zhang, Fan Cheng, and Yaochu Jin. An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility. IEEE Transactions on Evolutionary Computation, 22(4):609–622, 2018.
[ bib | DOI ]

IGD-based archiver

[1310]

Tiew-On Ting, M. V. C. Rao, C. K. Loo, and S. S. Ngu. Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization. Journal of Heuristics, 9(6):507–520, 2003.
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[1311]

Santosh Tiwari, Georges Fadel, and Kalyanmoy Deb. AMGA2: Improving the performance of the archive-based micro-genetic algorithm for multi-objective optimization. Engineering Optimization, 43(4):377–401, 2011.
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V. T'Kindt, Nicolas Monmarché, F. Tercinet, and D. Laügt. An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem. European Journal of Operational Research, 142(2):250–257, 2002.
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[1313]

Michal K Tomczyk and Milosz Kadziński. Decomposition-based interactive evolutionary algorithm for multiple objective optimization. IEEE Transactions on Evolutionary Computation, 24(2):320–334, 2019.
[ bib | DOI ]

We propose a decomposition-based interactive evolutionary algorithm (EA) for multiple objective optimization. During an evolutionary search, a decision maker (DM) is asked to compare pairwise solutions from the current population. Using the Monte Carlo simulation, the proposed algorithm generates from a uniform distribution a set of instances of the preference model compatible with such an indirect preference information. These instances are incorporated as the search directions with the aim of systematically converging a population toward the DMs most preferred region of the Pareto front. The experimental comparison proves that the proposed decomposition-based method outperforms the state-of-the-art interactive counterparts of the dominance-based EAs. We also show that the quality of constructed solutions is highly affected by the form of the incorporated preference model.

Keywords: interactive multi-objective; decision-making

[1314]

Michal K Tomczyk and Milosz Kadziński. EMOSOR: Evolutionary multiple objective optimization guided by interactive stochastic ordinal regression. Computers & Operations Research, 108:134–154, 2019.
[ bib | DOI ]

We propose a family of algorithms, called EMOSOR, combining Evolutionary Multiple Objective Optimization with Stochastic Ordinal Regression. The proposed methods ask the Decision Maker (DM) to holistically compare, at regular intervals, a pair of solutions, and use the Monte Carlo simulation to construct a set of preference model instances compatible with such indirect and incomplete information. The specific variants of EMOSOR are distinguished by the following three aspects. Firstly, they make use of two different preference models, i.e., either an additive value function or a Chebyshev function. Secondly, they aggregate the acceptability indices derived from the stochastic analysis in various ways, and use thus constructed indicators or relations to sort the solutions obtained in each generation. Thirdly, they incorporate different active learning strategies for selecting pairs of solutions to be critically judged by the DM. The extensive computational experiments performed on a set of benchmark optimization problems reveal that EMOSOR is able to bias an evolutionary search towards a part of the Pareto front being the most relevant to the DM, outperforming in this regard the state-of-the-art interactive evolutionary hybrids. Moreover, we demonstrate that the performance of EMOSOR improves in case the forms of a preference model used by the method and the DM's value system align. Furthermore, we discuss how vastly incorporation of different indicators based on the stochastic acceptability indices influences the quality of both the best constructed solution and an entire population. Finally, we demonstrate that our novel questioning strategies allow to reduce a number of interactions with the DM until a high-quality solution is constructed or, alternatively, to discover a better solution after the same number of interactions.

Keywords: Multiple objective optimization, Interactive evolutionary hybrids, Stochastic ordinal regression, Preference disaggregation, Pairwise comparisons, Active learning

[1315]

Michal K Tomczyk and Milosz Kadziński. Decomposition-based co-evolutionary algorithm for interactive multiple objective optimization. Information Sciences, 549:178–199, 2021.
[ bib | DOI ]

We propose a novel co-evolutionary algorithm for interactive multiple objective optimization, named CIEMO/D. It aims at finding a region in the Pareto front that is highly relevant to the Decision Maker (DM). For this reason, CIEMO/D asks the DM, at regular intervals, to compare pairs of solutions from the current population and uses such preference information to bias the evolutionary search. Unlike the existing interactive evolutionary algorithms dealing with just a single population, CIEMO/D co-evolves a pool of subpopulations in a steady-state decomposition-based evolutionary framework. The evolution of each subpopulation is driven by the use of a different preference model. In this way, the algorithm explores various regions in the objective space, thus increasing the chances of finding DM's most preferred solution. To improve the pace of the evolutionary search, CIEMO/D allows for the migration of solutions between different subpopulations. It also dynamically alters the subpopulations' size based on compatibility between the incorporated preference models and the decision examples supplied by the DM. The extensive experimental evaluation reveals that CIEMO/D can successfully adjust to different DM's decision policies. We also compare CIEMO/D with selected state-of-the-art interactive evolutionary hybrids that make use of the DM's pairwise comparisons, demonstrating its high competitiveness.

Keywords: Evolutionary multiple objective optimization, Co-evolution, Decomposition, Indirect preference information, Preference learning

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C. E. Torres, L. F. Rossi, J. Keffer, K. Li, and C.-C. Shen. Modeling, analysis and simulation of ant-based network routing protocols. Swarm Intelligence, 4(3):221–244, 2010.
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Heike Trautmann and Jörn Mehnen. Preference-based Pareto optimization in certain and noisy environments. Engineering Optimization, 41(1):23–38, January 2009.
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Vito Trianni and Manuel López-Ibáñez. Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. PLoS One, 10(8):e0136406, 2015.
[ bib | DOI ]

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

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The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

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Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a decision maker with the guidance of his/her preferences which are provided progressively. During the process, the decision maker can adjust his/her preferences and explore only interested regions of the search space. In recent decades, IMO has gradually become a common interest of two distinct communities, namely, the multiple criteria decision making (MCDM) and the evolutionary multiobjective optimization (EMO). The IMO methods developed by the MCDM community usually use the mathematical programming methodology to search for a single preferred Pareto optimal solution, while those which are rooted in EMO often employ evolutionary algorithms to generate a representative set of solutions in the decision maker's preferred region. This paper aims to give a review of IMO research from both MCDM and EMO perspectives. Taking into account four classification criteria including the interaction pattern, preference information, preference model, and search engine (i.e., optimization algorithm), a taxonomy is established to identify important IMO factors and differentiate various IMO methods. According to the taxonomy, state-of-the-art IMO methods are categorized and reviewed and the design ideas behind them are summarized. A collection of important issues, e.g., the burdens, cognitive biases and preference inconsistency of decision makers, and the performance measures and metrics for evaluating IMO methods, are highlighted and discussed. Several promising directions worthy of future research are also presented.

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Many small and medium enterprises (SMEs) in the UK use the beta (Business Excellence Through Action) approach to the EFQM Excellence Model to conduct business excellence self-assessment, which is in essence a multiple criteria decision analysis (MCDA) problem. This paper introduces a decision support software package called Intelligent Decision System (IDS) to implement the beta approach. It is demonstrated in the paper that the IDS-beta package can provide not only average scores but also the following numerical results and graphical displays on: Distributed assessment results to demonstrate the diversity of company performances The performance range to cater for incomplete assessment information Comparisons between current performances and past performances, among different companies among different action plans. Strengths and weaknesses The IDS-beta package also provides a structured knowledge base to help assessors to make judgements more objectively. The knowledge base contains guidelines provided by the developers of the beta approach, best practices gathered from research on award winning organizations, evidence collected from companies being assessed and comments provided by assessors to record the reasons why a specific criterion is assessed to a certain grade for a company. Four small UK companies, the industry partners of the research project, have carried out the preliminary self-assessment using the package. The results and experience of the application are discussed at the end of the paper.

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Keywords: multi-modal, IGDX

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Shlomo Zilberstein. Using Anytime Algorithms in Intelligent Systems. AI Magazine, 17(3):73–83, 1996.
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Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. This capability is essential for successful operation in domains such as signal interpretation, real-time diagnosis and repair, and mobile robot control. What characterizes these domains is that it is not feasible (computationally) or desirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. These problems relate to optimal management of uncertainty and precision. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation.

Keywords: performance profiles

[1418]

Stanley Zionts and Jyrki Wallenius. An interactive multiple objective linear programming method for a class of underlying nonlinear utility functions. Management Science, 29(5):519–529, 1983.
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[1419]

Eckart Zitzler and Lothar Thiele. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999.
[ bib | DOI ]

Proposed SPEA, http://www.tik.ee.ethz.ch/sop/publicationListFiles/zt1999a.pdf

[1420]

Eckart Zitzler, Lothar Thiele, and Johannes Bader. On Set-Based Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 14(1):58–79, 2010.
[ bib | DOI ]

Proposed SPAM and explores combination of quality indicators

Keywords: Performance assessment; Preference articulation; refinement; Set Partitioning; Set-preference

[1421]

Eckart Zitzler, Lothar Thiele, and Kalyanmoy Deb. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173–195, 2000.
[ bib | DOI ]

Keywords: ZDT benchmark

[1422]

Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M. Fonseca, and Viviane Grunert da Fonseca. Performance Assessment of Multiobjective Optimizers: an Analysis and Review. IEEE Transactions on Evolutionary Computation, 7(2):117–132, 2003.
[ bib | DOI ]

Proposed the combination of quality indicators; proposed epsilon-indicator

[1423]

M. Zlochin, Mauro Birattari, N. Meuleau, and Marco Dorigo. Model-Based Search for Combinatorial Optimization: A Critical Survey. Annals of Operations Research, 131(1–4):373–395, 2004.
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Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, and Michel Lang. mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. Arxiv preprint arXiv:1703.03373 [stat.ML], 2017.
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[1425]

Oscar Cordón, Francisco Herrera, and Thomas Stützle. Special Issue on Ant Colony Optimization: Models and Applications. Mathware & Soft Computing, 9(3):137–268, 2002.
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G. McCormick and R. S. Powell. Optimal Pump Scheduling in Water Supply Systems with Maximum Demand Charges. Journal of Water Resources Planning and Management, ASCE, 129(5):372–379, September / October 2003.
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Gang Quan, Garrison W. Greenwood, Donglin Liu, and Sharon Hu. Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms. European Journal of Operational Research, 177(3):1969–1984, 2007.
[ bib | DOI ]

Heavy industry maintenance facilities at aircraft service centers or railroad yards must contend with scheduling preventive maintenance tasks to ensure critical equipment remains available. The workforce that performs these tasks are often high-paid, which means the task scheduling should minimize worker idle time. Idle time can always be minimized by reducing the workforce. However, all preventive maintenance tasks should be completed as quickly as possible to make equipment available. This means the completion time should be also minimized. Unfortunately, a small workforce cannot complete many maintenance tasks per hour. Hence, there is a tradeoff: should the workforce be small to reduce idle time or should it be large so more maintenance can be performed each hour? A cost effective schedule should strike some balance between a minimum schedule and a minimum size workforce. This paper uses evolutionary algorithms to solve this multiobjective problem. However, rather than conducting a conventional dominance-based Pareto search, we introduce a form of utility theory to find Pareto optimal solutions. The advantage of this method is the user can target specific subsets of the Pareto front by merely ranking a small set of initial solutions. A large example problem is used to demonstrate our method.

Keywords: Evolutionary computations, Scheduling, Utility theory, Preventive maintenance, Multi-objective optimization, ranking-based, interactive

[1428]

Marvin N. Wright and Andreas Ziegler. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Arxiv preprint arXiv:1508.04409 [stat.ML], 2015.
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Marvin N. Wright and Andreas Ziegler. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1):1–17, 2017.
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[1430]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
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Jakobus E. van Zyl, Dragan A. Savic, and Godfrey A. Walters. Operational Optimization of Water Distribution Systems using a Hybrid Genetic Algorithm. Journal of Water Resources Planning and Management, ASCE, 130(2):160–170, March 2004.
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ACM. Artifact Review and Badging Version 1.1. https://www.acm.org/publications/policies/artifact-review-and-badging-current, August 2020.
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Emile H. L. Aarts, Jan H. M. Korst, and Wil Michiels. Simulated Annealing. In E. K. Burke and G. Kendall, editors, Search Methodologies, pp.  187–210. Springer, Boston, MA, 2005.
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Hussein A. Abbass. The self-adaptive Pareto differential evolution algorithm. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC'02), pp.  831–836, Piscataway, NJ, 2002. IEEE Press.
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Ricardo Henrique Remes de Lima and Aurora Trinidad Ramirez Pozo. A study on auto-configuration of Multi-Objective Particle Swarm Optimization Algorithm. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017), pp.  718–725, Piscataway, NJ, 2017. IEEE Press.
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[1437]

Hussein A. Abbass, Ruhul Sarker, and Charles Newton. PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In Proceedings of the 2001 Congress on Evolutionary Computation (CEC'01), pp.  971–978, Piscataway, NJ, 2001. IEEE Press.
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F. Ben Abdelaziz, S. Krichen, and J. Chaouachi. A hybrid heuristic for multiobjective knapsack problems. In M. G. C. Resende and J. Pinho de Souza, editors, Proceedings of MIC 1997, the 2nd Metaheuristics International Conference, pp.  205–212, 1997.
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A. Acan. An external memory implementation in ant colony optimization. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  73–84. Springer, Heidelberg, Germany, 2004.
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Keywords: memory-based ACO

[1440]

A. Acan. An external partial permutations memory for ant colony optimization. In G. R. Raidl and J. Gottlieb, editors, Proceedings of EvoCOP 2005 – 5th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 3448 of Lecture Notes in Computer Science, pp.  1–11. Springer, Heidelberg, Germany, 2005.
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Keywords: memory-based ACO

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Hernán E. Aguirre, Saúl Zapotecas, Arnaud Liefooghe, Sébastien Verel, and Kiyoshi Tanaka. Approaches for Many-Objective Optimization: Analysis and Comparison on MNK-Landscapes. In S. Bonnevay et al., editors, Artificial Evolution: 12th International Conference, Evolution Artificielle, EA, 2015, volume 9554 of Lecture Notes in Computer Science, pp.  14–28. Springer, Cham, Switzerland, 2016.
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A. Aho, J. Hopcroft, and J. Ullman. Data structures and algorithms. Addison-Wesley, Reading, MA, 1983.
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Weiwei Cheng, Jens Hühn, and Eyke Hüllermeier. Decision Tree and Instance-Based Learning for Label Ranking. In A. P. Danyluk, L. Bottou, and M. L. Littman, editors, Proceedings of the 26th International Conference on Machine Learning, ICML 2009, pp.  161–168, New York, NY, 2009. ACM Press.
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Hernán E. Aguirre and Kiyoshi Tanaka. Many-Objective Optimization by Space Partitioning and Adaptive ε-Ranking on MNK-Landscapes. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, Evolutionary Multi-criterion Optimization, EMO 2009, volume 5467 of Lecture Notes in Computer Science, pp.  407–422. Springer, Heidelberg, Germany, 2009.
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Hernán E. Aguirre. Advances on Many-objective Evolutionary Optimization. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2013, pp.  641–666. ACM Press, New York, NY, 2013.
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Keywords: many-objective evolutionary optimization

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R. K. Ahuja, T. Magnanti, and J. B. Orlin. Network Flows: Theory, Algorithms and Applications. Prentice-Hall, 1993.
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Uwe Aickelin, Edmund K. Burke, and Jingpeng Li. Improved Squeaky Wheel Optimisation for Driver Scheduling. In T. P. Runarsson, H.-G. Beyer, E. K. Burke, J.-J. Merelo, D. Whitley, and X. Yao, editors, Parallel Problem Solving from Nature – PPSN IX, volume 4193 of Lecture Notes in Computer Science, pp.  182–191. Springer, Heidelberg, Germany, 2006.
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Hassene Aissi and Bernard Roy. Robustness in Multi-criteria Decision Aiding. In M. Ehrgott, J. R. Figueira, and S. Greco, editors, Trends in Multiple Criteria Decision Analysis, volume 142 of International Series in Operations Research & Management Science, chapter 4, pp.  87–121. Springer, US, 2010.
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Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: A Next-generation Hyperparameter Optimization Framework. In Teredesai et al., editors, 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.  2623–2631. ACM Press, New York, NY, July 2019.
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S. M. Aktürk, Alper Atamtürk, and S. Gürel. A Strong Conic Quadratic Reformulation for Machine-Job Assignment with Controllable Processing Times. Research Report BCOL.07.01, University of California-Berkeley, 2007.
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I. Alaya, Christine Solnon, and Khaled Ghédira. Ant Colony Optimization for Multi-Objective Optimization Problems. In 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), volume 1, pp.  450–457. IEEE Computer Society Press, Los Alamitos, CA, 2007.
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I. Alaya, Christine Solnon, and Khaled Ghédira. Ant algorithm for the multi-dimensional knapsack problem. In B. Filipič and J. Šilc, editors, International Conference on Bioinspired Optimization Methods and their Applications (BIOMA 2004), pp.  63–72, 2004.
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Enrique Alba and Francisco Chicano. ACOhg: dealing with huge graphs. In D. Thierens et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007, pp.  10–17. ACM Press, New York, NY, 2007.
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[1454]

Mohamad Alissa, Kevin Sim, and Emma Hart. Algorithm Selection Using Deep Learning without Feature Extraction. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp.  198–206. ACM Press, New York, NY, 2019.
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Sam Allen, Edmund K. Burke, Matthew R. Hyde, and Graham Kendall. Evolving reusable 3d packing heuristics with genetic programming. In F. Rothlauf, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009, pp.  931–938. ACM Press, New York, NY, 2009.
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Keywords: hyper-heuristic

[1456]

Richard Allmendinger and Joshua D. Knowles. Evolutionary Optimization on Problems Subject to Changes of Variables. In R. Schaefer, C. Cotta, J. Kolodziej, and G. Rudolph, editors, Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture Notes in Computer Science, pp.  151–160. Springer, Heidelberg, Germany, 2010.
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Motivated by an experimental problem involving the identification of effective drug combinations drawn from a non-static drug library, this paper examines evolutionary algorithm strategies for dealing with changes of variables. We consider four standard techniques from dynamic optimization, and propose one new technique. The results show that only little additional diversity needs to be introduced into the population when changing a small number of variables, while changing many variables or optimizing a rugged landscape requires often a restart of the optimization process

[1457]

Richard Allmendinger and Joshua D. Knowles. Evolutionary Search in Lethal Environments. In International Conference on Evolutionary Computation Theory and Applications, pp.  63–72. SciTePress, 2011.
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Richard Allmendinger and Joshua D. Knowles. Policy Learning in Resource-Constrained Optimization. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  1971–1979. ACM Press, New York, NY, 2011.
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We consider an optimization scenario in which resources are required in the evaluation process of candidate solutions. The challenge we are focussing on is that certain resources have to be committed to for some period of time whenever they are used by an optimizer. This has the effect that certain solutions may be temporarily non-evaluable during the optimization. Previous analysis revealed that evolutionary algorithms (EAs) can be effective against this resourcing issue when augmented with static strategies for dealing with non-evaluable solutions, such as repairing, waiting, or penalty methods. Moreover, it is possible to select a suitable strategy for resource-constrained problems offline if the resourcing issue is known in advance. In this paper we demonstrate that an EA that uses a reinforcement learning (RL) agent, here Sarsa(λ), to learn offline when to switch between static strategies, can be more effective than any of the static strategies themselves. We also show that learning the same task as the RL agent but online using an adaptive strategy selection method, here D-MAB, is not as effective; nevertheless, online learning is an alternative to static strategies.

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Joseph Allen, Ahmed Moussa, and Xudong Liu. Human-in-the-Loop Learning of Qualitative Preference Models. In R. Barták and K. W. Brawner, editors, Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference, pp.  108–111. AAAI Press, 2019.
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[1460]

Richard Allmendinger. Tuning Evolutionary Search for Closed-Loop Optimization. PhD thesis, The University of Manchester, UK, January 2012.
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A. Alsheddy and E. Tsang. Guided Pareto local search and its application to the 0/1 multi-objective knapsack problems. In M. Caserta and S. Voß, editors, Proceedings of MIC 2009, the 8th Metaheuristics International Conference, Hamburg, Germany, 2010. University of Hamburg.
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Sanae Amani, Mahnoosh Alizadeh, and Christos Thrampoulidis. Linear Stochastic Bandits Under Safety Constraints. In H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. B. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems (NeurIPS 32), pp.  9256–9266, 2019.
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Klaus Andersen, René Victor Valqui Vidal, and Villy Bæk Iversen. Design of a Teleprocessing Communication Network Using Simulated Annealing. In R. V. V. Vidal, editor, Applied Simulated Annealing, pp.  201–215. Springer, 1993.
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J. H. Andersen and R. S. Powell. The Use of Continuous Decision Variables in an Optimising Fixed Speed Pump Scheduling Algorithm. In R. S. Powell and K. S. Hindi, editors, Computing and Control for the Water Industry, pp.  119–128. Research Studies Press Ltd., 1999.
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D. Anghinolfi, A. Boccalatte, M. Paolucci, and C. Vecchiola. Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling. In X. Li et al., editors, Simulated Evolution and Learning, 7th International Conference, SEAL 2008, volume 5361 of Lecture Notes in Computer Science, pp.  411–420. Springer, Heidelberg, Germany, 2008.
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Daniel Angus. Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation. In M. Randall, H. A. Abbass, and J. Wiles, editors, Progress in Artificial Life (ACAL), volume 4828 of Lecture Notes in Computer Science, pp.  232–244. Springer, Heidelberg, Germany, 2007.
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J. Ansel, S. Kamil, K. Veeramachaneni, J. Ragan-Kelley, J. Bosboom, Una-May O'Reilly, and S. Amarasinghe. OpenTuner: An extensible framework for program autotuning. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation, pp.  303–315, New York, NY, 2014. ACM Press.
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Carlos Ansótegui, Yuri Malitsky, Horst Samulowitz, Meinolf Sellmann, and Kevin Tierney. Model-Based Genetic Algorithms for Algorithm Configuration. In Q. Yang and M. Wooldridge, editors, Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15), pp.  733–739. IJCAI/AAAI Press, Menlo Park, CA, 2015.
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Keywords: GGA++

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Carlos Ansótegui, Yuri Malitsky, and Meinolf Sellmann. MaxSAT by Improved Instance-Specific Algorithm Configuration. In D. Stracuzzi et al., editors, Proceedings of the AAAI Conference on Artificial Intelligence, pp.  2594–2600. AAAI Press, 2014.
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Carlos Ansótegui, Meinolf Sellmann, and Kevin Tierney. A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms. In I. P. Gent, editor, Principles and Practice of Constraint Programming, CP 2009, volume 5732 of Lecture Notes in Computer Science, pp.  142–157. Springer, Heidelberg, Germany, 2009.
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Keywords: GGA

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David Applegate, Robert E. Bixby, Vašek Chvátal, and William J. Cook. Finding Tours in the TSP. Technical Report 99885, Forschungsinstitut für Diskrete Mathematik, University of Bonn, Germany, 1999.
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David Applegate, Robert E. Bixby, Vašek Chvátal, and William J. Cook. The Traveling Salesman Problem: A Computational Study. Princeton University Press, Princeton, NJ, 2006.
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Jay April, Fred Glover, James P. Kelly, and Manuel Laguna. Simulation-based optimization: Practical introduction to simulation optimization. In S. E. Chick, P. J. Sanchez, D. M. Ferrin, and D. J. Morrice, editors, Proceedings of the 35th Winter Simulation Conference: Driving Innovation, volume 1, pp.  71–78, New York, NY, December 2003. ACM Press.
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Sanjeev Arora and Boaz Barak. Computational complexity: a modern approach. Cambridge University Press, 2009.
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Etor Arza, Josu Ceberio, Aritz Pérez, and Ekhine Irurozki. Approaching the quadratic assignment problem with kernels of mallows models under the hamming distance. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019. ACM Press, New York, NY, 2019.
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Keywords: QAP, EDA, Mallows

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Y. Asahiro, K. Iwama, and E. Miyano. Random Generation of Test Instances with Controlled Attributes. In D. S. Johnson and M. A. Trick, editors, Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, volume 26 of DIMACS Series on Discrete Mathematics and Theoretical Computer Science, pp.  377–393. American Mathematical Society, Providence, RI, 1996.
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N. Ascheuer. Hamiltonian Path Problems in the On-line Optimization of Flexible Manufacturing Systems. PhD thesis, Technische Universität Berlin, Berlin, Germany, 1995.
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R. Atkinson, Jakobus E. van Zyl, Godfrey A. Walters, and Dragan A. Savic. Genetic algorithm optimisation of level-controlled pumping station operation. In Water network modelling for optimal design and management, pp.  79–90. Centre for Water Systems, Exeter, UK, 2000.
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Charles Audet, Cong-Kien Dang, and Dominique Orban. Algorithmic Parameter Optimization of the DFO Method with the OPAL Framework. In K. Naono, K. Teranishi, J. Cavazos, and R. Suda, editors, Software Automatic Tuning: From Concepts to State-of-the-Art Results, pp.  255–274. Springer, 2010.
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Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler. Articulating User Preferences in Many-Objective Problems by Sampling the Weighted Hypervolume. In F. Rothlauf, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009, pp.  555–562. ACM Press, New York, NY, 2009.
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Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler. Investigating and Exploiting the Bias of the Weighted Hypervolume to Articulate User Preferences. In F. Rothlauf, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009, pp.  563–570. ACM Press, New York, NY, 2009.
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Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler. Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point. In F. Rothlauf, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009, pp.  87–102. ACM Press, New York, NY, 2009.
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Anne Auger, Dimo Brockhoff, Manuel López-Ibáñez, Kaisa Miettinen, Boris Naujoks, and Günther Rudolph. Which questions should be asked to find the most appropriate method for decision making and problem solving? (Working Group “Algorithm Design Methods”). In S. Greco, J. D. Knowles, K. Miettinen, and E. Zitzler, editors, Learning in Multiobjective Optimization (Dagstuhl Seminar 12041), volume 2(1) of Dagstuhl Reports, pp.  92–93. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Germany, 2012.
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[1485]

A. Auger and B. Doerr, editors. Theory of Randomized Search Heuristics: Foundations and Recent Developments, volume 1 of Series on Theoretical Computer Science. World Scientific Publishing Co., Singapore, 2011.
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Anne Auger and Nikolaus Hansen. A restart CMA evolution strategy with increasing population size. In Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005), pp.  1769–1776, Piscataway, NJ, September 2005. IEEE Press.
[ bib | DOI ]

Keywords: IPOP-CMA-ES

[1487]

Anne Auger and Nikolaus Hansen. Performance evaluation of an advanced local search evolutionary algorithm. In Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005), pp.  1777–1784, Piscataway, NJ, September 2005. IEEE Press.
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Keywords: LR-CMAES

[1488]

Andreea Avramescu, Richard Allmendinger, and Manuel López-Ibáñez. A Multi-Objective Multi-Type Facility Location Problem for the Delivery of Personalised Medicine. In P. Castillo and J. L. Jiménez Laredo, editors, Applications of Evolutionary Computation, volume 12694 of Lecture Notes in Computer Science, pp.  388–403. Springer, Cham, Switzerland, 2021.
[ bib | DOI | supplementary material ]

Advances in personalised medicine targeting specific sub-populations and individuals pose a challenge to the traditional pharmaceutical industry. With a higher level of personalisation, an already critical supply chain is facing additional demands added by the very sensitive nature of its products. Nevertheless, studies concerned with the efficient development and delivery of these products are scarce. Thus, this paper presents the case of personalised medicine and the challenges imposed by its mass delivery. We propose a multi-objective mathematical model for the location-allocation problem with two interdependent facility types in the case of personalised medicine products. We show its practical application through a cell and gene therapy case study. A multi-objective genetic algorithm with a novel population initialisation procedure is used as solution method.

Keywords: Personalised medicine, Biopharmaceuticals Supply chain, Facility location-allocation, Evolutionary multi-objective optimisation

[1489]

Doǧan Aydın, Gürcan Yavuz, Serdar Özyön, Celal Yasar, and Thomas Stützle. Artificial Bee Colony Framework to Non-convex Economic Dispatch Problem with Valve Point Effects: A Case Study. In P. A. N. Bosman, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017, pp.  1311–1318. ACM Press, New York, NY, 2017.
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[1490]

Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez, Matthieu Parizy, and Arnaud Liefooghe. Applying Ising Machines to Multi-Objective QUBOs. In S. Silva and L. Paquete, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2023, pp.  2166–2174. ACM Press, New York, NY, 2023.
[ bib | DOI ]

Multi-objective optimisation problems involve finding solutions with varying trade-offs between multiple and often conflicting objectives. Ising machines are physical devices that aim to find the absolute or approximate ground states of an Ising model. To apply Ising machines to multi-objective problems, a weighted sum objective function is used to convert multi-objective into single-objective problems. However, deriving scalarisation weights that archives evenly distributed solutions across the Pareto front is not trivial. Previous work has shown that adaptive weights based on dichotomic search, and one based on averages of previously explored weights can explore the Pareto front quicker than uniformly generated weights. However, these adaptive methods have only been applied to bi-objective problems in the past. In this work, we extend the adaptive method based on averages in two ways: (i) we extend the adaptive method of deriving scalarisation weights for problems with two or more objectives, and (ii) we use an alternative measure of distance to improve performance. We compare the proposed method with existing ones and show that it leads to the best performance on multi-objective Unconstrained Binary Quadratic Programming (mUBQP) instances with 3 and 4 objectives and that it is competitive with the best one for instances with 2 objectives.

ISBN: 979-8-4007-0120-7

Keywords: digital annealer, multi-objective, bi-objective QAP, QUBO

[1491]

Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez, and Matthieu Parizy. Multi-Objective QUBO Solver: Bi-Objective Quadratic Assignment Problem. In J. E. Fieldsend and M. Wagner, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022, pp.  467–475. ACM Press, New York, NY, 2022.
[ bib | DOI ]

Quantum and quantum-inspired optimisation algorithms are designed to solve problems represented in binary, quadratic and unconstrained form. Combinatorial optimisation problems are therefore often formulated as Quadratic Unconstrained Binary Optimisation Problems (QUBO) to solve them with these algorithms. Moreover, these QUBO solvers are often implemented using specialised hardware to achieve enormous speedups, e.g. Fujitsu's Digital Annealer (DA) and D-Wave's Quantum Annealer. However, these are single-objective solvers, while many real-world problems feature multiple conflicting objectives. Thus, a common practice when using these QUBO solvers is to scalarise such multi-objective problems into a sequence of single-objective problems. Due to design trade-offs of these solvers, formulating each scalarisation may require more time than finding a local optimum. We present the first attempt to extend the algorithm supporting a commercial QUBO solver as a multi-objective solver that is not based on scalarisation. The proposed multi-objective DA algorithm is validated on the bi-objective Quadratic Assignment Problem. We observe that algorithm performance significantly depends on the archiving strategy adopted, and that combining DA with non-scalarisation methods to optimise multiple objectives outperforms the current scalarised version of the DA in terms of final solution quality.

Keywords: digital annealer, multi-objective, bi-objective QAP, QUBO

[1492]

Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez, and Matthieu Parizy. A Study of Scalarisation Techniques for Multi-objective QUBO Solving. In O. Grothe, S. Nickel, S. Rebennack, and O. Stein, editors, Operations Research Proceedings 2022, OR 2022, Lecture Notes in Operations Research, pp.  393–399. Springer, Cham, Switzerland, 2022.
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Domagoj Babić and Frank Hutter. Spear Theorem Prover. In SAT'08: Proceedings of the SAT 2008 Race, 2008.
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Keywords: SPOT

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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Automatic Component-Wise Design of Multi-Objective Evolutionary Algorithms. https://github.com/iridia-ulb/automoea-tevc-2016, 2015.
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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. To DE or Not to DE? Multi-objective Differential Evolution Revisited from a Component-Wise Perspective: Supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2015-001/, 2015.
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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms. In A. Gaspar-Cunha, C. H. Antunes, and C. A. Coello Coello, editors, Evolutionary Multi-criterion Optimization, EMO 2015 Part I, volume 9018 of Lecture Notes in Computer Science, pp.  396–410. Springer, Heidelberg, Germany, 2015.
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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms. http://iridia.ulb.ac.be/supp/IridiaSupp2015-007/, 2017.
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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms. Technical Report TR/IRIDIA/2017-005, IRIDIA, Université Libre de Bruxelles, Belgium, February 2017.
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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. An Empirical Assessment of the Properties of Inverted Generational Distance Indicators on Multi- and Many-objective Optimization. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. M. Wiecek, Y. Jin, and C. Grimme, editors, Evolutionary Multi-criterion Optimization, EMO 2017, volume 10173 of Lecture Notes in Computer Science, pp.  31–45. Springer International Publishing, Cham, Switzerland, 2017.
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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms: Supplementary material. https://github.com/iridia-ulb/automoea-ecj-2020, 2019.
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Hao Wang, Chaoli Sun, Yaochu Jin, Shufen Qin, and Haibo Yu. A Multi-indicator based Selection Strategy for Evolutionary Many-objective Optimization. In Proceedings of the 2019 Congress on Evolutionary Computation (CEC 2019), pp.  2042–2049, Piscataway, NJ, 2019. IEEE Press.
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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Archiver Effects on the Performance of State-of-the-art Multi- and Many-objective Evolutionary Algorithms: Supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/, 2019.
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Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration. In T. Bartz-Beielstein, B. Filipič, P. Korošec, and E.-G. Talbi, editors, High-Performance Simulation-Based Optimization, pp.  69–92. Springer International Publishing, Cham, Switzerland, 2020.
[ bib | DOI ]

Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this step to the computer and, thus, make it automatic. These research efforts go way beyond tuning only numerical parameters of already fully defined algorithms, but exploit automatic configuration as a means for automatic algorithm design. In this chapter, we review two main aspects where the research on automatic configuration and multi-objective optimization intersect. The first is the automatic configuration of multi-objective optimizers, where we discuss means and specific approaches. In addition, we detail a case study that shows how these approaches can be used to design new, high-performing multi-objective evolutionary algorithms. The second aspect is the research on multi-objective configuration, that is, the possibility of using multiple performance metrics for the evaluation of algorithm configurations. We highlight some few examples in this direction.

[1572]

Leonardo C. T. Bezerra. A component-wise approach to multi-objective evolutionary algorithms: from flexible frameworks to automatic design. PhD thesis, IRIDIA, École polytechnique, Université Libre de Bruxelles, Belgium, 2016.
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Supervised by Thomas Stützle and Manuel López-Ibáñez

[1573]

Leonora Bianchi, L. M. Gambardella, and Marco Dorigo. An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem. In J.-J. Merelo et al., editors, Parallel Problem Solving from Nature – PPSN VII, volume 2439 of Lecture Notes in Computer Science, pp.  883–892. Springer, Heidelberg, Germany, 2002.
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[1574]

Armin Biere. Yet another Local Search Solver and Lingeling and Friends Entering the SAT Competition 2014. In A. Belov, D. Diepold, M. Heule, and M. Järvisalo, editors, Proceedings of SAT Competition 2014: Solver and Benchmark Descriptions, volume B-2014-2 of Science Series of Publications B, pp.  39–40. University of Helsinki, 2014.
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André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, and Marius Thomas Lindauer. Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. In G. D. Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarín, and J. Lang, editors, Proceedings of the 24th European Conference on Artificial Intelligence (ECAI), volume 325 of Frontiers in Artificial Intelligence and Applications, pp.  427–434. IOS Press, 2020.
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André Biedenkapp, Marius Thomas Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, and Holger H. Hoos. Efficient Parameter Importance Analysis via Ablation with Surrogates. In S. P. Singh and S. Markovitch, editors, Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, February 2017.
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[1577]

André Biedenkapp, Joshua Marben, Marius Thomas Lindauer, and Frank Hutter. CAVE: Configuration assessment, visualization and evaluation. In R. Battiti, M. Brunato, I. Kotsireas, and P. M. Pardalos, editors, Learning and Intelligent Optimization, 12th International Conference, LION 12, volume 11353 of Lecture Notes in Computer Science, pp.  115–130. Springer, Cham, Switzerland, 2018.
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[1578]

George Bilchev and Ian C. Parmee. The Ant Colony Metaphor for Searching Continuous Design Spaces. In T. C. Fogarty, editor, Evolutionary Computing, AISB Workshop, volume 993 of Lecture Notes in Computer Science, pp.  25–39. Springer, Berlin, Germany, 1995.
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[1579]

Mauro Birattari, Prasanna Balaprakash, and Marco Dorigo. The ACO/F-RACE algorithm for combinatorial optimization under uncertainty. In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F. Hartl, and M. Reimann, editors, Metaheuristics – Progress in Complex Systems Optimization, volume 39 of Operations Research/Computer Science Interfaces Series, pp.  189–203. Springer, New York, NY, 2006.
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[1580]

Mauro Birattari, Marco Chiarandini, Marco Saerens, and Thomas Stützle. Learning Graphical Models for Algorithm Configuration. In T. Berthold, A. M. Gleixner, S. Heinz, and T. Koch, editors, Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2011, Lecture Notes in Computer Science. Springer, Heidelberg, Germany, 2011.
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[1581]

Mauro Birattari, Gianni A. Di Caro, and Marco Dorigo. Toward the formal foundation of Ant Programming. In M. Dorigo et al., editors, Ant Algorithms, Third International Workshop, ANTS 2002, volume 2463 of Lecture Notes in Computer Science, pp.  188–201. Springer, Heidelberg, Germany, 2002.
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[1582]

Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. O'Reilly Media, Inc., 2009.
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[1583]

Mauro Birattari, Thomas Stützle, Luís Paquete, and Klaus Varrentrapp. A Racing Algorithm for Configuring Metaheuristics. In W. B. Langdon et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2002, pp.  11–18. Morgan Kaufmann Publishers, San Francisco, CA, 2002.
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Keywords: F-race

[1584]

Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, and Thomas Stützle. F-Race and Iterated F-Race: An Overview. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pp.  311–336. Springer, Berlin/Heidelberg, 2010.
[ bib | DOI ]

Keywords: F-race, iterated F-race, irace, tuning

[1585]

Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, and Thomas Stützle. Parameter Adaptation in Ant Colony Optimization. In M. Caserta and S. Voß, editors, Proceedings of MIC 2009, the 8th Metaheuristics International Conference, Hamburg, Germany, 2010. University of Hamburg.
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[1586]

Mauro Birattari. Tuning Metaheuristics: A Machine Learning Perspective, volume 197 of Studies in Computational Intelligence. Springer, Berlin/Heidelberg, 2009.
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Based on the PhD thesis [1587]

[1587]

Mauro Birattari. The Problem of Tuning Metaheuristics as Seen from a Machine Learning Perspective. PhD thesis, IRIDIA, École polytechnique, Université Libre de Bruxelles, Belgium, 2004.
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Supervised by Marco Dorigo

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Francesco Biscani, Dario Izzo, and Chit Hong Yam. A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation. In Astrodynamics Tools and Techniques (ICATT 2010), 4th International Conference on, 2010.
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Keywords: PaGMO

[1589]

Bernd Bischl, Olaf Mersmann, Heike Trautmann, and Mike Preuss. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-sensitive Learning. In T. Soule and J. H. Moore, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012, pp.  313–320. ACM Press, New York, NY, 2012.
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Keywords: continuous optimization, landscape analysis, algorithm selection

[1590]

Christopher M. Bishop. Pattern recognition and machine learning. Springer, 2006.
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[1591]

Erdem Bıyık, Jonathan Margoliash, Shahrouz Ryan Alimo, and Dorsa Sadigh. Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models. In 2019 American Control Conference (ACC), pp.  1792–1799. IEEE, 2019.
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[1592]

María J. Blesa and Christian Blum. Ant Colony Optimization for the Maximum Edge-Disjoint Paths Problem. In G. R. Raidl et al., editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2004, volume 3005 of Lecture Notes in Computer Science, pp.  160–169. Springer, Heidelberg, Germany, 2004.
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John Blitzer, Ryan McDonald, and Fernando Pereira. Domain adaptation with structural correspondence learning. In D. Jurafsky and E. Gaussier, editors, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP2006, Empirical Methods in Natural Language Processing, pp.  120–128, 2006.
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Aymeric Blot, Holger H. Hoos, Laetitia Jourdan, Marie-Eléonore Kessaci-Marmion, and Heike Trautmann. MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. In P. Festa, M. Sellmann, and J. Vanschoren, editors, Learning and Intelligent Optimization, 10th International Conference, LION 10, volume 10079 of Lecture Notes in Computer Science, pp.  32–47. Springer, Cham, Switzerland, 2016.
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[1595]

Aymeric Blot, Laetitia Jourdan, and Marie-Eléonore Kessaci-Marmion. Automatic design of multi-objective local search algorithms: case study on a bi-objective permutation flowshop scheduling problem. In P. A. N. Bosman, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pp.  227–234. ACM Press, New York, NY, 2017.
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[1596]

Aymeric Blot, Manuel López-Ibáñez, Marie-Eléonore Kessaci-Marmion, and Laetitia Jourdan. New Initialisation Techniques for Multi-Objective Local Search: Application to the Bi-objective Permutation Flowshop. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, and D. Whitley, editors, Parallel Problem Solving from Nature – PPSN XV, volume 11101 of Lecture Notes in Computer Science, pp.  323–334. Springer, Cham, Switzerland, 2018.
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[1597]

Aymeric Blot, Alexis Pernet, Laetitia Jourdan, Marie-Eléonore Kessaci-Marmion, and Holger H. Hoos. Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. M. Wiecek, Y. Jin, and C. Grimme, editors, Evolutionary Multi-criterion Optimization, EMO 2017, volume 10173 of Lecture Notes in Computer Science, pp.  61–76. Springer International Publishing, Cham, Switzerland, 2017.
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[1598]

Christian Blum, J. Bautista, and J. Pereira. Beam-ACO applied to assembly line balancing. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, volume 4150 of Lecture Notes in Computer Science, pp.  96–107. Springer, Heidelberg, Germany, 2006.
[ bib | DOI ]

[1599]

Christian Blum, María J. Blesa, and Manuel López-Ibáñez. Beam Search for the Longest Common Subsequence Problem. Technical Report LSI-08-29, Department LSI, Universitat Politècnica de Catalunya, 2008. Published in Computers & Operations Research [150].
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[1600]

Christian Blum, Carlos Cotta, Antonio J. Fernández, and J. E. Gallardo. A probabilistic beam search algorithm for the shortest common supersequence problem. In C. Cotta et al., editors, Proceedings of EvoCOP 2007 – Seventh European Conference on Evolutionary Computation in Combinatorial Optimisation, volume 4446 of Lecture Notes in Computer Science, pp.  36–47. Springer, Berlin, Germany, 2007.
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[1601]

Christian Blum and Manuel López-Ibáñez. Ant Colony Optimization. In The Industrial Electronics Handbook: Intelligent Systems. CRC Press, 2nd edition, 2011.
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[1602]

Christian Blum and M. Mastrolilli. Using Branch & Bound Concepts in Construction-Based Metaheuristics: Exploiting the Dual Problem Knowledge. In T. Bartz-Beielstein, M. J. Blesa, C. Blum, B. Naujoks, A. Roli, G. Rudolph, and M. Sampels, editors, Hybrid Metaheuristics, volume 4771 of Lecture Notes in Computer Science, pp.  123–139. Springer, Heidelberg, Germany, 2007.
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[1603]

C. Blum and D. Merkle, editors. Swarm Intelligence–Introduction and Applications. Natural Computing Series. Springer Verlag, Berlin, Germany, 2008.
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[1604]

Christian Blum and Günther R. Raidl. Hybrid Metaheuristics—Powerful Tools for Optimization. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Berlin, Germany, 2016.
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[1605]

Christian Blum and Andrea Roli. Hybrid metaheuristics: an introduction. In C. Blum, M. J. Blesa, A. Roli, and M. Sampels, editors, Hybrid Metaheuristics: An emergent approach for optimization, volume 114 of Studies in Computational Intelligence, pp.  1–30. Springer, Berlin, Germany, 2008.
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Christian Blum and M. Yábar Vallès. Multi-level ant colony optimization for DNA sequencing by hybridization. In F. Almeida et al., editors, Hybrid Metaheuristics, volume 4030 of Lecture Notes in Computer Science, pp.  94–109. Springer, Heidelberg, Germany, 2006.
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[1607]

K. D. Boese. Models for Iterative Global Optimization. PhD thesis, University of California, Computer Science Department, Los Angeles, CA, 1996.
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Béla Bollobás. Random Graphs. Cambridge University Press, New York, NY, 2nd edition, 2001.
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Grady Booch, James E. Rumbaugh, and Ivar Jacobson. The Unified Modeling Language User Guide. Addison-Wesley, 2nd edition, 2005.
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P. C. Borges and Michael Pilegaard Hansen. A basis for future successes in multiobjective combinatorial optimization. Technical Report IMM-REP-1998-8, Institute of Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark, 1998.
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Allan Borodin and Ran El-Yaniv. Online computation and competitive analysis. Cambridge University Press, New York, NY, 1998.
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Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, and Hannah R. Rothstein. Introduction to Meta-Analysis. Wiley, 2009.
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Bernhard E. Boser, Isabelle Guyon, and Vladimir Vapnik. A Training Algorithm for Optimal Margin Classifiers. In D. Haussler, editor, COLT'92, pp.  144–152. ACM Press, 1992.
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Proposed SVM

[1614]

Jakob Bossek, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann. Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In T. Friedrich, C. Doerr, and D. V. Arnold, editors, Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, pp.  58–71. ACM, 2019.
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Paul F. Boulos, Chun Hou Orr, Werner de Schaetzen, J. G. Chatila, Michael Moore, Paul Hsiung, and Devan Thomas. Optimal pump operation of water distribution systems using genetic algorithms. In AWWA Distribution System Symp., Denver, USA, 2001. American Water Works Association.
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[1616]

V. Bowman and Jr. Joseph. On the Relationship of the Tchebycheff Norm and the Efficient Frontier of Multiple-Criteria Objectives. In H. Thiriez and S. Zionts, editors, Multiple Criteria Decision Making, volume 130 of Lecture Notes in Economics and Mathematical Systems, pp.  76–86. Springer, Berlin/Heidelberg, 1976.
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[1617]

George E. P. Box and Norman R. Draper. Response surfaces, mixtures, and ridge analyses. John Wiley & Sons, 2007.
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[1618]

G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for experimenters: an introduction to design, data analysis, and model building. John Wiley & Sons, New York, NY, 1978.
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A. Brandt. Multilevel Computations: Review and Recent Developments. In S. F. McCormick, editor, Multigrid Methods: Theory, Applications, and Supercomputing, Proceedings of the 3rd Copper Mountain Conference on Multigrid Methods, volume 110 of Lecture Notes in Pure and Applied Mathematics, pp.  35–62. Marcel Dekker, New York, NY, 1988.
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L. Bradstreet, L. Barone, L. While, S. Huband, and P. Hingston. Use of the WFG Toolkit and PISA for Comparison of MOEAs. In IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, IEEE MCDM, pp.  382–389, 2007.
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Cristóbal Barba-González, Vesa Ojalehto, José García-Nieto, Antonio J. Nebro, Kaisa Miettinen, and José F. Aldana-Montes. Artificial Decision Maker Driven by PSO: An Approach for Testing Reference Point Based Interactive Methods. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, and D. Whitley, editors, Parallel Problem Solving from Nature – PPSN XV, volume 11101 of Lecture Notes in Computer Science, pp.  274–285. Springer, Cham, Switzerland, 2018.
[ bib | DOI ]

Keywords: machine decision-maker

[1622]

Jürgen Branke, Salvatore Corrente, Salvatore Greco, Milosz Kadziński, Manuel López-Ibáñez, Vincent Mousseau, Mauro Munerato, and Roman Slowiński. Behavior-Realistic Artificial Decision-Makers to Test Preference-Based Multi-objective Optimization Method (Working Group “Machine Decision-Making”). In S. Greco, K. Klamroth, J. D. Knowles, and G. Rudolph, editors, Understanding Complexity in Multiobjective Optimization (Dagstuhl Seminar 15031), volume 5(1) of Dagstuhl Reports, pp.  110–116. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Germany, 2015.
[ bib | DOI ]

Keywords: multiple criteria decision making, evolutionary multiobjective optimization

[1623]

Jürgen Branke and Kalyanmoy Deb. Integrating User Preferences into Evolutionary Multi-Objective Optimization. In Y. Jin, editor, Knowledge Incorporation in Evolutionary Computation, pp.  461–477. Springer, Berlin/Heidelberg, 2005.
[ bib | DOI ]

Many real-world optimization problems involve multiple, typically conflicting objectives. Often, it is very difficult to weigh the different criteria exactly before alternatives are known. Evolutionary multi-objective optimization usually solves this predicament by searching for the whole Pareto-optimal front of solutions. However, often the user has at least a vague idea about what kind of solutions might be preferred. In this chapter, we argue that such knowledge should be used to focus the search on the most interesting (from a user's perspective) areas of the Paretooptimal front. To this end, we present and compare two methods which allow to integrate vague user preferences into evolutionary multi-objective algorithms. As we show, such methods may speed up the search and yield a more fine-grained selection of alternatives in the most relevant parts of the Pareto-optimal front.

[1624]

Yesnier Bravo, Javier Ferrer, Gabriel J. Luque, and Enrique Alba. Smart Mobility by Optimizing the Traffic Lights: A New Tool for Traffic Control Centers. In E. Alba, F. Chicano, and G. J. Luque, editors, Smart Cities (Smart-CT 2016), Lecture Notes in Computer Science, pp.  147–156. Springer, Cham, Switzerland, 2016.
[ bib | DOI ]

Urban traffic planning is a fertile area of Smart Cities to improve efficiency, environmental care, and safety, since the traffic jams and congestion are one of the biggest sources of pollution and noise. Traffic lights play an important role in solving these problems since they control the flow of the vehicular network at the city. However, the increasing number of vehicles makes necessary to go from a local control at one single intersection to a holistic approach considering a large urban area, only possible using advanced computational resources and techniques. Here we propose HITUL, a system that supports the decisions of the traffic control managers in a large urban area. HITUL takes the real traffic conditions and compute optimal traffic lights plans using bio-inspired techniques and micro-simulations. We compare our system against plans provided by experts. Our solutions not only enable continuous traffic flows but reduce the pollution. A case study of Málaga city allows us to validate the approach and show its benefits for other cities as well.

Keywords: Multi-objective optimization, Smart mobility, Traffic lights planning

[1625]

Jean-Pierre Brans and Bertrand Mareschal. PROMETHEE-GAIA. Une méthode d'aide à la décision en présence de critères multiples. Editions Ellipses, Paris, France, 2002.
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[1626]

Jean-Pierre Brans and Bertrand Mareschal. PROMETHEE Methods. In J. R. Figueira, S. Greco, and M. Ehrgott, editors, Multiple Criteria Decision Analysis, State of the Art Surveys, chapter 5, pp.  163–195. Springer, 2005.
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[1627]

Jürgen Branke, C. Schmidt, and H. Schmeck. Efficient fitness estimation in noisy environments. In E. D. Goodman, editor, Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, GECCO 2001, pp.  243–250. Morgan Kaufmann Publishers, San Francisco, CA, 2001.
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[1628]

Jürgen Branke, Salvatore Corrente, Salvatore Greco, Roman Slowiński, and P. Zielniewicz. Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. Technical report, WBS, University of Warwick, 2014.
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[1629]

Jürgen Branke and Jawad Elomari. Simultaneous tuning of metaheuristic parameters for various computing budgets. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  263–264. ACM Press, New York, NY, 2011.
[ bib | DOI ]

Keywords: meta-optimization, offline parameter optimization

[1630]

Jürgen Branke and Jawad Elomari. Racing with a Fixed Budget and a Self-Adaptive Significance Level. In P. M. Pardalos and G. Nicosia, editors, Learning and Intelligent Optimization, 7th International Conference, LION 7, volume 7997 of Lecture Notes in Computer Science. Springer, Heidelberg, Germany, 2013.
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[1631]

Leo Breiman, Jerome Friedman, Charles J. Stone, and Richard A. Olshen. Classification and regression trees. CRC Press, 1984.
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[1632]

Mátyás Brendel and Marc Schoenauer. Learn-and-Optimize: A Parameter Tuning Framework for Evolutionary AI Planning. In J.-K. Hao, P. Legrand, P. Collet, N. Monmarché, E. Lutton, and M. Schoenauer, editors, Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011, volume 7401 of Lecture Notes in Computer Science, pp.  145–155. Springer, Heidelberg, Germany, 2012.
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[1633]

Mátyás Brendel and Marc Schoenauer. Instance-based Parameter Tuning for Evolutionary AI Planning. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2011, pp.  591–598. ACM Press, New York, NY, 2011.
[ bib | DOI ]

[1634]

Karl Bringmann and Tobias Friedrich. Approximating the Least Hypervolume Contributor: NP-Hard in General, But Fast in Practice. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, Evolutionary Multi-criterion Optimization, EMO 2009, volume 5467 of Lecture Notes in Computer Science, pp.  6–20. Springer, Heidelberg, Germany, 2009.
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Extended version published in [185]

[1635]

Karl Bringmann and Tobias Friedrich. The Maximum Hypervolume Set Yields Near-optimal Approximation. In M. Pelikan and J. Branke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010, pp.  511–518. ACM Press, New York, NY, 2010.
[ bib ]

Proved that hypervolume approximates the additive ε-indicator, converging quickly as N increases, that is, sets that maximize hypervolume are near optimal on additive ε too, with the gap diminishing as quickly as O(1/N).

[1636]

Karl Bringmann and Tobias Friedrich. Tight bounds for the approximation ratio of the hypervolume indicator. In R. Schaefer, C. Cotta, J. Kolodziej, and G. Rudolph, editors, Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture Notes in Computer Science, pp.  607–616. Springer, Heidelberg, Germany, 2010.
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[1637]

Karl Bringmann and Tobias Friedrich. Convergence of Hypervolume-Based Archiving Algorithms I: Effectiveness. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  745–752. ACM Press, New York, NY, 2011.
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Extended version published as [187]

[1638]

Karl Bringmann and Tobias Friedrich. Convergence of Hypervolume-Based Archiving Algorithms II: Competitiveness. In T. Soule and J. H. Moore, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012, pp.  457–464. ACM Press, New York, NY, 2012.
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Extended version published as [187]

[1639]

Karl Bringmann, Tobias Friedrich, and Patrick Klitzke. Generic postprocessing via subset selection for hypervolume and epsilon-indicator. In T. Bartz-Beielstein, J. Branke, B. Filipič, and J. Smith, editors, Parallel Problem Solving from Nature – PPSN XIII, volume 8672 of Lecture Notes in Computer Science, pp.  518–527. Springer, Heidelberg, Germany, 2014.
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[1640]

Karl Bringmann, Tobias Friedrich, and Patrick Klitzke. Two-dimensional subset selection for hypervolume and epsilon-indicator. In C. Igel and D. V. Arnold, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014. ACM Press, New York, NY, 2014.
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[1641]

Andre Britto and Aurora Pozo. Using archiving methods to control convergence and diversity for many-objective problems in particle swarm optimization. In Proceedings of the 2012 Congress on Evolutionary Computation (CEC 2012), pp.  1–8, Piscataway, NJ, 2012. IEEE Press.
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[1642]

Karl Bringmann and Tobias Friedrich. Don't be greedy when calculating hypervolume contributions. In I. I. Garibay, T. Jansen, R. P. Wiegand, and A. S. Wu, editors, Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA), pp.  103–112. ACM, 2009.
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Extended version published in [186]

[1643]

Karl Bringmann, Tobias Friedrich, Frank Neumann, and Markus Wagner. Approximation-guided Evolutionary Multi-objective Optimization. In T. Walsh, editor, Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11), pp.  1198–1203. IJCAI/AAAI Press, Menlo Park, CA, 2011.
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[1644]

Dimo Brockhoff. A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons for Code Release and a Performance Re-Assessment. In A. Gaspar-Cunha, C. H. Antunes, and C. A. Coello Coello, editors, Evolutionary Multi-criterion Optimization, EMO 2015 Part I, volume 9018 of Lecture Notes in Computer Science, pp.  187–201. Springer, Heidelberg, Germany, 2015.
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[1645]

Dimo Brockhoff, Roberto Calandra, Manuel López-Ibáñez, Frank Neumann, and Selvakumar Ulaganathan. Meta-modeling for (interactive) multi-objective optimization (WG5). In K. Klamroth, J. D. Knowles, G. Rudolph, and M. M. Wiecek, editors, Personalized Multiobjective Optimization: An Analytics Perspective (Dagstuhl Seminar 18031), volume 8(1) of Dagstuhl Reports, pp.  85–94. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Germany, 2018.
[ bib | DOI ]

Keywords: multiple criteria decision making, evolutionary multiobjective optimization

[1646]

Dimo Brockhoff, Tobias Friedrich, N. Hebbinghaus, C. Klein, Frank Neumann, and Eckart Zitzler. Do Additional Objectives Make a Problem Harder? In D. Thierens et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007, pp.  765–772. ACM Press, New York, NY, 2007.
[ bib | DOI ]

[1647]

Dimo Brockhoff, Manuel López-Ibáñez, Boris Naujoks, and Günther Rudolph. Runtime Analysis of Simple Interactive Evolutionary Biobjective Optimization Algorithms. In C. A. Coello Coello et al., editors, Parallel Problem Solving from Nature – PPSN XII, Part I, volume 7491 of Lecture Notes in Computer Science, pp.  123–132. Springer, Heidelberg, Germany, 2012.
[ bib | DOI ]

Development and deployment of interactive evolutionary multiobjective optimization algorithms (EMOAs) have recently gained broad interest. In this study, first steps towards a theory of interactive EMOAs are made by deriving bounds on the expected number of function evaluations and queries to a decision maker. We analyze randomized local search and the (1+1)-EA on the biobjective problems LOTZ and COCZ under the scenario that the decision maker interacts with these algorithms by providing a subjective preference whenever solutions are incomparable. It is assumed that this decision is based on the decision maker's internal utility function. We show that the performance of the interactive EMOAs may dramatically worsen if the utility function is non-linear instead of linear.

[1648]

Dimo Brockhoff, Dhish Kumar Saxena, Kalyanmoy Deb, and Eckart Zitzler. On Handling a Large Number of Objectives A Posteriori and During Optimization. In J. D. Knowles, D. Corne, K. Deb, and D. R. Chair, editors, Multiobjective Problem Solving from Nature, Natural Computing Series, pp.  377–403. Springer, Berlin/Heidelberg, 2008.
[ bib | DOI ]

Dimensionality reduction methods are used routinely in statistics, pattern recognition, data mining, and machine learning to cope with high-dimensional spaces. Also in the case of high-dimensional multiobjective optimization problems, a reduction of the objective space can be beneficial both for search and decision making. New questions arise in this context, e.g., how to select a subset of objectives while preserving most of the problem structure. In this chapter, two different approaches to the task of objective reduction are developed, one based on assessing explicit conflicts, the other based on principal component analysis (PCA). Although both methods use different principles and preserve different properties of the underlying optimization problems, they can be effectively utilized either in an a posteriori scenario or during search. Here, we demonstrate the usability of the conflict-based approach in a decision-making scenario after the search and show how the principal-component-based approach can be integrated into an evolutionary multicriterion optimization (EMO) procedure.

[1649]

Dimo Brockhoff and Tea Tušar. Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbed. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019, pp.  1905–1911. ACM Press, New York, NY, 2019.
[ bib | DOI ]

Keywords: unbounded archive

[1650]

Dimo Brockhoff, Tobias Wagner, and Heike Trautmann. On the properties of the R2 indicator. In T. Soule and J. H. Moore, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012, pp.  465–472. ACM Press, New York, NY, 2012.
[ bib ]

Proof that R2 is weakly Pareto compliant.

[1651]

Dimo Brockhoff and Eckart Zitzler. Are All Objectives Necessary? On Dimensionality Reduction in Evolutionary Multiobjective Optimization. In T. P. Runarsson, H.-G. Beyer, E. K. Burke, J.-J. Merelo, D. Whitley, and X. Yao, editors, Parallel Problem Solving from Nature – PPSN IX, volume 4193 of Lecture Notes in Computer Science, pp.  533–542. Springer, Heidelberg, Germany, 2006.
[ bib ]

Most of the available multiobjective evolutionary algorithms (MOEA) for approximating the Pareto set have been designed for and tested on low dimensional problems (≤3 objectives). However, it is known that problems with a high number of objectives cause additional difficulties in terms of the quality of the Pareto set approximation and running time. Furthermore, the decision making process becomes the harder the more objectives are involved. In this context, the question arises whether all objectives are necessary to preserve the problem characteristics. One may also ask under which conditions such an objective reduction is feasible, and how a minimum set of objectives can be computed. In this paper, we propose a general mathematical framework, suited to answer these three questions, and corresponding algorithms, exact and heuristic ones. The heuristic variants are geared towards direct integration into the evolutionary search process. Moreover, extensive experiments for four well-known test problems show that substantial dimensionality reductions are possible on the basis of the proposed methodology.

[1652]

Dimo Brockhoff and Eckart Zitzler. Dimensionality Reduction in Multiobjective Optimization: The Minimum Objective Subset Problem. In K.-H. Waldmann and U. M. Stocker, editors, Operations Research Proceedings 2006, pp.  423–429. Springer, Berlin/Heidelberg, 2007.
[ bib | DOI ]

The number of objectives in a multiobjective optimization problem strongly influences both the performance of generating methods and the decision making process in general. On the one hand, with more objectives, more incomparable solutions can arise, the number of which affects the generating method's performance. On the other hand, the more objectives are involved the more complex is the choice of an appropriate solution for a (human) decision maker. In this context, the question arises whether all objectives are actually necessary and whether some of the objectives may be omitted; this question in turn is closely linked to the fundamental issue of conflicting and non-conflicting optimization criteria. Besides a general definition of conflicts between objective sets, we here introduce the NP-hard problem of computing a minimum subset of objectives without losing information (MOSS). Furthermore, we present for MOSS both an approximation algorithm with optimum approximation ratio and an exact algorithm which works well for small input instances. We conclude with experimental results for a random problem and the multiobjective 0/1-knapsack problem

Keywords: objective reduction

[1653]

Dimo Brockhoff and Eckart Zitzler. Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods. In Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007), pp.  2086–2093, Piscataway, NJ, 2007. IEEE Press.
[ bib | DOI ]

Keywords: objective reduction

[1654]

Artur Brum and Marcus Ritt. Automatic Design of Heuristics for Minimizing the Makespan in Permutation Flow Shops. In Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018), pp.  1–8, Piscataway, NJ, 2018. IEEE Press.
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[1655]

Artur Brum and Marcus Ritt. Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time. In A. Liefooghe and M. López-Ibáñez, editors, Proceedings of EvoCOP 2018 – 18th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 10782 of Lecture Notes in Computer Science, pp.  85–100. Springer, Heidelberg, Germany, 2018.
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[1656]

T. N. Bui and J. R. Rizzo, Jr. Finding Maximum Cliques with Distributed Ants. In K. Deb et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, Part I, volume 3102 of Lecture Notes in Computer Science, pp.  24–35. Springer, Heidelberg, Germany, 2004.
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[1657]

Edmund K. Burke and Yuri Bykov. The Late Acceptance Hill-Climbing Heuristic. Technical Report CSM-192, University of Stirling, 2012.
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[1658]

Edmund K. Burke, Matthew R. Hyde, Graham Kendall, and John R. Woodward. Automatic Heuristic Generation with Genetic Programming: Evolving a Jack-of-all-trades or a Master of One. In D. Thierens et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007, pp.  1559–1565. ACM Press, New York, NY, 2007.
[ bib | DOI ]

[1659]

Edmund K. Burke, Matthew R. Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan, and John R. Woodward. A Classification of Hyper-Heuristic Approaches: Revisited. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 272 of International Series in Operations Research & Management Science, chapter 14, pp.  453–477. Springer, 2019.
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[1660]

Rainer E. Burkard, Eranda Çela, Panos M. Pardalos, and L. S. Pitsoulis. The quadratic assignment problem. In P. M. Pardalos and D.-Z. Du, editors, Handbook of Combinatorial Optimization, volume 2, pp.  241–338. Kluwer Academic Publishers, 1998.
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[1661]

Maxim Buzdalov. Towards better estimation of statistical significance when comparing evolutionary algorithms. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019, pp.  1782–1788. ACM Press, New York, NY, 2019.
[ bib | DOI ]

[1662]

Nicola Beume, Carlos M. Fonseca, Manuel López-Ibáñez, Luís Paquete, and Jan Vahrenhold. On the Complexity of Computing the Hypervolume Indicator. Technical Report CI-235/07, University of Dortmund, December 2007. Published in IEEE Transactions on Evolutionary Computation [127].
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[1663]

COnfiguration and SElection of ALgorithms. http://www.coseal.net, 2017.
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[1664]

IBM. ILOG CPLEX Optimizer. http://www.ibm.com/software/integration/optimization/cplex-optimizer/, 2017.
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[1665]

Borja Calvo, Ofer M. Shir, Josu Ceberio, Carola Doerr, Hao Wang, Thomas Bäck, and José A. Lozano. Bayesian Performance Analysis for Black-box Optimization Benchmarking. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019, pp.  1789–1797. ACM Press, New York, NY, 2019.
[ bib | DOI ]

Keywords: bayesian inference, benchmarking, black-box optimization, evolutionary algorithms, performance measures, plackett-luce model

[1666]

Christian Leonardo Camacho-Villalón, Marco Dorigo, and Thomas Stützle. Why the Intelligent Water Drops Cannot Be Considered as a Novel Algorithm. In M. Dorigo, M. Birattari, A. L. Christensen, A. Reina, and V. Trianni, editors, Swarm Intelligence, 11th International Conference, ANTS 2018, volume 11172 of Lecture Notes in Computer Science, pp.  302–314. Springer, Heidelberg, Germany, 2018.
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Paolo Campigotto and Andrea Passerini. Adapting to a realistic decision maker: experiments towards a reactive multi-objective optimizer. In C. Blum and R. Battiti, editors, Learning and Intelligent Optimization, 4th International Conference, LION 4, volume 6073 of Lecture Notes in Computer Science, pp.  338–341. Springer, Heidelberg, Germany, 2010.
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[1668]

Christian Leonardo Camacho-Villalón, Thomas Stützle, and Marco Dorigo. Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty. In M. Dorigo, T. Stützle, M. J. Blesa, C. Blum, H. Hamann, and M. K. Heinrich, editors, Swarm Intelligence, 12th International Conference, ANTS 2020, volume 12421 of Lecture Notes in Computer Science, pp.  121–133. Springer, Heidelberg, Germany, 2020.
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[1669]

Felipe Campelo, Áthila R. Trindade, and Manuel López-Ibáñez. Pseudoreplication in Racing Methods for Tuning Metaheuristics. In preparation, 2017.
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[1670]

E. Cantú-Paz. Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Boston, MA, 2000.
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P. Cardoso, M. Jesus, and A. Marquez. MONACO: multi-objective network optimisation based on an ACO. In Proc. X Encuentros de Geometría Computacional, Seville, Spain, 2003.
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Alex Guimarães Cardoso de Sá, Walter José G. S. Pinto, Luiz Otávio Vilas Boas Oliveira, and Gisele Pappa. RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines. In J. McDermott, M. Castelli, L. Sekanina, E. Haasdijk, and P. García-Sánchez, editors, Proceedings of the 20th European Conference on Genetic Programming, EuroGP 2017, volume 10196 of Lecture Notes in Computer Science, pp.  246–261. Springer, Heidelberg, Germany, 2017.
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[1673]

Ioannis Caragiannis, Ariel D. Procaccia, and Nisarg Shah. When Do Noisy Votes Reveal the Truth? In M. J. Kearns, R. P. McAfee, and É. Tardos, editors, Proceedings of the Fourteenth ACM Conference on Electronic Commerce, pp.  143–160. ACM Press, New York, NY, 2013.
[ bib | DOI ]

A well-studied approach to the design of voting rules views them as maximum likelihood estimators; given votes that are seen as noisy estimates of a true ranking of the alternatives, the rule must reconstruct the most likely true ranking. We argue that this is too stringent a requirement, and instead ask: How many votes does a voting rule need to reconstruct the true ranking? We define the family of pairwise-majority consistent rules, and show that for all rules in this family the number of samples required from the Mallows noise model is logarithmic in the number of alternatives, and that no rule can do asymptotically better (while some rules like plurality do much worse). Taking a more normative point of view, we consider voting rules that surely return the true ranking as the number of samples tends to infinity (we call this property accuracy in the limit); this allows us to move to a higher level of abstraction. We study families of noise models that are parametrized by distance functions, and find voting rules that are accurate in the limit for all noise models in such general families. We characterize the distance functions that induce noise models for which pairwise-majority consistent rules are accurate in the limit, and provide a similar result for another novel family of position-dominance consistent rules. These characterizations capture three well-known distance functions.

Keywords: computer social choice, mallows model, sample complexity

[1674]

Josu Ceberio, Alexander Mendiburu, and José A. Lozano. Kernels of Mallows Models for Solving Permutation-based Problems. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  505–512. ACM Press, New York, NY, 2015.
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Eranda Çela. The Quadratic Assignment Problem: Theory and Algorithms. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1998.
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Amadeo Cesta, Angelo Oddi, and Stephen F. Smith. Iterative Flattening: A Scalable Method for Solving Multi-Capacity Scheduling Problems. In H. A. Kautz and B. W. Porter, editors, Proceedings of AAAI 2000 – Seventeenth National Conference on Artificial Intelligence, pp.  742–747. AAAI Press/MIT Press, Menlo Park, CA, 2000.
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S. T. H. Chang. Optimizing the Real Time Operation of a Pumping Station at a Water Filtration Plant using Genetic Algorithms. Honors thesis, Department of Civil and Environmental Engineering, The University of Adelaide, 1999.
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Donald V. Chase and Lindell E. Ormsbee. Optimal pump operation of water distribution systems with multiple storage tanks. In Proceedings of American Water Works Association Computer Specialty Conference, pp.  205–214, Denver, USA, 1989. AWWA.
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Donald V. Chase and Lindell E. Ormsbee. An alternate formulation of time as a decision variable to facilitate real-time operation of water supply systems. In Proceedings of the 18th Annual Conference of Water Resources Planning and Management, pp.  923–927, New York, NY, 1991. ASCE.
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Deyao Chen, Maxim Buzdalov, Carola Doerr, and Nguyen Dang. Using Automated Algorithm Configuration for Parameter Control. In F. Chicano, T. Friedrich, T. Kötzing, and F. Rothlauf, editors, Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, pp.  38–49. ACM, 2023.
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Fei Chen, Yang Gao, Zhao-qian Chen, and Shi-fu Chen. SCGA: Controlling genetic algorithms with Sarsa(0). In Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on, volume 1, pp.  1177–1183. IEEE, 2005.
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[1682]

Clément Chevalier, David Ginsbourger, Julien Bect, and Ilya Molchanov. Estimating and Quantifying Uncertainties on Level Sets Using the Vorob'ev Expectation and Deviation with Gaussian Process Models. In D. Ucinski, A. C. Atkinson, and M. Patan, editors, mODa 10–Advances in Model-Oriented Design and Analysis, pp.  35–43. Springer International Publishing, Heidelberg, Germany, 2013.
[ bib | DOI ]

Several methods based on Kriging have recently been proposed for calculating a probability of failure involving costly-to-evaluate functions. A closely related problem is to estimate the set of inputs leading to a response exceeding a given threshold. Now, estimating such a level set—and not solely its volume—and quantifying uncertainties on it are not straightforward. Here we use notions from random set theory to obtain an estimate of the level set, together with a quantification of estimation uncertainty. We give explicit formulae in the Gaussian process set-up and provide a consistency result. We then illustrate how space-filling versus adaptive design strategies may sequentially reduce level set estimation uncertainty.

[1683]

Weiyu Chen, Hisao Ishibuchi, and Ke Shang. Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization. In 2021 IEEE International Conference on Systems, Man, and Cybernetics, pp.  468–475. IEEE, 2021.
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Peter C. Cheeseman, Bob Kanefsky, and William M. Taylor. Where the Really Hard Problems Are. In J. Mylopoulos and R. Reiter, editors, Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI-91), pp.  331–340. Morgan Kaufmann Publishers, 1995.
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L. Chen, X. H. Xu, and Y. X. Chen. An adaptive ant colony clustering algorithm. In I. Cloete, K.-P. Wong, and M. Berthold, editors, Proceedings of the International Conference on Machine Learning and Cybernetics, pp.  1387–1392. IEEE Press, 2004.
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Weiyu Chen, Hisao Ishibuchi, and Ke Shang. Modified Distance-based Subset Selection for Evolutionary Multi-objective Optimization Algorithms. In Proceedings of the 2020 Congress on Evolutionary Computation (CEC 2020), pp.  1–8, Piscataway, NJ, 2020. IEEE Press.
[ bib ]

Keywords: IGD+

[1687]

Lu Chen, Bin Xin, Jie Chen, and Juan Li. A virtual-decision-maker library considering personalities and dynamically changing preference structures for interactive multiobjective optimization. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017), pp.  636–641, Piscataway, NJ, 2017. IEEE Press.
[ bib | DOI ]

Keywords: machine DM, interactive EMOA

[1688]

Francisco Chicano, Bilel Derbel, and Sébastien Verel. Fourier Transform-based Surrogates for Permutation Problems. In S. Silva and L. Paquete, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023, pp.  275–283. ACM Press, New York, NY, 2023.
[ bib | DOI ]

ISBN: 9798400701191

[1689]

Marco Chiarandini and Yuri Goegebeur. Mixed Models for the Analysis of Optimization Algorithms. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pp.  225–264. Springer, Berlin/Heidelberg, 2010.
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Preliminary version available as Tech. Rep. MF-2009-07-001 at the The Danish Mathematical Society

[1690]

Marco Chiarandini. Stochastic Local Search Methods for Highly Constrained Combinatorial Optimisation Problems. PhD thesis, FB Informatik, TU Darmstadt, Germany, 2005.
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Tsung-Che Chiang. nsga3cpp: A C++ implementation of NSGA-III. http://web.ntnu.edu.tw/~tcchiang/publications/nsga3cpp/nsga3cpp.htm, 2014.
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Matthias Christen, Olaf Schenk, and Helmar Burkhart. PATUS: A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures. In F. Mueller, editor, Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium, IPDPS '11, pp.  676–687. IEEE Computer Society, 2011.
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[1693]

Jan Christiaens and Greet Vanden Berghe. Slack Induction by String Removals for Vehicle Routing Problems. Technical Report 7-05-2018, Department of Computing Science, KU Leuven, Gent, Belgium, 2018.
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[1694]

Nicos Christofides. Worst-case analysis of a new heuristic for the travelling salesman problem. Technical Report 388, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, PA, 1976.
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[1695]

Tinkle Chugh and Manuel López-Ibáñez. Maximising Hypervolume and Minimising ε-Indicators using Bayesian Optimisation over Sets. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021, pp.  1326–1334. ACM Press, New York, NY, 2021.
[ bib | DOI | supplementary material ]

Keywords: multi-objective, surrogate models, epsilon, hypervolume

[1696]

S. Chusanapiputt, D. Nualhong, S. Jantarang, and S. Phoomvuthisarn. Selective self-adaptive approach to ant system for solving unit commitment problem. In M. Cattolico et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006, pp.  1729–1736. ACM Press, New York, NY, 2006.
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[1697]

Tinkle Chugh. Handling expensive multiobjective optimization problems with evolutionary algorithms. PhD thesis, University of Jyväskylä, 2017.
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[1698]

Tinkle Chugh. Scalarizing Functions in Bayesian Multiobjective Optimization. In Proceedings of the 2020 Congress on Evolutionary Computation (CEC 2020), pp.  1–8, Piscataway, NJ, 2020. IEEE Press.
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[1699]

Christian Cintrano, Javier Ferrer, Manuel López-Ibáñez, and Enrique Alba. Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs. In C. Zarges and S. Verel, editors, Proceedings of EvoCOP 2021 – 21th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 12692 of Lecture Notes in Computer Science, pp.  17–33. Springer, Cham, Switzerland, 2021.
[ bib | DOI ]

In many real-world optimization problems, like the traffic light scheduling problem tackled here, the evaluation of candidate solutions requires the simulation of a process under various scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has revealed the effectiveness of IRACE for this task. However, the operators used by IRACE to generate new solutions were designed for configuring algorithmic parameters, that have various data types (categorical, numerical, etc.). Meanwhile, evolutionary algorithms have powerful operators for numerical optimization, which could help to sample new solutions from the best ones found in the search. Therefore, in this work, we propose a hybridization of the elitist iterated racing mechanism of IRACE with evolutionary operators from differential evo- lution and genetic algorithms. We consider a realistic case study derived from the traffic network of Malaga (Spain) with 275 traffic lights that should be scheduled optimally. After a meticulous study, we discovered that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than conventional algorithms and also improves travel times and reduces pollution.

Extended version published in Evolutionary Computation journal [266].

Keywords: Hybrid algorithms, Evolutionary algorithms, Simulation optimization, Uncertainty, Traffic light planning

[1700]

Jill Cirasella, David S. Johnson, Lyle A. McGeoch, and Weixiong Zhang. The Asymmetric Traveling Salesman Problem: Algorithms, Instance Generators, and Tests. In A. L. Buchsbaum and J. Snoeyink, editors, Algorithm Engineering and Experimentation, Third International Workshop, ALENEX 2001, Washington, DC, USA, January 5-6, 2001, Revised Papers, volume 2153 of Lecture Notes in Computer Science, pp.  32–59, Berlin, Germany, 2001. Springer.
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[1701]

Jon Claerbout and Martin Karrenbach. Electronic documents give reproducible research a new meaning. In SEG Technical Program Expanded Abstracts 1992, pp.  601–604. Society of Exploration Geophysicists, 1992.
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Proposed a reproducibility taxonomy, defined reproducibility and taxonomy

[1702]

Maurice Clerc and J. Kennedy. Standard PSO 2011. Particle Swarm Central, 2011.
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[1703]

Maurice Clerc. Standard Particle Swarm Optimisation. hal-00764996, September 2012.
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Since 2006, three successive standard PSO versions have been put on line on the Particle Swarm Central (http://particleswarm.info), namely SPSO 2006, 2007, and 2011. The basic principles of all three versions can be informally described the same way, and in general, this statement holds for almost all PSO variants. However, the exact formulae are slightly different, because they took advantage of latest theoretical analysis available at the time they were designed.

Keywords: particle swarm optimisation

[1704]

Carlos A. Coello Coello. Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges. In Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, pp.  3–18. Springer, 2015.
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[1705]

Carlos A. Coello Coello, Gary B. Lamont, and David A. Van Veldhuizen. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York, NY, 2nd edition, 2007.
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Carlos A. Coello Coello and Margarita Reyes-Sierra. A Study of the Parallelization of a Coevolutionary Multi-objective Evolutionary Algorithm. In R. Monroy, G. Arroyo-Figueroa, L. E. Sucar, and H. Sossa, editors, Proceedings of MICAI, volume 2972 of Lecture Notes in Artificial Intelligence, pp.  688–697. Springer, Heidelberg, Germany, 2004.
[ bib ]

Introduces Inverted Generational Distance (IGD)

Keywords: IGD

[1707]

Carlos A. Coello Coello. Handling Preferences in Evolutionary Multiobjective Optimization: A Survey. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC'00), pp.  30–37, Piscataway, NJ, July 2000. IEEE Press.
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Carlos A. Coello Coello. Recent Results and Open Problems in Evolutionary Multiobjective Optimization. In C. Martín-Vide, R. Neruda, and M. A. Vega-Rodríguez, editors, Theory and Practice of Natural Computing - 6th International Conference, TPNC 2017, volume 10687 of Lecture Notes in Computer Science, pp.  3–21. Springer International Publishing, Cham, Switzerland, 2017.
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[1709]

Paul R. Cohen. Empirical Methods for Artificial Intelligence. MIT Press, Cambridge, MA, 1995.
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[1710]

G. Cohen. Optimal Control of Water Supply Networks. In S. G. Tzafestas, editor, Optimization and Control of Dynamic Operational Research Models, volume 4, chapter 8, pp.  251–276. North-Holland Publishing Company, Amsterdam, 1982.
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[1711]

Alberto Colorni, Marco Dorigo, and Vittorio Maniezzo. Distributed Optimization by Ant Colonies. In F. J. Varela and P. Bourgine, editors, Proceedings of the First European Conference on Artificial Life, pp.  134–142. MIT Press, Cambridge, MA, 1992.
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Sonia Colas, Nicolas Monmarché, Pierre Gaucher, and Mohamed Slimane. Artificial Ants for the Optimization of Virtual Keyboard Arrangement for Disabled People. In N. Monmarché, E.-G. Talbi, P. Collet, M. Schoenauer, and E. Lutton, editors, Artificial Evolution, volume 4926 of Lecture Notes in Computer Science, pp.  87–99. Springer, Heidelberg, Germany, 2008.
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David Corne, Nick R. Jerram, Joshua D. Knowles, and Martin J. Oates. PESA-II: Region-Based Selection in Evolutionary Multiobjective Optimization. In E. D. Goodman, editor, Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, GECCO 2001, pp.  283–290. Morgan Kaufmann Publishers, San Francisco, CA, 2001.
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David Corne and Joshua D. Knowles. Some Multiobjective Optimizers are Better than Others. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03), pp.  2506–2512, Piscataway, NJ, December 2003. IEEE Press.
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David Corne and Joshua D. Knowles. No free lunch and free leftovers theorems for multiobjective optimisation problems. In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors, Evolutionary Multi-criterion Optimization, EMO 2003, volume 2632 of Lecture Notes in Computer Science, pp.  327–341. Springer, Heidelberg, Germany, 2003.
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David Corne, Joshua D. Knowles, and M. J. Oates. The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization. In M. Schoenauer et al., editors, Parallel Problem Solving from Nature – PPSN VI, volume 1917 of Lecture Notes in Computer Science, pp.  839–848. Springer, Heidelberg, Germany, 2000.
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David Corne and Alan Reynolds. Evaluating optimization algorithms: bounds on the performance of optimizers on unseen problems. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  707–710. ACM Press, New York, NY, 2011.
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Oscar Cordón, I. Fernández de Viana, Francisco Herrera, and L. Moreno. A New ACO Model Integrating Evolutionary Computation Concepts: The Best-Worst Ant System. In M. Dorigo et al., editors, Abstract proceedings of ANTS 2000 – From Ant Colonies to Artificial Ants: Second International Workshop on Ant Algorithms, pp.  22–29. IRIDIA, Université Libre de Bruxelles, Belgium, September 7–9 2000.
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First mention of the term hyper-heuristic.

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M. J. Crawley. The R Book. Wiley, 2nd edition, 2012.
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Nguyen Dang Thi Thanh and Patrick De Causmaecker. Motivations for the Development of a Multi-objective Algorithm Configurator. In B. Vitoriano, E. Pinson, and F. Valente, editors, ICORES 2014 - Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems, pp.  328–333. SciTePress, 2014.
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Nguyen Dang Thi Thanh and Patrick De Causmaecker. Characterization of Neighborhood Behaviours in a Multi-neighborhood Local Search Algorithm. In P. Festa, M. Sellmann, and J. Vanschoren, editors, Learning and Intelligent Optimization, 10th International Conference, LION 10, volume 10079 of Lecture Notes in Computer Science, pp.  234–239. Springer, Cham, Switzerland, 2016.
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Nguyen Dang and Patrick De Causmaecker. Analysis of Algorithm Components and Parameters: Some Case Studies. In N. F. Matsatsinis, Y. Marinakis, and P. M. Pardalos, editors, Learning and Intelligent Optimization, 13th International Conference, LION 13, volume 11968 of Lecture Notes in Computer Science, pp.  288–303. Springer, Cham, Switzerland, 2019.
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Modern high-performing algorithms are usually highly parameterised, and can be configured either manually or by an automatic algorithm configurator. The algorithm performance dataset obtained after the configuration step can be used to gain insights into how different algorithm parameters influence algorithm performance. This can be done by a number of analysis methods that exploit the idea of learning prediction models from an algorithm performance dataset and then using them for the data analysis on the importance of variables. In this paper, we demonstrate the complementary usage of three methods along this line, namely forward selection, fANOVA and ablation analysis with surrogates on three case studies, each of which represents some special situations that the analyses can fall into. By these examples, we illustrate how to interpret analysis results and discuss the advantage of combining different analysis methods.

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Nguyen Dang and Carola Doerr. Hyper-parameter tuning for the (1 + (λ, λ)) GA. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp.  889–897. ACM Press, New York, NY, 2019.
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Keywords: irace; theory

[1737]

Nguyen Dang Thi Thanh, Leslie Pérez Cáceres, Patrick De Causmaecker, and Thomas Stützle. Configuring irace Using Surrogate Configuration Benchmarks. In P. A. N. Bosman, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pp.  243–250. ACM Press, New York, NY, 2017.
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Keywords: irace

[1738]

Augusto Lopez Dantas and Aurora Trinidad Ramirez Pozo. A Meta-Learning Algorithm Selection Approach for the Quadratic Assignment Problem. In Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018), pp.  1–8, Piscataway, NJ, 2018. IEEE Press.
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Graeme C. Dandy and Matthew S. Gibbs. Optimizing System Operations and Water Quality. In P. Bizier and P. DeBarry, editors, Proceedings of World Water and Environmental Resources Congress. ASCE, Philadelphia, USA, 2003. on CD-ROM.
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Nguyen Dang Thi Thanh. Data analytics for algorithm design. PhD thesis, KU Leuven, Belgium, 2018.
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Supervised by Patrick De Causmaecker

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Fabio Daolio, Sébastien Verel, Gabriela Ochoa, and Marco Tomassini. Local Optima Networks and the Performance of Iterated Local Search. In T. Soule and J. H. Moore, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012, pp.  369–376. ACM Press, New York, NY, 2012.
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Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems (NeurIPS 33), pp.  9851–9864, 2020.
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Werner de Schaetzen, Dragan A. Savic, and Godfrey A. Walters. A genetic algorithm approach to pump scheduling in water supply. In V. Babovic and L. C. Larsen, editors, Hydroinformatics '98, pp.  897–899, Rotterdam, Balkema, 1998.
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Thomas Dean and Mark S. Boddy. An Analysis of Time-Dependent Planning. In H. E. Shrobe, T. M. Mitchell, and R. G. Smith, editors, Proceedings of the 7th National Conference on Artificial Intelligence, AAAI-88, pp.  49–54. AAAI Press/MIT Press, Menlo Park, CA, 1988.
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Keywords: anytime, performance profiles

[1745]

Angela Dean and Daniel Voss. Design and Analysis of Experiments. Springer, London, UK, 1999.
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[1746]

Kalyanmoy Deb. Introduction to evolutionary multiobjective optimization. In J. Branke, K. Deb, K. Miettinen, and R. Slowiński, editors, Multiobjective Optimization: Interactive and Evolutionary Approaches, volume 5252 of Lecture Notes in Computer Science, pp.  59–96. Springer, Heidelberg, Germany, 2008.
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In its current state, evolutionary multiobjective optimization (EMO) is an established field of research and application with more than 150 PhD theses, more than ten dedicated texts and edited books, commercial softwares and numerous freely downloadable codes, a biannual conference series running successfully since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. In this chapter, we provide a brief introduction to EMO principles, illustrate some EMO algorithms with simulated results, and outline the current research and application potential of EMO. For solving multiobjective optimization problems, EMO procedures attempt to find a set of well-distributed Pareto-optimal points, so that an idea of the extent and shape of the Pareto-optimal front can be obtained. Although this task was the early motivation of EMO research, EMO principles are now being found to be useful in various other problem solving tasks, enabling one to treat problems naturally as they are. One of the major current research thrusts is to combine EMO procedures with other multiple criterion decision making (MCDM) tools so as to develop hybrid and interactive multiobjective optimization algorithms for finding a set of trade-off optimal solutions and then choose a preferred solution for implementation. This chapter provides the background of EMO principles and their potential to launch such collaborative studies with MCDM researchers in the coming years.

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Kalyanmoy Deb. Multi-objective optimization. In E. K. Burke and G. Kendall, editors, Search Methodologies, pp.  273–316. Springer, Boston, MA, 2005.
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[1748]

Kalyanmoy Deb. Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester, UK, 2001.
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Kalyanmoy Deb and S. Agrawal. A Niched-Penalty Approach for Constraint Handling in Genetic Algorithms. In A. Dobnikar, N. C. Steele, D. W. Pearson, and R. F. Albrecht, editors, Artificial Neural Nets and Genetic Algorithms (ICANNGA-99), pp.  235–243. Springer Verlag, 1999.
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Keywords: polynomial mutation

[1750]

Kalyanmoy Deb, S. Agarwal, A. Pratap, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In M. Schoenauer et al., editors, Parallel Problem Solving from Nature – PPSN VI, volume 1917 of Lecture Notes in Computer Science, pp.  849–858. Springer, Heidelberg, Germany, 2000.
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Kalyanmoy Deb and Sachin Jain. Multi-Speed Gearbox Design Using Multi-Objective Evolutionary Algorithms. Technical Report 2002001, KanGAL, February 2002.
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Kalyanmoy Deb and Christie Myburgh. Breaking the billion-variable barrier in real-world optimization using a customized evolutionary algorithm. In T. Friedrich, F. Neumann, and A. M. Sutton, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2016, pp.  653–660. ACM Press, New York, NY, 2016.
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Kalyanmoy Deb and Ankur Sinha. Solving Bilevel Multi-Objective Optimization Problems Using Evolutionary Algorithms. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, Evolutionary Multi-criterion Optimization, EMO 2009, volume 5467 of Lecture Notes in Computer Science, pp.  110–124. Springer, Heidelberg, Germany, 2009.
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Kalyanmoy Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms. In M. Cattolico et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006, pp.  635–642. ACM Press, New York, NY, 2006.
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Proposed R-NSGA-II

[1755]

Kalyanmoy Deb, Rahul Tewari, Mayur Dixit, and Joydeep Dutta. Finding trade-off solutions close to KKT points using evolutionary multi-objective optimization. In Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007), pp.  2109–2116, Piscataway, NJ, 2007. IEEE Press.
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Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. Scalable Test Problems for Evolutionary Multi-Objective Optimization. Technical Report 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland, 2001. Do not cite this TR! It is incorrect and it is superseeded by [1757].
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Keywords: DTLZ benchmark

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Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. Scalable Test Problems for Evolutionary Multiobjective Optimization. In A. Abraham, L. Jain, and R. Goldberg, editors, Evolutionary Multiobjective Optimization, Advanced Information and Knowledge Processing, pp.  105–145. Springer, London, UK, January 2005.
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Keywords: DTLZ benchmark

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William A. Dees, Jr. and Patrick G. Karger. Automated Rip-up and Reroute Techniques. In DAC'82, Proceedings of the 19th Design Automation Workshop, pp.  432–439. IEEE Press, 1982.
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Matthijs L. den Besten. Simple Metaheuristics for Scheduling. PhD thesis, FB Informatik, TU Darmstadt, Germany, 2004.
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Roman Denysiuk, Lino Costa, and Isabel Espírito Santo. Many-objective optimization using differential evolution with variable-wise mutation restriction. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013, pp.  591–598. ACM Press, New York, NY, 2013.
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp.  248–255. IEEE, 2009.
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Marcelo De Souza and Marcus Ritt. An Automatically Designed Recombination Heuristic for the Test-Assignment Problem. In Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018), pp.  1–8, Piscataway, NJ, 2018. IEEE Press.
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Marcelo De Souza and Marcus Ritt. Automatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming. In A. Liefooghe and M. López-Ibáñez, editors, Proceedings of EvoCOP 2018 – 18th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 10782 of Lecture Notes in Computer Science, pp.  67–84. Springer, Heidelberg, Germany, 2018.
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Marcelo De Souza and Marcus Ritt. Hybrid Heuristic for Unconstrained Binary Quadratic Programming – Source Code of HHBQP. https://github.com/souzamarcelo/hhbqp, 2018.
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Marcelo De Souza, Marcus Ritt, Manuel López-Ibáñez, and Leslie Pérez Cáceres. ACVIZ: A Tool for the Visual Analysis of the Configuration of Algorithms with irace – Source Code. https://github.com/souzamarcelo/acviz, 2020.
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Marcelo De Souza, Marcus Ritt, Manuel López-Ibáñez, and Leslie Pérez Cáceres. ACVIZ: Algorithm Configuration Visualizations for irace: Supplementary material. http://doi.org/10.5281/zenodo.4714582, September 2020.
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Sophie Dewez. On the toll setting problem. PhD thesis, Faculté de Sciences, Université Libre de Bruxelles, 2014.
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Supervised by Dr. Martine Labbé

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Ilias Diakonikolas and Mihalis Yannakakis. Succinct approximate convex Pareto curves. In Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms, pp.  74–83. Society for Industrial and Applied Mathematics, 2008.
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Diego Díaz, Pablo Valledor, Paula Areces, Jorge Rodil, and Montserrat Suárez. An ACO Algorithm to Solve an Extended Cutting Stock Problem for Scrap Minimization in a Bar Mill. In M. Dorigo et al., editors, Swarm Intelligence, 9th International Conference, ANTS 2014, volume 8667 of Lecture Notes in Computer Science, pp.  13–24. Springer, Heidelberg, Germany, 2014.
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Luca Di Gaspero, Marco Chiarandini, and Andrea Schaerf. A Study on the Short-Term Prohibition Mechanisms in Tabu Search. In G. Brewka, S. Coradeschi, A. Perini, and P. Traverso, editors, Proceedings of the 17th European Conference on Artificial Intelligence, ECAI 2006, Riva del Garda, Italy, August29 - September 1, 2006, pp.  83–87. IOS Press, 2006.
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Luca Di Gaspero, Andrea Rendl, and Tommaso Urli. Constraint-Based Approaches for Balancing Bike Sharing Systems. In C. Schulte, editor, Principles and Practice of Constraint Programming, volume 8124 of Lecture Notes in Computer Science, pp.  758–773. Springer, Heidelberg, Germany, 2013.
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Keywords: F-race

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Luca Di Gaspero, Andrea Rendl, and Tommaso Urli. A Hybrid ACO+CP for Balancing Bicycle Sharing Systems. In M. J. Blesa, C. Blum, P. Festa, A. Roli, and M. Sampels, editors, Hybrid Metaheuristics, volume 7919 of Lecture Notes in Computer Science, pp.  198–212. Springer, Heidelberg, Germany, 2013.
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Keywords: F-race

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Daniel Doblas, Antonio J. Nebro, Manuel López-Ibáñez, José García-Nieto, and Carlos A. Coello Coello. Automatic Design of Multi-objective Particle Swarm Optimizers. In M. Dorigo, H. Hamann, M. López-Ibáñez, J. García-Nieto, A. Engelbrecht, C. Pinciroli, V. Strobel, and C. L. Camacho-Villalón, editors, Swarm Intelligence, 13th International Conference, ANTS 2022, volume 13491 of Lecture Notes in Computer Science, pp.  28–40. Springer, Cham, Switzerland, 2022.
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Pedro Domingos and Geoff Hulten. Mining high-speed data streams. In R. Ramakrishnan, S. J. Stolfo, R. J. Bayardo, and I. Parsa, editors, The 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp.  71–80. ACM Press, New York, NY, 2000.
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Marco Dorigo and Gianni A. Di Caro. The Ant Colony Optimization Meta-Heuristic. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pp.  11–32. McGraw Hill, London, UK, 1999.
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Marco Dorigo and L. M. Gambardella. Ant Colony System. Technical Report IRIDIA/96-05, IRIDIA, Université Libre de Bruxelles, Belgium, 1996.
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Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. The Ant System: An autocatalytic optimizing process. Technical Report 91-016 Revised, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1991.
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Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. Positive Feedback as a Search Strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1991.
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Marco Dorigo, Marco A. Montes de Oca, Sabrina Oliveira, and Thomas Stützle. Ant Colony Optimization. In J. J. Cochran, editor, Wiley Encyclopedia of Operations Research and Management Science, volume 1, pp.  114–125. John Wiley & Sons, 2011.
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Marco Dorigo and Thomas Stützle. The Ant Colony Optimization Metaheuristic: Algorithms, Applications and Advances. In F. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pp.  251–285. Kluwer Academic Publishers, Norwell, MA, 2002.
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Marco Dorigo and Thomas Stützle. Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.
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Marco Dorigo. Optimization, Learning and Natural Algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992. In Italian.
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Johann Dreo. Using performance fronts for parameter setting of stochastic metaheuristics. In F. Rothlauf, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2009, pp.  2197–2200. ACM Press, New York, NY, 2009.
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Johann Dreo, Carola Doerr, and Yann Semet. Coupling the design of benchmark with algorithm in landscape-aware solver design. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019, pp.  1419–1420. ACM Press, New York, NY, 2019.
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[1785]

Johann Dreo, Arnaud Liefooghe, Sébastien Verel, Marc Schoenauer, Juan-Julián Merelo, Alexandre Quemy, Benjamin Bouvier, and Jan Gmys. Paradiseo: from a modular framework for evolutionary computation to the automated design of metaheuristics: 22 years of Paradiseo. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021, pp.  1522–1530. ACM Press, New York, NY, 2021.
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Keywords: metaheuristics, evolutionary computation, software framework, automated algorithm design

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Johann Dreo and P. Siarry. A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions. In M. Dorigo et al., editors, Ant Algorithms, Third International Workshop, ANTS 2002, volume 2463 of Lecture Notes in Computer Science, pp.  216–221. Springer, Heidelberg, Germany, 2002.
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Johann Dreo. Adaptation de la métaheuristique des colonies de fourmis pour l'optimisation difficile en variables continues: Application en génie biologique et médical. PhD thesis, LERISS - Laboratoire d'étude et de recherche en instrumentation, signaux et systémesUniversité Paris XII Val de Marne, December 2003.
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Keywords: metaheuristic ; continuous optimization ; global optimization ; imagery ; registration ; ant colony algorithm ; estimation of distribution algorithm ; evolutionary computation ; métaheuristique ; optimisation continue ; optimisation globale ; imagerie ; biomédical ; recalage ; algorithme de colonie de fourmis ; algorithme à estimation de distribution ; algorithme évolutionnaire

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Stefan Droste, Thomas Jansen, and Ingo Wegener. A new framework for the valuation of algorithms for black-box-optimization. In K. A. De Jong, R. Poli, and J. E. Rowe, editors, Proceedings of the Seventh Workshop on Foundations of Genetic Algorithms (FOGA), pp.  253–270. Morgan Kaufmann Publishers, 2002.
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Hisao Ishibuchi, Lie Meng Pang, and Ke Shang. A new framework of evolutionary multi-objective algorithms with an unbounded external archive. In G. D. Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarín, and J. Lang, editors, Proceedings of the 24th European Conference on Artificial Intelligence (ECAI), volume 325 of Frontiers in Artificial Intelligence and Applications. IOS Press, 2020.
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Chris Drummond. Replicability is not Reproducibility: Nor is it Good Science. In Proceedings of the Evaluation Methods for Machine Learning Workshop at the 26th ICML, Montreal, Canada, 2009.
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Mădălina M. Drugan and Dirk Thierens. Path-Guided Mutation for Stochastic Pareto Local Search Algorithms. In R. Schaefer, C. Cotta, J. Kolodziej, and G. Rudolph, editors, Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture Notes in Computer Science, pp.  485–495. Springer, Heidelberg, Germany, 2010.
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Abraham Duarte, Jesús Sánchez-Oro, Nenad Mladenović, and Raca Todosijević. Variable Neighborhood Descent. In R. Martí, P. M. Pardalos, and M. G. C. Resende, editors, Handbook of Heuristics, pp.  341–367. Springer International Publishing, 2018.
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Jérémie Dubois-Lacoste. Weight Setting Strategies for Two-Phase Local Search: A Study on Biobjective Permutation Flowshop Scheduling. Technical Report TR/IRIDIA/2009-024, IRIDIA, Université Libre de Bruxelles, Belgium, 2009.
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Jérémie Dubois-Lacoste, Holger H. Hoos, and Thomas Stützle. On the Empirical Scaling Behaviour of State-of-the-art Local Search Algorithms for the Euclidean TSP. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  377–384. ACM Press, New York, NY, 2015.
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[1795]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling. In M. J. Blesa, C. Blum, L. Di Gaspero, A. Roli, M. Sampels, and A. Schaerf, editors, Hybrid Metaheuristics, volume 5818 of Lecture Notes in Computer Science, pp.  100–114. Springer, Heidelberg, Germany, 2009.
[ bib | DOI ]

[1796]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Supplementary material: Improving the Anytime Behavior of Two-Phase Local Search. http://iridia.ulb.ac.be/supp/IridiaSupp2010-012, 2010.
[ bib ]

[1797]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Supplementary material: A Hybrid TP+PLS Algorithm for Bi-objective Flow-shop Scheduling Problems. http://iridia.ulb.ac.be/supp/IridiaSupp2010-001, 2010.
[ bib ]

[1798]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Adaptive “Anytime” Two-Phase Local Search. In C. Blum and R. Battiti, editors, Learning and Intelligent Optimization, 4th International Conference, LION 4, volume 6073 of Lecture Notes in Computer Science, pp.  52–67. Springer, Heidelberg, Germany, 2010.
[ bib | DOI ]

[1799]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Automatic Configuration of State-of-the-art Multi-Objective Optimizers Using the TP+PLS Framework. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  2019–2026. ACM Press, New York, NY, 2011.
[ bib | DOI ]

[1800]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Pareto Local Search Algorithms for Anytime Bi-objective Optimization. In J.-K. Hao and M. Middendorf, editors, Proceedings of EvoCOP 2012 – 12th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 7245 of Lecture Notes in Computer Science, pp.  206–217. Springer, Heidelberg, Germany, 2012.
[ bib | DOI ]

[1801]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search. In E.-G. Talbi, editor, Hybrid Metaheuristics, volume 434 of Studies in Computational Intelligence, pp.  97–117. Springer Verlag, 2013.
[ bib | DOI | http ]

[1802]

Jérémie Dubois-Lacoste, Federico Pagnozzi, and Thomas Stützle. Supplementary material: An iterated greedy algorithm with optimization of partial solutions for the permutation flowshop problem. http://iridia.ulb.ac.be/supp/IridiaSupp2013-006, 2017.
[ bib ]

[1803]

Jérémie Dubois-Lacoste and Thomas Stützle. Tuning of a Stigmergy-based Traffic Light Controller as a Dynamic Optimization Problem. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017), pp.  1–8, Piscataway, NJ, 2017. IEEE Press.
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[1804]

Jérémie Dubois-Lacoste. A study of Pareto and Two-Phase Local Search Algorithms for Biobjective Permutation Flowshop Scheduling. Master's thesis, IRIDIA, Université Libre de Bruxelles, Belgium, 2009.
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[1805]

Jérémie Dubois-Lacoste. Effective Stochastic Local Search Algorithms For Bi-Objective Permutation Flowshop Scheduling. Rapport d'avancement de recherches présenté pour la formation doctorale en sciences de l'ingénieur, IRIDIA, Université Libre de Bruxelles, Belgium, 2010.
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[1806]

Jérémie Dubois-Lacoste. Anytime Local Search for Multi-Objective Combinatorial Optimization: Design, Analysis and Automatic Configuration. PhD thesis, IRIDIA, École polytechnique, Université Libre de Bruxelles, Belgium, 2014.
[ bib ]

Supervised by Thomas Stützle and Manuel López-Ibáñez

[1807]

Gunter Dueck, Martin Maehler, Johannes Schneider, Gerhard Schrimpf, and Hermann Stamm-Wilbrandt. Optimization with Ruin Recreate. US Patent 6,418,398 B1, July 2002. Filed on October 1, 1999 and granted on July 9, 2002; Assignee is IBM Corporation, Armonk, NY (US).
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[1808]

Irina Dumitrescu and Thomas Stützle. Combinations of Local Search and Exact Algorithms. In G. R. Raidl and J. Gottlieb, editors, Proceedings of EvoCOP 2003 – 3rd European Conference on Evolutionary Computation in Combinatorial Optimization, volume 2611 of Lecture Notes in Computer Science, pp.  211–223. Springer, Heidelberg, Germany, 2003.
[ bib | DOI ]

[1809]

Irina Dumitrescu and Thomas Stützle. Usage of Exact Algorithms to Enhance Stochastic Local Search Algorithms. In V. Maniezzo, T. Stützle, and S. Voß, editors, Matheuristics—Hybridizing Metaheuristics and Mathematical Programming, volume 10 of Annals of Information Systems, pp.  103–134. Springer, New York, NY, 2009.
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[1810]

Juan J. Durillo, José García-Nieto, Antonio J. Nebro, Carlos A. Coello Coello, Francisco Luna, and Enrique Alba. Multi-Objective Particle Swarm Optimizers: An Experimental Comparison. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, Evolutionary Multi-criterion Optimization, EMO 2009, volume 5467 of Lecture Notes in Computer Science, pp.  495–509. Springer, Heidelberg, Germany, 2009.
[ bib ]

Particle Swarm Optimization (PSO) has received increasing attention in the optimization research community since its first appearance in the mid-1990s. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOPSOs) can be found in the specialized literature. Unfortunately, no experimental comparisons have been made in order to clarify which MOPSO version shows the best performance. In this paper, we use a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) with the aim of analyzing the search capabilities of six representative state-of-the-art MOPSOs, namely, NSPSO, SigmaMOPSO, OMOPSO, AMOPSO, MOPSOpd, and CLMOPSO. We additionally propose a new MOPSO algorithm, called SMPSO, characterized by including a velocity constraint mechanism, obtaining promising results where the rest perform inadequately.

[1811]

Juan J. Durillo, Antonio J. Nebro, Francisco Luna, and Enrique Alba. On the Effect of the Steady-State Selection Scheme in Multi-Objective Genetic Algorithms. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, Evolutionary Multi-criterion Optimization, EMO 2009, volume 5467 of Lecture Notes in Computer Science, pp.  183–197. Springer, Heidelberg, Germany, 2009.
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[1812]

Cynthia Dwork, Ravi Kumar, Moni Naor, and D. Sivakumar. Rank aggregation methods for the Web. In V. Y. Shen, N. Saito, M. R. Lyu, and M. E. Zurko, editors, Proceedings of the Tenth International World Wide Web Conference, WWW 10, pp.  613–622. ACM Press, New York, NY, 2001.
[ bib | DOI ]

Keywords: Kemeny ranking,multi-word queries,rank aggregation,ranking functions,spam

[1813]

L. A. Rossman. EPANET 2 Users Manual. U.S. Environmental Protection Agency, Cincinnati, USA, 2000.
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L. A. Rossman. EPANET User's Guide. Risk Reduction Engineering Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, USA, 1994.
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L. A. Rossman. The EPANET Programmer's Toolkit for Analysis of Water Distribution Systems. In Proceedings of the Annual Water Resources Planning and Management Conference, Reston, USA, 1999. ASCE.
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[1816]

Russell C. Eberhart and J. Kennedy. A New Optimizer Using Particle Swarm Theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp.  39–43, 1995.
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[1817]

Katharina Eggensperger, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Efficient Benchmarking of Hyperparameter Optimizers via Surrogates. In B. Bonet and S. Koenig, editors, Proceedings of the AAAI Conference on Artificial Intelligence, pp.  1114–1120. AAAI Press, 2015.
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[1818]

Werner Ehm. Reproducibility from the perspective of meta-analysis. In H. Atmanspacher and S. Maasen, editors, Reproducibility – Principles, problems, practices and prospects, pp.  141–168. Wiley, 2016.
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Matthias Ehrgott and Xavier Gandibleux. Hybrid Metaheuristics for Multi-objective Combinatorial Optimization. In C. Blum, M. J. Blesa, A. Roli, and M. Sampels, editors, Hybrid Metaheuristics: An emergent approach for optimization, volume 114 of Studies in Computational Intelligence, pp.  221–259. Springer, Berlin, Germany, 2008.
[ bib | DOI ]

Many real-world optimization problems can be modelled as combinatorial optimization problems. Often, these problems are characterized by their large size and the presence of multiple, conflicting objectives. Despite progress in solving multi-objective combinatorial optimization problems exactly, the large size often means that heuristics are required for their solution in acceptable time. Since the middle of the nineties the trend is towards heuristics that “pick and choose” elements from several of the established metaheuristic schemes. Such hybrid approximation techniques may even combine exact and heuristic approaches. In this chapter we give an overview over approximation methods in multi-objective combinatorial optimization. We briefly summarize “classical” metaheuristics and focus on recent approaches, where metaheuristics are hybridized and/or combined with exact methods.

[1820]

Matthias Ehrgott. Multicriteria Optimization, volume 491 of Lecture Notes in Economics and Mathematical Systems. Springer, Berlin, Germany, 2000.
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[1821]

Matthias Ehrgott. Multicriteria Optimization. Springer, Berlin, Germany, 2nd edition, 2005.
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[1822]

Agoston E. Eiben, Mark Horvath, Wojtek Kowalczyk, and Martijn C. Schut. Reinforcement learning for online control of evolutionary algorithms. In International Workshop on Engineering Self-Organising Applications, pp.  151–160. Springer, 2006.
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Agoston E. Eiben and M. Jelasity. A critical note on experimental research methodology in EC. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC'02), pp.  582–587, Piscataway, NJ, 2002. IEEE Press.
[ bib | DOI ]

Discusses reproducibility, generalizability and separation between training (for tuning and experimentation) and testing instances (for comparisons).

[1824]

Agoston E. Eiben, Zbigniew Michalewicz, Marc Schoenauer, and James E. Smith. Parameter Control in Evolutionary Algorithms. In F. Lobo, C. F. Lima, and Z. Michalewicz, editors, Parameter Setting in Evolutionary Algorithms, pp.  19–46. Springer, Berlin, Germany, 2007.
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[1825]

Agoston E. Eiben and James E. Smith. Introduction to Evolutionary Computing. Springer, 2003.
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[1826]

Agoston E. Eiben and James E. Smith. Introduction to Evolutionary Computing. Natural Computing Series. Springer, 2nd edition, 2007.
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[1827]

Mohammed El-Abd. Opposition-based Artificial Bee Colony Algorithm. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  109–116. ACM Press, New York, NY, 2011.
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[1828]

Nada Elsokkary, Faisal Shah Khan, Davide La Torre, Travis S. Humble, and Joel Gottlieb. Financial Portfolio Management using D-Wave's Quantum Optimizer: The Case of Abu Dhabi Securities Exchange. Technical report, Oak Ridge National Lab, Oak Ridge, TN, USA, 2017.
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[1829]

Michael T. M. Emmerich, André H. Deutz, and J. W. Klinkenberg. Hypervolume-based expected improvement: Monotonicity properties and exact computation. In Proceedings of the 2011 Congress on Evolutionary Computation (CEC 2011), pp.  2147–2154, Piscataway, NJ, 2011. IEEE Press.
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Proposed Expected Hypervolume Improvement (EHVI)

[1830]

Michael T. M. Emmerich and Carlos M. Fonseca. Computing Hypervolume Contributions in Low Dimensions: Asymptotically Optimal Algorithm and Complexity Results. In R. H. C. Takahashi, K. Deb, E. F. Wanner, and S. Greco, editors, Evolutionary Multi-criterion Optimization, EMO 2011, volume 6576 of Lecture Notes in Computer Science, pp.  121–135. Springer, Berlin/Heidelberg, 2011.
[ bib | DOI ]

Given a finite set Y ⊂R^d of n mutually non-dominated vectors in d ≥2 dimensions, the hypervolume contribution of a point y ∈Y is the difference between the hypervolume indicator of Y and the hypervolume indicator of Y ∖ {y}. In multi-objective metaheuristics, hypervolume contributions are computed in several selection and bounded-size archiving procedures. This paper presents new results on the (time) complexity of computing all hypervolume contributions. It is proved that for d = 2 and 3 the problem has time complexity Θ(n logn), and, for d > 3, the time complexity is bounded below by Ω(n logn). Moreover, complexity bounds are derived for computing a single hypervolume contribution. A dimension sweep algorithm with time complexity O (n logn) and space complexity O(n) is proposed for computing all hypervolume contributions in three dimensions. It improves the complexity of the best known algorithm for d = 3 by a factor of √(n) . Theoretical results are complemented by performance tests on randomly generated test-problems.

[1831]

Stefan Eppe, Yves De Smet, and Thomas Stützle. A bi-objective optimization model to eliciting decision maker's preferences for the PROMETHEE II method. In R. I. Brafman, F. Roberts, and A. Tsoukiàs, editors, Algorithmic Decision Theory, Third International Conference, ADT 2011, volume 6992 of Lecture Notes in Artificial Intelligence, pp.  56–66. Springer, Heidelberg, Germany, 2011.
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Stefan Eppe, Manuel López-Ibáñez, Thomas Stützle, and Yves De Smet. An Experimental Study of Preference Model Integration into Multi-Objective Optimization Heuristics. In Proceedings of the 2011 Congress on Evolutionary Computation (CEC 2011), pp.  2751–2758, Piscataway, NJ, 2011. IEEE Press.
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David Eriksson, Michael Pearce, Jacob Gardner, Ryan D. Turner, and Matthias Poloczek. Scalable Global Optimization via Local Bayesian Optimization. In H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. B. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems (NeurIPS 32), pp.  5496–5507, 2019.
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Arxiv preprint arXiv: https://arxiv.org/abs/1910.01739

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Emre Ertin, Anthony N. Dean, Mathew L. Moore, and Kevin L. Priddy. Dynamic Optimization for Optimal Control of Water Distribution Systems. In K. L. Priddy, P. E. Keller, and P. J. Angeline, editors, Applications and Science of Computational Intelligence IV, Proceedings of SPIE, volume 4390, pp.  142–149, March 2001.
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V. Esat and M. Hall. Water resources system optimization using genetic algorithms. In A. Verwey, A. Minns, V. Babovic, and C. Maksimović, editors, Hydroinformatics'94, pp.  225–231, Balkema, Rotterdam, The Netherlands, 1994.
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Larry J. Eshelman and J. David Schaffer. Real-Coded Genetic Algorithms and Interval-Schemata. In D. Whitley, editor, Foundations of Genetic Algorithms (FOGA), pp.  187–202. Morgan Kaufmann Publishers, 1993.
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Larry J. Eshelman, A. Caruana, and J. David Schaffer. Biases in the Crossover Landscape. In J. D. Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA'89), pp.  86–91. Morgan Kaufmann Publishers, San Mateo, CA, 1989.
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Richard M. Everson, Jonathan E. Fieldsend, and Sameer Singh. Full Elite Sets for Multi-objective Optimisation. In Adaptive Computing in Design and Manufacture V, pp.  343–354. Springer, London, UK, 2002.
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Extended version published as [448]

[1839]

C. J. Eyckelhof and M. Snoek. Ant Systems for a Dynamic TSP: Ants Caught in a Traffic Jam. In M. Dorigo et al., editors, Ant Algorithms, Third International Workshop, ANTS 2002, volume 2463 of Lecture Notes in Computer Science, pp.  88–99. Springer, Heidelberg, Germany, 2002.
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[1840]

Stefan Falkner, Marius Thomas Lindauer, and Frank Hutter. SpySMAC: Automated configuration and performance analysis of SAT solvers. In M. Heule and S. Weaver, editors, Theory and Applications of Satisfiability Testing – SAT 2015, volume 9340 of Lecture Notes in Computer Science, pp.  215–222. Springer, Cham, Switzerland, 2015.
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[1841]

Jesús Guillermo Falcón-Cardona, Saúl Zapotecas-Martínez, and Abel García-Nájera. Pareto compliance from a practical point of view. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021, pp.  395–402. ACM Press, New York, NY, 2021.
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M. Farina and P. Amato. On the Optimal Solution Definition for Many-criteria Optimization Problems. In Proceedings of the NAFIPS-FLINT International Conference'2002, pp.  233–238, Piscataway, New Jersey, June 2002. IEEE Service Center.
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First to mention exponential number of nondominated solutions with respect to many objectives?

[1843]

D. Favaretto, E. Moretti, and Paola Pellegrini. On the explorative behavior of Max-Min Ant System. In T. Stützle, M. Birattari, and H. H. Hoos, editors, Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2009, volume 5752 of Lecture Notes in Computer Science, pp.  115–119. Springer, Heidelberg, Germany, 2009.
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Chris Fawcett, Malte Helmert, Holger H. Hoos, Erez Karpas, Gabriele Röger, and Jendrik Seipp. FD-Autotune: Domain-Specific Configuration using Fast-Downward. In E. Karpas, S. Jiménez Celorrio, and S. Kambhampati, editors, Proceedings of ICAPS-PAL11, 2011.
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Chris Fawcett and Holger H. Hoos. Analysing Differences between Algorithm Configurations through Ablation. In Proceedings of MIC 2013, the 10th Metaheuristics International Conference, pp.  123–132, 2013.
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Silvino Fernández, Segundo Álvarez, Diego Díaz, Miguel Iglesias, and Borja Ena. Scheduling a Galvanizing Line by Ant Colony Optimization. In M. Dorigo et al., editors, Swarm Intelligence, 9th International Conference, ANTS 2014, volume 8667 of Lecture Notes in Computer Science, pp.  146–157. Springer, Heidelberg, Germany, 2014.
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Silvino Fernández, Segundo Álvarez, Eneko Malatsetxebarria, Pablo Valledor, and Diego Díaz. Performance Comparison of Ant Colony Algorithms for the Scheduling of Steel Production Lines. In J. L. Jiménez Laredo, S. Silva, and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2015. ACM Press, New York, NY, 2015.
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Keywords: irace

[1848]

José C. Ferreira, Carlos M. Fonseca, and António Gaspar-Cunha. Methodology to select solutions from the Pareto-optimal set: a comparative study. In D. Thierens et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007, pp.  789–796. ACM Press, New York, NY, 2007.
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F. J. Ferri, P. Pudil, M. Hatef, and J. Kittler. Comparative study of techniques for large-scale feature selection. In E. S. Gelsema and L. S. Kanal, editors, Pattern Recognition in Practice IV, volume 16 of Machine Intelligence and Pattern Recognition, pp.  403–413. North-Holland, 1994.
[ bib | DOI ]

The combinatorial search problem arising in feature selection in high dimensional spaces is considered. Recently developed techniques based on the classical sequential methods and the (l, r) search called Floating search algorithms are compared against the Genetic approach to feature subset search. Both approaches have been designed with the view to give a good compromise between efficiency and effectiveness for large problems. The purpose of this paper is to investigate the applicability of these techniques to high dimensional problems of feature selection. The aim is to establish whether the properties inferred for these techniques from medium scale experiments involving up to a few tens of dimensions extend to dimensionalities of one order of magnitude higher. Further, relative merits of these techniques vis-a-vis such high dimensional problems are explored and the possibility of exploiting the best aspects of these methods to create a composite feature selection procedure with superior properties is considered.

Describes sequential forward / backward selection

[1850]

Silvino Fernández, Pablo Valledor, Diego Díaz, Eneko Malatsetxebarria, and Miguel Iglesias. Criticality of Response Time in the usage of Metaheuristics in Industry. In T. Friedrich, F. Neumann, and A. M. Sutton, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2016, pp.  937–940. ACM Press, New York, NY, 2016.
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Matthias Feurer and Frank Hutter. Hyperparameter Optimization. In F. Hutter, L. Kotthoff, and J. Vanschoren, editors, Automated Machine Learning, pp.  3–33. Springer, 2019.
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[1852]

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. Efficient and robust automated machine learning. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems (NIPS 28), pp.  2962–2970, 2015.
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[1853]

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. Auto-sklearn: Efficient and Robust Automated Machine Learning. In F. Hutter, L. Kotthoff, and J. Vanschoren, editors, Automated Machine Learning, pp.  113–134. Springer, 2019.
[ bib | DOI | epub ]

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on the Python machine learning package scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub Auto-sklearn, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won six out of ten phases of the first ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of Auto-sklearn.

[1854]

Álvaro Fialho, Raymond Ros, Marc Schoenauer, and Michèle Sebag. Comparison-based adaptive strategy selection with bandits in differential evolution. In R. Schaefer, C. Cotta, J. Kolodziej, and G. Rudolph, editors, Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture Notes in Computer Science, pp.  194–203. Springer, Heidelberg, Germany, 2010.
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Álvaro Fialho, Marc Schoenauer, and Michèle Sebag. Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the BBOB-2010 noiseless testbed. In M. Pelikan and J. Branke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2010, pp.  1535–1542. ACM Press, New York, NY, 2010.
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Álvaro Fialho, Marc Schoenauer, and Michèle Sebag. Toward comparison-based adaptive operator selection. In M. Pelikan and J. Branke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010, pp.  767–774. ACM Press, New York, NY, 2010.
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Proposed F-AUC and F-SR

[1857]

Álvaro Fialho. Adaptive operator selection for optimization. PhD thesis, Université Paris Sud-Paris XI, 2010.
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[1858]

Jonathan E. Fieldsend. University staff teaching allocation: formulating and optimising a many-objective problem. In P. A. N. Bosman, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pp.  1097–1104. ACM Press, New York, NY, 2017.
[ bib | DOI ]

Example of NSGA-III deteriorating.

[1859]

Jonathan E. Fieldsend and Richard M. Everson. Visualising high-dimensional Pareto relationships in two-dimensional scatterplots. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, editors, Evolutionary Multi-criterion Optimization, EMO 2013, volume 7811 of Lecture Notes in Computer Science, pp.  558–572. Springer, Heidelberg, Germany, 2013.
[ bib | DOI ]

[1860]

Jonathan E. Fieldsend. Data structures for non-dominated sets: implementations and empirical assessment of two decades of advances. In C. A. Coello Coello, editor, Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp.  489–497. ACM Press, New York, NY, 2020.
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unbounded archives

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Andreas Fink and Stefan Voß. HotFrame: A Heuristic Optimization Framework. In S. Voß and D. L. Woodruff, editors, Optimization Software Class Libraries, pp.  81–154. Kluwer Academic Publishers, Boston, MA, 2002.
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[1862]

Benjamin Fisset, Clarisse Dhaenens, and Laetitia Jourdan. MO-Mineclust: A Framework for Multi-Objective Clustering. In C. Dhaenens, L. Jourdan, and M.-E. Marmion, editors, Learning and Intelligent Optimization, 9th International Conference, LION 9, volume 8994 of Lecture Notes in Computer Science, pp.  293–305. Springer, Heidelberg, Germany, 2015.
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Keywords: irace

[1863]

Peter J. Fleming, Robin C. Purshouse, and Robert J. Lygoe. Many-objective optimization: An engineering design perspective. In C. A. Coello Coello, A. Hernández Aguirre, and E. Zitzler, editors, Evolutionary Multi-criterion Optimization, EMO 2005, volume 3410 of Lecture Notes in Computer Science, pp.  14–32. Springer, Berlin/Heidelberg, 2005.
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[1864]

Robin C. Purshouse, Cezar Jalbă, and Peter J. Fleming. Preference-Driven Co-Evolutionary Algorithms Show Promise for Many-Objective optimisation. In R. H. C. Takahashi, K. Deb, E. F. Wanner, and S. Greco, editors, Evolutionary Multi-criterion Optimization, EMO 2011, volume 6576 of Lecture Notes in Computer Science, pp.  136–150. Springer, Berlin/Heidelberg, 2011.
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Filippo Focacci, François Laburthe, and Andrea Lodi. Local Search and Constraint Programming. In F. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pp.  369–403. Kluwer Academic Publishers, Norwell, MA, 2002.
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Carlos M. Fonseca and Peter J. Fleming. On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers. In H.-M. Voigt et al., editors, Parallel Problem Solving from Nature – PPSN IV, volume 1141 of Lecture Notes in Computer Science, pp.  584–593. Springer, Heidelberg, Germany, 1996.
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Viviane Grunert da Fonseca and Carlos M. Fonseca. The Relationship between the Covered Fraction, Completeness and Hypervolume Indicators. In J.-K. Hao, P. Legrand, P. Collet, N. Monmarché, E. Lutton, and M. Schoenauer, editors, Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011, volume 7401 of Lecture Notes in Computer Science, pp.  25–36. Springer, Heidelberg, Germany, 2012.
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Carlos M. Fonseca, Viviane Grunert da Fonseca, and Luís Paquete. Exploring the Performance of Stochastic Multiobjective Optimisers with the Second-Order Attainment Function. In C. A. Coello Coello, A. Hernández Aguirre, and E. Zitzler, editors, Evolutionary Multi-criterion Optimization, EMO 2005, volume 3410 of Lecture Notes in Computer Science, pp.  250–264. Springer, Berlin/Heidelberg, 2005.
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The attainment function has been proposed as a measure of the statistical performance of stochastic multiobjective optimisers which encompasses both the quality of individual non-dominated solutions in objective space and their spread along the trade-off surface. It has also been related to results from random closed-set theory, and cast as a mean-like, first-order moment measure of the outcomes of multiobjective optimisers. In this work, the use of more informative, second-order moment measures for the evaluation and comparison of multiobjective optimiser performance is explored experimentally, with emphasis on the interpretability of the results.

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Carlos M. Fonseca, Andreia P. Guerreiro, Manuel López-Ibáñez, and Luís Paquete. On the Computation of the Empirical Attainment Function. In R. H. C. Takahashi, K. Deb, E. F. Wanner, and S. Greco, editors, Evolutionary Multi-criterion Optimization, EMO 2011, volume 6576 of Lecture Notes in Computer Science, pp.  106–120. Springer, Berlin/Heidelberg, 2011.
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The attainment function provides a description of the location of the distribution of a random non-dominated point set. This function can be estimated from experimental data via its empirical counterpart, the empirical attainment function (EAF). However, computation of the EAF in more than two dimensions is a non-trivial task. In this article, the problem of computing the empirical attainment function is formalised, and upper and lower bounds on the corresponding number of output points are presented. In addition, efficient algorithms for the two and three-dimensional cases are proposed, and their time complexities are related to lower bounds derived for each case.

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Carlos M. Fonseca, Luís Paquete, and Manuel López-Ibáñez. An improved dimension- sweep algorithm for the hypervolume indicator. In Proceedings of the 2006 Congress on Evolutionary Computation (CEC 2006), pp.  1157–1163, Piscataway, NJ, July 2006. IEEE Press.
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This paper presents a recursive, dimension-sweep algorithm for computing the hypervolume indicator of the quality of a set of n non-dominated points in d>2 dimensions. It improves upon the existing HSO (Hypervolume by Slicing Objectives) algorithm by pruning the recursion tree to avoid repeated dominance checks and the recalculation of partial hypervolumes. Additionally, it incorporates a recent result for the three-dimensional special case. The proposed algorithm achieves O(n^d-2 log n) time and linear space complexity in the worst-case, but experimental results show that the pruning techniques used may reduce the time complexity exponent even further.

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Jorge Ramón Fonseca Cacho and Kazem Taghva. The State of Reproducible Research in Computer Science. In S. Latifi, editor, 17th International Conference on Information Technology-New Generations (ITNG 2020), Advances in Intelligent Systems and Computing, pp.  519–524. Springer International Publishing, 2020.
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Reproducible research is the cornerstone of cumulative science and yet is one of the most serious crisis that we face today in all fields. This paper aims to describe the ongoing reproducible research crisis along with counter-arguments of whether it really is a crisis, suggest solutions to problems limiting reproducible research along with the tools to implement such solutions by covering the latest publications involving reproducible research.

Keywords: Docker, Improving transparency, OCR, Open science, Replicability, Reproducibility

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Alberto Franzin, Raphaël Gyory, Jean-Charles Nadé, Guillaume Aubert, Georges Klenkle, and Hughes Bersini. Philéas: Anomaly Detection for IoT Monitoring. In L. Cao, W. Kosters, and J. Lijffijt, editors, Proceedings of the 32nd Benelux Conference on Artificial Intelligence, BNAIC 2020, Leiden, The Netherlands, 19-20 November 2020, pp.  56–70, 2020.
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Jose M. Framiñán, Rainer Leisten, and Rubén Ruiz. Manufacturing Scheduling Systems: An Integrated View on Models, Methods, and Tools. Springer, New York, NY, 2014.
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Alberto Franzin and Thomas Stützle. Exploration of Metaheuristics through Automatic Algorithm Configuration Techniques and Algorithmic Frameworks. In T. Friedrich, F. Neumann, and A. M. Sutton, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2016, pp.  1341–1347. ACM Press, New York, NY, 2016.
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Alberto Franzin and Thomas Stützle. Comparison of Acceptance Criteria in Randomized Local Searches. In E. Lutton, P. Legrand, P. Parrend, N. Monmarché, and M. Schoenauer, editors, EA 2017: Artificial Evolution, volume 10764 of Lecture Notes in Computer Science, pp.  16–29. Springer, Heidelberg, Germany, 2017.
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Alberto Franzin and Thomas Stützle. Revisiting Simulated Annealing: a Component-Based Analysis: Supplementaty Material. http://iridia.ulb.ac.be/supp/IridiaSupp2018-001, 2018.
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Alberto Franzin and Thomas Stützle. A causal framework for understanding optimisation algorithms. In F. Heintz, M. Milano, and B. O'Sullivan, editors, Trustworthy AI – Integrating Learning, Optimization and Reasoning. TAILOR 2020, volume 12641 of Lecture Notes in Computer Science, pp.  140–145. Springer, Cham, Switzerland, 2021.
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Alberto Franzin and Thomas Stützle. A Landscape-based Analysis of Fixed Temperature and Simulated Annealing: Supplementaty Material. http://iridia.ulb.ac.be/supp/IridiaSupp2021-002, 2021.
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Tobias Friedrich, Andreas Göbel, Francesco Quinzan, and Markus Wagner. Heavy-Tailed Mutation Operators in Single-Objective Combinatorial Optimization. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, and D. Whitley, editors, Parallel Problem Solving from Nature – PPSN XV, volume 11101 of Lecture Notes in Computer Science, pp.  134–145. Springer, Cham, Switzerland, 2018.
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A core feature of evolutionary algorithms is their mutation operator. Recently, much attention has been devoted to the study of mutation operators with dynamic and non-uniform mutation rates. Following up on this line of work, we propose a new mutation operator and analyze its performance on the (1+1) Evolutionary Algorithm (EA). Our analyses show that this mutation operator competes with pre-existing ones, when used by the (1+1)-EA on classes of problems for which results on the other mutation operators are available. We present a “jump” function for which the performance of the (1+1)-EA using any static uniform mutation and any restart strategy can be worse than the performance of the (1+1)-EA using our mutation operator with no restarts. We show that the (1+1)-EA using our mutation operator finds a (1/3)-approximation ratio on any non-negative submodular function in polynomial time. This performance matches that of combinatorial local search algorithms specifically designed to solve this problem.

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Tobias Friedrich, Timo Kötzing, Martin S. Krejca, and Andrew M. Sutton. Robustness of Ant Colony Optimization to Noise. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  17–24. ACM Press, New York, NY, 2015.
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Keywords: ant colony optimization, noisy fitness, run time analysis, theory

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Tobias Friedrich, Timo Kötzing, and Markus Wagner. A Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search. In S. P. Singh and S. Markovitch, editors, Proceedings of the AAAI Conference on Artificial Intelligence, pp.  801–807. AAAI Press, February 2017.
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Tobias Friedrich, Francesco Quinzan, and Markus Wagner. Escaping Large Deceptive Basins of Attraction with Heavy-tailed Mutation Operators. In H. E. Aguirre and K. Takadama, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, pp.  293–300. ACM Press, New York, NY, 2018.
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Keywords: combinatorial optimization, heavy-tailed mutation, single-objective optimization, experiments-motivated theory, irace

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Michael Friendly. Statistical graphics for multivariate data. In SAS Conference Proceedings: SAS Users Group International 16 (SUGI 16), 1991.
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Noriyuki Fujimoto and Kouki Nanai. Solving QUBO with GPU parallel MOPSO. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021, pp.  1788–1794. ACM Press, New York, NY, 2021.
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Alex S. Fukunaga. Evolving Local Search Heuristics for SAT Using Genetic Programming. In K. Deb et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, Part II, volume 3103 of Lecture Notes in Computer Science, pp.  483–494. Springer, Heidelberg, Germany, 2004.
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Satisfiability testing (SAT) is a very active area of research today, with numerous real-world applications. We describe CLASS2.0, a genetic programming system for semi-automatically designing SAT local search heuristics. An empirical comparison shows that that the heuristics generated by our GP system outperform the state of the art human-designed local search algorithms, as well as previously proposed evolutionary approaches, with respect to both runtime as well as search efficiency (number of variable flips to solve a problem).

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Matteo Gagliolo and Catherine Legrand. Algorithm Survival Analysis. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pp.  161–184. Springer, Berlin/Heidelberg, 2010.
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Algorithm selection is typically based on models of algorithm performance,learned during a separate offline training sequence, which can be prohibitively expensive. In recent work, we adopted an online approach, in which models of the runtime distributions of the available algorithms are iteratively updated and used to guide the allocation of computational resources, while solving a sequence of problem instances. The models are estimated using survival analysis techniques, which allow us to reduce computation time, censoring the runtimes of the slower algorithms. Here, we review the statistical aspects of our online selection method, discussing the bias induced in the runtime distributions (RTD) models by the competition of different algorithms on the same problem instances.

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L. M. Gambardella and Marco Dorigo. Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning (ML-95), pp.  252–260. Morgan Kaufmann Publishers, Palo Alto, CA, 1995.
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L. M. Gambardella and Marco Dorigo. Solving Symmetric and Asymmetric TSPs by Ant Colonies. In T. Bäck, T. Fukuda, and Z. Michalewicz, editors, Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC'96), pp.  622–627. IEEE Press, Piscataway, NJ, 1996.
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L. M. Gambardella, Éric D. Taillard, and G. Agazzi. MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pp.  63–76. McGraw Hill, London, UK, 1999.
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Xavier Gandibleux, X. Delorme, and V. T'Kindt. An Ant Colony Optimisation Algorithm for the Set Packing Problem. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  49–60. Springer, Heidelberg, Germany, 2004.
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Xavier Gandibleux, N. Mezdaoui, and A. Fréville. A tabu search procedure to solve multiobjective combinatorial optimization problem. In R. Caballero, F. Ruiz, and R. Steuer, editors, Advances in Multiple Objective and Goal Programming, volume 455 of Lecture Notes in Economics and Mathematical Systems, pp.  291–300. Springer, Heidelberg, Germany, 1997.
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Xavier Gandibleux, H. Morita, and N. Katoh. Use of a genetic heritage for solving the assignment problem with two objectives. In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors, Evolutionary Multi-criterion Optimization, EMO 2003, volume 2632 of Lecture Notes in Computer Science, pp.  43–57. Springer, Heidelberg, Germany, 2003.
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Huiru Gao, Haifeng Nie, and Ke Li. Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons. In Proceedings of the 2019 Congress on Evolutionary Computation (CEC 2019), pp.  1750–1757, Piscataway, NJ, 2019. IEEE Press.
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Deon Garrett and Dipankar Dasgupta. Multiobjective landscape analysis and the generalized assignment problem. In V. Maniezzo, R. Battiti, and J.-P. Watson, editors, Learning and Intelligent Optimization, Second International Conference, LION 2, volume 5313 of Lecture Notes in Computer Science, pp.  110–124. Springer, Heidelberg, Germany, 2008.
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José García-Nieto, Esteban López-Camacho, María Jesús Godoy García, Antonio J. Nebro, Juan J. Durillo, and José F. Aldana-Montes. A study of archiving strategies in multi-objective PSO for molecular docking. In M. Dorigo, M. Birattari, X. Li, M. López-Ibáñez, K. Ohkura, C. Pinciroli, and T. Stützle, editors, Swarm Intelligence, 10th International Conference, ANTS 2016, volume 9882 of Lecture Notes in Computer Science, pp.  40–52. Springer, Heidelberg, Germany, 2016.
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Beatriz A. Garro, Humberto Sossa, and Roberto A. Vazquez. Evolving ant colony system for optimizing path planning in mobile robots. In Electronics, Robotics and Automotive Mechanics Conference, pp.  444–449, Los Alamitos, CA, 2007. IEEE Computer Society.
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Luca Di Gaspero and Andrea Schaerf. Easysyn++: A tool for automatic synthesis of stochastic local search algorithms. In T. Stützle, M. Birattari, and H. H. Hoos, editors, Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2007, volume 4638 of Lecture Notes in Computer Science, pp.  177–181. Springer, Heidelberg, Germany, 2007.
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Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Torsten Schaub, Marius Thomas Schneider, and Stefan Ziller. A portfolio solver for answer set programming: Preliminary report. In P. Calabar and T. C. Son, editors, Logic Programming and Nonmonotonic Reasoning, volume 8148 of Lecture Notes in Artificial Intelligence, pp.  352–357. Springer, Heidelberg, Germany, 2013.
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Peter Geibel. Reinforcement Learning for MDPs with Constraints. In J. Fürnkranz, T. Scheffer, and M. Spiliopoulou, editors, Machine Learning: ECML 2006, volume 4212 of Lecture Notes in Computer Science, pp.  646–653, 2006.
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In this article, I will consider Markov Decision Processes with two criteria, each defined as the expected value of an infinite horizon cumulative return. The second criterion is either itself subject to an inequality constraint, or there is maximum allowable probability that the single returns violate the constraint. I describe and discuss three new reinforcement learning approaches for solving such control problems.

Keywords: Safe RL

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Ian P. Gent, Stuart A. Grant, Ewen MacIntyre, Patrick Prosser, Paul Shaw, Barbara M. Smith, and Toby Walsh. How Not To Do It. Technical Report 97.27, School of Computer Studies, University of Leeds, May 1997.
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We give some dos and don'ts for those analysing algorithms experimentally. We illustrate these with many examples from our own research on the study of algorithms for NP-complete problems such as satisfiability and constraint satisfaction. Where we have not followed these maxims, we have suffered as a result.

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Ian P. Gent, Holger H. Hoos, P. Prosser, and T. Walsh. Morphing: Combining Structure and Randomness. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pp.  654–660, 1999.
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Michel Gendreau and Jean-Yves Potvin. Tabu Search. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 146 of International Series in Operations Research & Management Science, pp.  41–59. Springer, New York, NY, 2nd edition, 2010.
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Daniel Geschwender, Frank Hutter, Lars Kotthoff, Yuri Malitsky, Holger H. Hoos, and Kevin Leyton-Brown. Algorithm Configuration in the Cloud: A Feasibility Study. In P. M. Pardalos, M. G. C. Resende, C. Vogiatzis, and J. L. Walteros, editors, Learning and Intelligent Optimization, 8th International Conference, LION 8, volume 8426 of Lecture Notes in Computer Science, pp.  41–46. Springer, Heidelberg, Germany, 2014.
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Matthew S. Gibbs, Graeme C. Dandy, Holger R. Maier, and John B. Nixon. Calibrating genetic algorithms for water distribution system optimisation. In 7th Annual Symposium on Water Distribution Systems Analysis. ASCE, May 2005.
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Xavier Gillard, Vianney Coppé, Pierre Schaus, and André A. Cire. Improving the Filtering of Branch-And-Bound MDD Solver. In P. J. Stuckey, editor, Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2021, volume 12735 of Lecture Notes in Computer Science, pp.  231–247. Springer, Cham, Switzerland, 2021.
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[1927]

Xavier Gillard. Discrete Optimization with Decision Diagrams: Design of a Generic Solver, Improved Bounding Techniques, and Discovery of Good Feasible Solutions with Large Neighborhood Search. PhD thesis, Université Catholique de Louvain, 2022.
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T. Glasmachers. A fast incremental BSP tree archive for non-dominated points. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. M. Wiecek, Y. Jin, and C. Grimme, editors, Evolutionary Multi-criterion Optimization, EMO 2017, volume 10173 of Lecture Notes in Computer Science, pp.  252–266. Springer International Publishing, Cham, Switzerland, 2017.
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Fred Glover. A Template for Scatter Search and Path Relinking. In J.-K. Hao, E. Lutton, E. M. A. Ronald, M. Schoenauer, and D. Snyers, editors, Artificial Evolution, volume 1363 of Lecture Notes in Computer Science, pp.  1–51. Springer, Heidelberg, Germany, 1998.
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[1930]

Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp.  249–256, 2010.
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Fred Glover and Gary A. Kochenberger. Critical Even Tabu Search for Multidimensional Knapsack Problems. In I. H. Osman and J. P. Kelly, editors, Metaheuristics: Theory & Applications, pp.  407–427. Kluwer Academic Publishers, Norwell, MA, 1996.
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Fred Glover and Manuel Laguna. Tabu Search. Kluwer Academic Publishers, Boston, MA, 1997.
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Fred Glover, Manuel Laguna, and Rafael Martí. Scatter Search and Path Relinking: Advances and Applications. In F. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pp.  1–35. Kluwer Academic Publishers, Norwell, MA, 2002.
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Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D. Sculley. Google Vizier: A Service for Black-Box Optimization. In S. Matwin, S. Yu, and F. Farooq, editors, 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.  1487–1495. ACM Press, 2017.
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Elizabeth Ferreira Gouvêa Goldbarg, Givanaldo R. Souza, and Marco Cesar Goldbarg. Particle Swarm for the Traveling Salesman Problem. In J. Gottlieb and G. R. Raidl, editors, Proceedings of EvoCOP 2006 – 6th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 3906 of Lecture Notes in Computer Science, pp.  99–110. Springer, Heidelberg, Germany, 2006.
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David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston, MA, 1989.
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Fred E. Goldman and Larry W. Mays. The Application of Simulated Annealing to the Optimal Operation of Water Systems. In Proceedings of 26th Annual Water Resources Planning and Management Conference, Tempe, USA, June 2000. ASCE.
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Ralph E. Gomory. An algorithm for integer solutions to linear programs. In R. L. Graves and P. Wolfe, editors, Recent Advances in Mathematical Programming, pp.  260–302. McGraw Hill, New York, NY, 1963.
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Wenyin Gong, Álvaro Fialho, and Zhihua Cai. Adaptive strategy selection in differential evolution. In M. Pelikan and J. Branke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010, pp.  409–416. ACM Press, New York, NY, 2010.
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[1940]

M. Gorges-Schleuter. Asparagos96 and the Travelling Salesman Problem. In T. Bäck, Z. Michalewicz, and X. Yao, editors, Proceedings of the 1997 IEEE International Conference on Evolutionary Computation (ICEC'97), pp.  171–174. IEEE Press, Piscataway, NJ, 1997.
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[1941]

J. Gottlieb, M. Puchta, and Christine Solnon. A Study of Greedy, Local Search, and Ant Colony Optimization Approaches for Car Sequencing Problems. In S. Cagnoni et al., editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2003, volume 2611 of Lecture Notes in Computer Science, pp.  246–257. Springer, Heidelberg, Germany, 2003.
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[1942]

Jonathan Gratch and Gerald DeJong. COMPOSER: A probabilistic solution to the utility problem in speed-up learning. In W. R. Swartout, editor, Proceedings of the 10th National Conference on Artificial Intelligence, pp.  235–240. AAAI Press/MIT Press, Menlo Park, CA, 1992.
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Eearliest hyper-heuristic?

[1943]

Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. In Acoustics, speech and signal processing (icassp), 2013 ieee international conference on, pp.  6645–6649. IEEE, 2013.
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[1944]

Garrison W. Greenwood, Xiaobo Hu, and Joseph G. D'Ambrosio. Fitness functions for multiple objective optimization problems: Combining preferences with Pareto rankings. In R. K. Belew and M. D. Vose, editors, Foundations of Genetic Algorithms (FOGA), pp.  437–455. Morgan Kaufmann Publishers, 1996.
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[1945]

Salvatore Greco, Benedetto Matarazzo, and Roman Slowiński. Interactive evolutionary multiobjective optimization using dominance-based rough set approach. In H. Ishibuchi et al., editors, Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010), pp.  1–8, Piscataway, NJ, 2010. IEEE Press.
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[1946]

Viviane Grunert da Fonseca and Carlos M. Fonseca. A characterization of the outcomes of stochastic multiobjective optimizers through a reduction of the hitting function test sets. Technical report, CSI, Universidade do Algarve, 2004.
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Keywords: high-order EAF

[1947]

Viviane Grunert da Fonseca and Carlos M. Fonseca. The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pp.  103–130. Springer, Berlin/Heidelberg, 2010.
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[1948]

Viviane Grunert da Fonseca and Carlos M. Fonseca. The Relationship between the Covered Fraction, Completeness and Hypervolume Indicators. In J.-K. Hao, P. Legrand, P. Collet, N. Monmarché, E. Lutton, and M. Schoenauer, editors, Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011, volume 7401 of Lecture Notes in Computer Science. Springer, Heidelberg, Germany, 2012.
[ bib | DOI ]

This paper investigates the relationship between the covered fraction, completeness, and (weighted) hypervolume indicators for assessing the quality of the Pareto-front approximations produced by multiobjective optimizers. It is shown that these unary quality indicators are all, by definition, weighted Hausdorff measures of the intersection of the region attained by such an optimizer outcome in objective space with some reference set. Moreover, when the optimizer is stochastic, the indicators considered lead to real-valued random variables following particular probability distributions. Expressions for the expected value of these distributions are derived, and shown to be directly related to the first-order attainment function.

Keywords: hypervolume, empiricial attainment function

[1949]

Viviane Grunert da Fonseca, Carlos M. Fonseca, and Andreia O. Hall. Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function. In E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, and D. Corne, editors, Evolutionary Multi-criterion Optimization, EMO 2001, volume 1993 of Lecture Notes in Computer Science, pp.  213–225. Springer, Berlin/Heidelberg, 2001.
[ bib | DOI ]

The performance of stochastic optimisers can be assessed experimentally on given problems by performing multiple optimisation runs, and analysing the results. Since an optimiser may be viewed as an estimator for the (Pareto) minimum of a (vector) function, stochastic optimiser performance is discussed in the light of the criteria applicable to more usual statistical estimators. Multiobjective optimisers are shown to deviate considerably from standard point estimators, and to require special statistical methodology. The attainment function is formulated, and related results from random closed-set theory are presented, which cast the attainment function as a mean-like measure for the outcomes of multiobjective optimisers. Finally, a covariance-measure is defined, which should bring additional insight into the stochastic behaviour of multiobjective optimisers. Computational issues and directions for further work are discussed at the end of the paper.

Proposed looking at anytime behavior as a multi-objective problem

Keywords: EAF

[1950]

C. Guéret, Nicolas Monmarché, and M. Slimane. Ants Can Play Music. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  310–317. Springer, Heidelberg, Germany, 2004.
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[1951]

M. Guntsch and Jürgen Branke. New Ideas for Applying Ant Colony Optimization to the Probabilistic TSP. In S. Cagnoni et al., editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2003, volume 2611 of Lecture Notes in Computer Science, pp.  165–175. Springer, Heidelberg, Germany, 2003.
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[1952]

M. Guntsch and Martin Middendorf. Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP. In E. J. W. Boers et al., editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2001, volume 2037 of Lecture Notes in Computer Science, pp.  213–222. Springer, Heidelberg, Germany, 2001.
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[1953]

M. Guntsch and Martin Middendorf. A Population Based Approach for ACO. In S. Cagnoni et al., editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2002, volume 2279 of Lecture Notes in Computer Science, pp.  71–80. Springer, Heidelberg, Germany, 2002.
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[1954]

M. Guntsch and Martin Middendorf. Solving Multi-Objective Permutation Problems with Population Based ACO. In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors, Evolutionary Multi-criterion Optimization, EMO 2003, volume 2632 of Lecture Notes in Computer Science, pp.  464–478. Springer, Heidelberg, Germany, 2003.
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[1955]

M. Guntsch and Martin Middendorf. Applying Population Based ACO to Dynamic Optimization Problems. In M. Dorigo et al., editors, Ant Algorithms, Third International Workshop, ANTS 2002, volume 2463 of Lecture Notes in Computer Science, pp.  111–122. Springer, Heidelberg, Germany, 2002.
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[1956]

Gurobi. Gurobi Optimizer. http://www.gurobi.com/products/gurobi-optimizer, 2017.
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[1957]

D. Gusfield. Algorithms on Strings, Trees, and Sequences. In Computer Science and Computational Biology. Cambridge University Press, 1997.
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[1958]

Walter J. Gutjahr. S-ACO: An Ant-Based Approach to Combinatorial Optimization Under Uncertainty. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  238–249. Springer, Heidelberg, Germany, 2004.
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[1959]

Walter J. Gutjahr. A converging ACO algorithm for stochastic combinatorial optimization. In A. Albrecht and K. Steinhöfel, editors, Stochastic Algorithms: Foundations and Applications, volume 2827 of Lecture Notes in Computer Science, pp.  10–25. Springer Verlag, 2003.
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[1960]

Evert Haasdijk, Arif Atta-ul Qayyum, and Agoston E. Eiben. Racing to improve on-line, on-board evolutionary robotics. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  187–194. ACM Press, New York, NY, 2011.
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[1961]

S. Häckel, M. Fischer, D. Zechel, and T. Teich. A multi-objective ant colony approach for Pareto-optimization using dynamic programming. In C. Ryan, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008, pp.  33–40. ACM Press, New York, NY, 2008.
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[1962]

David Hadka, Patrick M. Reed, and T. W. Simpson. Diagnostic assessment of the Borg MOEA for many-objective product family design problems. In Proceedings of the 2012 Congress on Evolutionary Computation (CEC 2012), pp.  1–10, Piscataway, NJ, 2012. IEEE Press.
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[1963]

Apache Software Foundation. Hadoop, 2008.
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[1964]

Thomas M. Walski, Donald V. Chase, Dragan A. Savic, Walter Grayman, Stephen Beckwith, and Edmundo Koelle. Advanced Water Distribution Modeling and Management. Haestad Methods, Inc., Haestad Press, 1st edition, 2003.
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[1965]

George T. Hall, Pietro S. Oliveto, and Dirk Sudholt. On the impact of the cutoff time on the performance of algorithm configurators. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp.  907–915. ACM Press, New York, NY, 2019.
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Keywords: theory, automatic configuration, capping

[1966]

George T. Hall, Pietro S. Oliveto, and Dirk Sudholt. Fast Perturbative Algorithm Configurators. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. T. M. Emmerich, and H. Trautmann, editors, Parallel Problem Solving from Nature – PPSN XVI, volume 12269 of Lecture Notes in Computer Science, pp.  19–32. Springer, Cham, Switzerland, 2020.
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[1967]

George T. Hall, Pietro S. Oliveto, and Dirk Sudholt. Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. In C. A. Coello Coello, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020, pp.  823–831. ACM Press, New York, NY, 2020.
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[1968]

Greg Hamerly and Charles Elkan. Learning the k in k-means. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems (NIPS 16). MIT Press, 2003.
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[1969]

Hayfa Hammami and Thomas Stützle. A Computational Study of Neighborhood Operators for Job-Shop Scheduling Problems with Regular Objectives. In B. Hu and M. López-Ibáñez, editors, Proceedings of EvoCOP 2017 – 17th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 10197 of Lecture Notes in Computer Science, pp.  1–17. Springer, Heidelberg, Germany, 2017.
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[1970]

Michael Pilegaard Hansen. Tabu search for multiobjective optimization: MOTS. In J. Climaco, editor, Proceedings of the 13th International Conference on Multiple Criteria Decision Making (MCDM'97), pp.  574–586. Springer Verlag, 1997.
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[1971]

Nikolaus Hansen, Youhei Akimoto, and Petr Baudis. CMA-ES/pycma on Github. Zenodo, February 2019.
[ bib | DOI ]

[1972]

Nikolaus Hansen, Anne Auger, S. Finck, and R. Ros. Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental setup. Technical Report RR-6828, INRIA, France, 2009.
[ bib | supplementary material ]

[1973]

Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen Finck, and Petr Pošík. Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009. In M. Pelikan and J. Branke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2010, pp.  1689–1696. ACM Press, New York, NY, 2010.
[ bib | DOI ]

This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime of the algorithms, measured in number of function evaluations, is investigated and a connection between a single convergence graph and the runtime distribution is uncovered. Performance is investigated for different dimensions up to 40-D, for different target precision values, and in different subgroups of functions. Searching in larger dimension and multi-modal functions appears to be more difficult. The choice of the best algorithm also depends remarkably on the available budget of function evaluations.

Keywords: benchmarking, black-box optimization

[1974]

Nikolaus Hansen, Steffen Finck, Raymond Ros, and Anne Auger. Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions. Technical Report RR-6829, INRIA, France, 2009. Updated February 2010.
[ bib | epub ]

http://coco.gforge.inria.fr/bbob2012-downloads

[1975]

Michael Pilegaard Hansen and Andrzej Jaszkiewicz. Evaluating the quality of approximations to the non-dominated set. Technical Report IMM-REP-1998-7, Institute of Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark, 1998.
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Proposed R2 indicator

[1976]

Julia Handl and Joshua D. Knowles. Modes of Problem Solving with Multiple Objectives: Implications for Interpreting the Pareto Set and for Decision Making. In J. D. Knowles, D. Corne, K. Deb, and D. R. Chair, editors, Multiobjective Problem Solving from Nature, Natural Computing Series, pp.  131–151. Springer, Berlin/Heidelberg, 2008.
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[1977]

Pierre Hansen and Nenad Mladenović. Variable Neighborhood Search. In F. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pp.  145–184. Kluwer Academic Publishers, Norwell, MA, 2002.
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[1978]

Pierre Hansen, Nenad Mladenović, Jack Brimberg, and José A. Moreno Pérez. Variable Neighborhood Search. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 146 of International Series in Operations Research & Management Science, pp.  61–86. Springer, New York, NY, 2nd edition, 2010.
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[1979]

Nikolaus Hansen and Andreas Ostermeier. Adapting Arbitrary Normal Mutation Distributions in Evolution Strategies: The Covariance Matrix Adaptation. In T. Bäck, T. Fukuda, and Z. Michalewicz, editors, Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC'96), pp.  312–317. IEEE Press, Piscataway, NJ, 1996.
[ bib | DOI ]

A new formulation for coordinate system independent adaptation of arbitrary normal mutation distributions with zero mean is presented. This enables the evolution strategy (ES) to adapt the correct scaling of a given problem and also ensures invariance with respect to any rotation of the fitness function (or the coordinate system). Especially rotation invariance, here resulting directly from the coordinate system independent adaptation of the mutation distribution, is an essential feature of the ES with regard to its general applicability to complex fitness functions. Compared to previous work on this subject, the introduced formulation facilitates an interpretation of the resulting mutation distribution, making sensible manipulation by the user possible (if desired). Furthermore it enables a more effective control of the overall mutation variance (expected step length)

Proposed CMA-ES

Keywords: Evolution strategies, Evolutionary algorithms, self-adaptation, stochastic processes, Covariance matrix, matrix algebra, derandomised adaptation, mutation distribution, rotation invariance, electronic switching systems

[1980]

Thomas Hanne. Global Multiobjective Optimization with Evolutionary Algorithms: Selection Mechanisms and Mutation Control. In E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, and D. Corne, editors, Evolutionary Multi-criterion Optimization, EMO 2001, volume 1993 of Lecture Notes in Computer Science, pp.  197–212. Springer, Berlin/Heidelberg, 2001.
[ bib ]

[1981]

Michael Pilegaard Hansen. Metaheuristics for multiple objective combinatorial optimization. PhD thesis, Institute of Mathematical Modelling, Technical University of Denmark, March 1998.
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[1982]

Nikolaus Hansen. The CMA evolution strategy: a comparing review. In Towards a new evolutionary computation, pp.  75–102. Springer, 2006.
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[1983]

Nikolaus Hansen. Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. In F. Rothlauf, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2009, pp.  2389–2396. ACM Press, New York, NY, 2009.
[ bib ]

Keywords: bipop-cma-es

[1984]

Zhifeng Hao, Ruichu Cai, and Han Huang. An Adaptive Parameter Control Strategy for ACO. In Proceedings of the International Conference on Machine Learning and Cybernetics, pp.  203–206. IEEE Press, 2006.
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[1985]

Zhifeng Hao, Han Huang, Yong Qin, and Ruichu Cai. An ACO Algorithm with Adaptive Volatility Rate of Pheromone Trail. In Y. Shi, G. D. van Albada, J. Dongarra, and P. M. A. Sloot, editors, Computational Science – ICCS 2007, 7th International Conference, Proceedings, Part IV, volume 4490 of Lecture Notes in Computer Science, pp.  1167–1170. Springer, Heidelberg, Germany, 2007.
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[1986]

Jin-Kao Hao and Jêrome Pannier. Simulated Annealing and Tabu Search for Constraint Solving. In M. C. Golumbic et al., editors, Fifth International Symposium on Artificial Intelligence and Mathematics, AIM 1998, Fort Lauderdale, Florida, USA, January 4-6, 1998, pp.  1–15, 1998.
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[1987]

William D. Harvey and Matthew L. Ginsberg. Limited Discrepancy Search. In C. S. Mellish, editor, Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95), pp.  607–615. Morgan Kaufmann Publishers, 1995.
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[1988]

Emma Hart, Ian Miguel, Christopher Stone, and Quentin Renau. Towards optimisers that `Keep Learning'. In S. Silva and L. Paquete, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2023, pp.  1636–1638. ACM Press, New York, NY, 2023.
[ bib | DOI ]

ISBN: 979-8-4007-0120-7

[1989]

Hado van Hasselt, Arthur Guez, and David Silver. Deep Reinforcement Learning with Double Q-Learning. In D. Schuurmans and M. P. Wellman, editors, Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, 2016.
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[1990]

Verena Heidrich-Meisner and Christian Igel. Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search. In A. P. Danyluk, L. Bottou, and M. L. Littman, editors, Proceedings of the 26th International Conference on Machine Learning, ICML 2009, pp.  401–408, New York, NY, 2009. ACM Press.
[ bib | DOI ]

Keywords: automated algorithm configuration, CMA-ES, racing

[1991]

Keld Helsgaun. Source Code of the Lin-Kernighan-Helsgaun Traveling Salesman Heuristic. http://webhotel4.ruc.dk/~keld/research/LKH/, 2018.
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[1992]

Keld Helsgaun. Efficient Recombination in the Lin-Kernighan-Helsgaun Traveling Salesman Heuristic. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, and D. Whitley, editors, Parallel Problem Solving from Nature – PPSN XV, volume 11101 of Lecture Notes in Computer Science, pp.  95–107. Springer, Cham, Switzerland, 2018.
[ bib | DOI ]

[1993]

Pascal van Hentenryck. The OPL optimization programming language. MIT Press, Cambridge, MA, 1999.
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[1994]

Daniel Hennes and Dario Izzo. Interplanetary trajectory planning with Monte Carlo tree search. In Q. Yang and M. Wooldridge, editors, Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15), pp.  769–775. IJCAI/AAAI Press, Menlo Park, CA, 2015.
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[1995]

Daniel Hennes, Dario Izzo, and Damon Landau. Fast approximators for optimal low-thrust hops between main belt asteroids. In X. Chen and A. Stafylopatis, editors, Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, pp.  1–7, 2016.
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[1996]

Darrall Henderson, Sheldon H. Jacobson, and Alan W. Johnson. The Theory and Practice of Simulated Annealing. In F. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pp.  287–319. Springer, Boston, MA, 2003.
[ bib | DOI ]

[1997]

Pascal van Hentenryck and Laurent D. Michel. Constraint-based Local Search. MIT Press, Cambridge, MA, 2005.
[ bib ]

[1998]

Pascal van Hentenryck and Laurent D. Michel. Synthesis of constraint-based local search algorithms from high-level models. In R. C. Holte and A. Howe, editors, Proceedings of the AAAI Conference on Artificial Intelligence, pp.  273–278. AAAI Press/MIT Press, Menlo Park, CA, 2007.
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[1999]

R. Herbrich, T. Graepel, and K. Obermayer. Support vector learning for ordinal regression. In ICANN'99: Proceedings of the 9th International Conference on Artificial Neural Networks, pp.  97–102, 1999.
[ bib | DOI ]

We investigate the problem of predicting variables of ordinal scale. This task is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. The approach presented is based on a mapping from objects to scalar utility values. Similar to support vector methods we derive a new learning algorithm for the task of ordinal regression based on large margin rank boundaries. We give experimental results for an information retrieval task: learning the order of documents with respect to an initial query. Experimental results indicate that the presented algorithm outperforms more naive approaches to ordinal regression such as support vector classification and support vector regression in the case of more than two ranks.

Proposed the pairwise transform for learning-to-rank

Keywords: support vector machine;metric regression;support vector learning;ordinal regression;information retrieval;risk functional;machine learning;pattern classification;

[2000]

Francisco Herrera, Manuel Lozano, and Daniel Molina. Test suite for the special issue of Soft Computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems. http://sci2s.ugr.es/eamhco/, 2010.
[ bib ]

Keywords: SOCO benchmark

[2001]

Carlos Hernández and Oliver Schütze. A bounded archive based for bi-objective problems based on distance and e-dominance to avoid cyclic behavior. In J. E. Fieldsend and M. Wagner, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022, pp.  583–591. ACM Press, New York, NY, 2022.
[ bib | DOI ]

[2002]

Daniel P Heyman and Matthew J Sobel. Stochastic models in operations research: stochastic optimization, volume 2. Courier Corporation, 2003.
[ bib ]

[2003]

J. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
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[2004]

Myle Hollander and Douglas A. Wolfe. Nonparametric statistical inference. John Wiley & Sons, New York, NY, 1973. Second edition (1999).
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[2005]

Giles Hooker. Discovering Additive Structure in Black Box Functions. In W. Kim, R. Kohavi, J. Gehrke, and W. DuMouchel, editors, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD'04, pp.  575–580. ACM Press, New York, NY, 2004.
[ bib | DOI ]

Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that drive it. A common approach to interpretation is to plot the dependence of a learned function on one or two predictors. We present a method that seeks not to display the behavior of a function, but to evaluate the importance of non-additive interactions within any set of variables. Should the function be close to a sum of low dimensional components, these components can be viewed and even modeled parametrically. Alternatively, the work here provides an indication of where intrinsically high-dimensional behavior takes place.The calculations used in this paper correspond closely with the functional ANOVA decomposition; a well-developed construction in Statistics. In particular, the proposed score of interaction importance measures the loss associated with the projection of the prediction function onto a space of additive models. The algorithm runs in linear time and we present displays of the output as a graphical model of the function for interpretation purposes.

Keywords: diagnostics, functional ANOVA, feature selection, interpretation, visualization, additive models, draphical models

[2006]

Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown. Automated Configuration and Selection of SAT Solvers. In Handbook of Satisfiability, pp.  481–507. IOS Press, February 2021.
[ bib | DOI ]

[2007]

Holger H. Hoos and Thomas Stützle. Stochastic Local Search: Foundations and Applications. Elsevier, Amsterdam, The Netherlands, 2004.
[ bib ]

[2008]

Holger H. Hoos and Thomas Stützle. Stochastic Local Search—Foundations and Applications. Morgan Kaufmann Publishers, San Francisco, CA, 2005.
[ bib ]

[2009]

Holger H. Hoos and Thomas Stützle. Evaluating Las Vegas Algorithms — Pitfalls and Remedies. In G. F. Cooper and S. Moral, editors, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp.  238–245. Morgan Kaufmann Publishers, San Francisco, CA, 1998.
[ bib ]

[2010]

Holger H. Hoos. Programming by Optimisation: Towards a new Paradigm for Developing High-Performance Software. In MIC 2011, the 9th Metaheuristics International Conference, 2011. Plenary talk.
[ bib | http ]

[2011]

Holger H. Hoos. Automated Algorithm Configuration and Parameter Tuning. In Y. Hamadi, E. Monfroy, and F. Saubion, editors, Autonomous Search, pp.  37–71. Springer, Berlin, Germany, 2012.
[ bib | DOI ]

[2012]

Christian Horoba and Frank Neumann. Benefits and drawbacks for the use of epsilon-dominance in evolutionary multi-objective optimization. In C. Ryan, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008, pp.  641–648. ACM Press, New York, NY, 2008.
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Proposed ε-box

[2013]

J. Horn, N. Nafpliotis, and David E. Goldberg. A niched Pareto genetic algorithm for multiobjective optimization. In Proceedings of the 1994 World Congress on Computational Intelligence (WCCI 1994), pp.  82–87, Piscataway, NJ, June 1994. IEEE Press.
[ bib | DOI ]

[2014]

Kenneth Hoste and Lieven Eeckhout. Cole: Compiler Optimization Level Exploration. In M. L. Soffa and E. Duesterwald, editors, Proceedings of the 6th Annual IEEE/ACM International Symposium on Code Generation and Optimization, CGO '08, pp.  165–174, New York, NY, 2008. ACM Press.
[ bib | DOI ]

[2015]

Han Huang, Xiaowei Yang, Zhifeng Hao, and Ruichu Cai. A Novel ACO Algorithm with Adaptive Parameter. In D.-S. Huang, K. Li, and G. W. Irwin, editors, International Conference on Computational Science (3), volume 4115 of Lecture Notes in Computer Science, pp.  12–21. Springer, Heidelberg, Germany, 2006.
[ bib ]

[2016]

Kuo-Si Huang, Chang-Biau Yang, and Kuo tsung Tseng. Fast algorithms for finding the common subsequences of multiple sequences. In Proceedings of the International Computer Symposium, pp.  1006–1011. IEEE Press, 2004.
[ bib ]

[2017]

Evan J. Hughes. Multiple single objective Pareto sampling. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03), pp.  2678–2684, Piscataway, NJ, December 2003. IEEE Press.
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[2018]

Evan J. Hughes. MSOPS-II: A general-purpose many-objective optimiser. In Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007), pp.  3944–3951, Piscataway, NJ, 2007. IEEE Press.
[ bib ]

[2019]

Evan J. Hughes. Many-objective directed evolutionary line search. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  761–768. ACM Press, New York, NY, 2011.
[ bib ]

[2020]

Maura Hunt and Manuel López-Ibáñez. Modeling a Decision-Maker in Goal Programming by means of Computational Rationality. In I. Palomares, editor, International Alan Turing Conference on Decision Support and Recommender systems, pp.  17–20, London, UK, November 21–22 2019. Alan Turing Institute.
[ bib | epub ]

This paper extends a simulation of cognitive mechanisms in the context of multi-criteria decision-making by using ideas from computational rationality. Specifically, this paper improves the simulation of a human decision-maker (DM) by considering how resource constraints impact their evaluation process in an interactive Goal Programming problem. Our analysis confirms and emphasizes a previous simulation study by showing key areas that could be effected by cognitive mechanisms. While the results are promising, the effects should be validated by future experiments with human DMs.

[2021]

Mohamed Saifullah Hussin and Thomas Stützle. Hierarchical Iterated Local Search for the Quadratic Assignment Problem. In M. J. Blesa, C. Blum, L. Di Gaspero, A. Roli, M. Sampels, and A. Schaerf, editors, Hybrid Metaheuristics, volume 5818 of Lecture Notes in Computer Science, pp.  115–129. Springer, Heidelberg, Germany, 2009.
[ bib | DOI ]

[2022]

Frank Hutter, Domagoj Babić, Holger H. Hoos, and Alan J. Hu. Boosting Verification by Automatic Tuning of Decision Procedures. In J. Baumgartner and M. Sheeran, editors, FMCAD'07: Proceedings of the 7th International Conference Formal Methods in Computer Aided Design, pp.  27–34, Austin, Texas, USA, 2007. IEEE Computer Society, Washington, DC, USA.
[ bib ]

[2023]

Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, and Kevin P. Murphy. An experimental investigation of model-based parameter optimisation: SPO and beyond. In F. Rothlauf, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009, pp.  271–278. ACM Press, New York, NY, 2009.
[ bib | DOI ]

[2024]

Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Automated Configuration of Mixed Integer Programming Solvers. In A. Lodi, M. Milano, and P. Toth, editors, Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010, volume 6140 of Lecture Notes in Computer Science, pp.  186–202. Springer, Heidelberg, Germany, 2010.
[ bib | DOI ]

Keywords: MIP, ParamILS

[2025]

Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Sequential Model-Based Optimization for General Algorithm Configuration. In C. A. Coello Coello, editor, Learning and Intelligent Optimization, 5th International Conference, LION 5, volume 6683 of Lecture Notes in Computer Science, pp.  507–523. Springer, Heidelberg, Germany, 2011.
[ bib | DOI ]

Keywords: SMAC,ROAR

[2026]

Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Parallel Algorithm Configuration. In Y. Hamadi and M. Schoenauer, editors, Learning and Intelligent Optimization, 6th International Conference, LION 6, volume 7219 of Lecture Notes in Computer Science, pp.  55–70. Springer, Heidelberg, Germany, 2012.
[ bib ]

[2027]

Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Identifying Key Algorithm Parameters and Instance Features using Forward Selection. In P. M. Pardalos and G. Nicosia, editors, Learning and Intelligent Optimization, 7th International Conference, LION 7, volume 7997 of Lecture Notes in Computer Science, pp.  364–381. Springer, Heidelberg, Germany, 2013.
[ bib | DOI ]

Most state-of-the-art algorithms for large-scale optimization problems expose free parameters, giving rise to combinatorial spaces of possible configurations. Typically, these spaces are hard for humans to understand. In this work, we study a model-based approach for identifying a small set of both algorithm parameters and instance features that suffices for predicting empirical algorithm performance well. Our empirical analyses on a wide variety of hard combinatorial problem benchmarks spanning SAT, MIP, and TSP show that–for parameter configurations sampled uniformly at random–very good performance predictions can typically be obtained based on just two key parameters, and that similarly, few instance features and algorithm parameters suffice to predict the most salient algorithm performance characteristics in the combined configuration/feature space. We also use these models to identify settings of these key parameters that are predicted to achieve the best overall performance, both on average across instances and in an instance-specific way. This serves as a further way of evaluating model quality and also provides a tool for further understanding the parameter space. We provide software for carrying out this analysis on arbitrary problem domains and hope that it will help algorithm developers gain insights into the key parameters of their algorithms, the key features of their instances, and their interactions.

Keywords: parameter importance

[2028]

Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. An Efficient Approach for Assessing Hyperparameter Importance. In E. P. Xing and T. Jebara, editors, Proceedings of the 31st International Conference on Machine Learning, ICML 2014, volume 32, pp.  754–762. PMLR, 2014.
[ bib | http ]

Keywords: fANOVA, parameter importance

[2029]

Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, and Kevin Murphy. Time-Bounded Sequential Parameter Optimization. In C. Blum and R. Battiti, editors, Learning and Intelligent Optimization, 4th International Conference, LION 4, volume 6073 of Lecture Notes in Computer Science, pp.  281–298. Springer, Heidelberg, Germany, 2010.
[ bib | DOI ]

[2030]

Frank Hutter, Holger H. Hoos, and Thomas Stützle. Automatic Algorithm Configuration Based on Local Search. In R. C. Holte and A. Howe, editors, Proceedings of the AAAI Conference on Artificial Intelligence, pp.  1152–1157. AAAI Press/MIT Press, Menlo Park, CA, 2007.
[ bib ]

[2031]

Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Thomas Lindauer, Holger H. Hoos, Kevin Leyton-Brown, and Thomas Stützle. AClib: A Benchmark Library for Algorithm Configuration. In P. M. Pardalos, M. G. C. Resende, C. Vogiatzis, and J. L. Walteros, editors, Learning and Intelligent Optimization, 8th International Conference, LION 8, volume 8426 of Lecture Notes in Computer Science, pp.  36–40. Springer, Heidelberg, Germany, 2014.
[ bib | DOI ]

[2032]

Frank Hutter. SAT benchmarks used in automated algorithm configuration. http://www.cs.ubc.ca/labs/beta/Projects/AAC/SAT-benchmarks.html, 2007.
[ bib ]

[2033]

Frank Hutter. Automated Configuration of Algorithms for Solving Hard Computational Problems. PhD thesis, University of British Columbia, Department of Computer Science, Vancouver, Canada, October 2009.
[ bib ]

[2034]

Zhiyuan Liu and Jian Tang. IJCAI 2021 Reproducibility Guidelines, 35th International Joint Conference on Artificial Intelligence. https://ijcai-21.org/wp-content/uploads/2020/12/20201226-IJCAI-Reproducibility.pdf, 2021.
[ bib ]

[2035]

Jérémie Humeau, Arnaud Liefooghe, El-Ghazali Talbi, and Sébastien Verel. ParadisEO-MO: From Fitness Landscape Analysis to Efficient Local Search Algorithms. Rapport de recherche RR-7871, INRIA, France, 2012.
[ bib | epub ]

[2036]

Mauro Birattari. The race Package for R: Racing Methods for the Selection of the Best. Technical Report TR/IRIDIA/2003-037, IRIDIA, Université Libre de Bruxelles, Belgium, 2003.
[ bib ]

[2037]

Mauro Birattari. On the Estimation of the Expected Performance of a Metaheuristic on a Class of Instances. How Many Instances, How Many Runs? Technical Report TR/IRIDIA/2004-001, IRIDIA, Université Libre de Bruxelles, Belgium, 2004.
[ bib ]

[2038]

Krzysztof Socha and Marco Dorigo. Ant Colony Optimization for Mixed-Variable Optimization Problems. Technical Report TR/IRIDIA/2007-019, IRIDIA, Université Libre de Bruxelles, Belgium, October 2007.
[ bib ]

[2039]

Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization. Technical Report TR/IRIDIA/2009-015, IRIDIA, Université Libre de Bruxelles, Belgium, May 2009. Published as a book chapter [2228].
[ bib ]

[2040]

Manuel López-Ibáñez and Thomas Stützle. An Analysis of Algorithmic Components for Multiobjective Ant Colony Optimization: A Case Study on the Biobjective TSP. Technical Report TR/IRIDIA/2009-019, IRIDIA, Université Libre de Bruxelles, Belgium, June 2009. Published in the proceedings of Evolution Artificielle, 2009 [2235].
[ bib ]

[2041]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling. Technical Report TR/IRIDIA/2009-020, IRIDIA, Université Libre de Bruxelles, Belgium, June 2009. Published in the proceedings of Hybrid Metaheuristics 2009 [1795].
[ bib | http ]

[2042]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Adaptive “Anytime” Two-Phase Local Search. Technical Report TR/IRIDIA/2009-026, IRIDIA, Université Libre de Bruxelles, Belgium, 2010. Published in the proceedings of LION 4 [1798].
[ bib | http ]

[2043]

Thomas Stützle, Manuel López-Ibáñez, Paola Pellegrini, Michael Maur, Marco A. Montes de Oca, Mauro Birattari, and Marco Dorigo. Parameter Adaptation in Ant Colony Optimization. Technical Report TR/IRIDIA/2010-002, IRIDIA, Université Libre de Bruxelles, Belgium, January 2010. Published as a book chapter [2600].
[ bib ]

[2044]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. A Hybrid TP+PLS Algorithm for Bi-objective Flow-Shop Scheduling Problems. Technical Report TR/IRIDIA/2010-019, IRIDIA, Université Libre de Bruxelles, Belgium, 2010. Published in Computers & Operations Research [395].
[ bib | http ]

[2045]

M. S. Hussin and Thomas Stützle. Tabu Search vs. Simulated Annealing for Solving Large Quadratic Assignment Instances. Technical Report TR/IRIDIA/2010-020, IRIDIA, Université Libre de Bruxelles, Belgium, 2010.
[ bib ]

[2046]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, and Thomas Stützle. Improving the Anytime Behavior of Two-Phase Local Search. Technical Report TR/IRIDIA/2010-022, IRIDIA, Université Libre de Bruxelles, Belgium, 2010. Published in Annals of Mathematics and Artificial Intelligence [394].
[ bib | http ]

[2047]

Manuel López-Ibáñez, Joshua D. Knowles, and Marco Laumanns. On Sequential Online Archiving of Objective Vectors. Technical Report TR/IRIDIA/2011-001, IRIDIA, Université Libre de Bruxelles, Belgium, 2011. This is a revised version of the paper published in EMO 2011 [2221].
[ bib | http ]

[2048]

Mauro Birattari, Marco Chiarandini, Marco Saerens, and Thomas Stützle. Learning graphical models for parameter tuning. Technical Report TR/IRIDIA/2011-002, IRIDIA, Université Libre de Bruxelles, Belgium, 2011.
[ bib | http ]

[2049]

Manuel López-Ibáñez and Thomas Stützle. The Automatic Design of Multi-Objective Ant Colony Optimization Algorithms. Technical Report TR/IRIDIA/2011-003, IRIDIA, Université Libre de Bruxelles, Belgium, 2011. Published in IEEE Transactions on Evolutionary Computation [876].
[ bib | http ]

[2050]

Tianjun Liao, Daniel Molina, Marco A. Montes de Oca, and Thomas Stützle. A Note on the Effects of Enforcing Bound Constraints on Algorithm Comparisons using the IEEE CEC'05 Benchmark Function Suite. Technical Report TR/IRIDIA/2011-010, IRIDIA, Université Libre de Bruxelles, Belgium, 2011. Published in Evolutionary Computation [839].
[ bib | http ]

[2051]

Tianjun Liao, Daniel Molina, Marco A. Montes de Oca, and Thomas Stützle. Computational Results for an Automatically Tuned IPOP-CMA-ES on the CEC'05 Benchmark Set. Technical Report TR/IRIDIA/2011-022, IRIDIA, Université Libre de Bruxelles, Belgium, 2011.
[ bib ]

[2052]

Manuel López-Ibáñez and Thomas Stützle. Automatically Improving the Anytime Behaviour of Optimisation Algorithms. Technical Report TR/IRIDIA/2012-012, IRIDIA, Université Libre de Bruxelles, Belgium, May 2012. Published in European Journal of Operational Research [877].
[ bib ]

[2053]

Andreea Radulescu, Manuel López-Ibáñez, and Thomas Stützle. Automatically Improving the Anytime Behaviour of Multiobjective Evolutionary Algorithms. Technical Report TR/IRIDIA/2012-019, IRIDIA, Université Libre de Bruxelles, Belgium, 2012. Published in the proceedings of EMO 2013 [2459].
[ bib ]

[2054]

Tianjun Liao, Thomas Stützle, Marco A. Montes de Oca, and Marco Dorigo. A Unified Ant Colony Optimization Algorithm for Continuous Optimization. Technical Report TR/IRIDIA/2013-002, IRIDIA, Université Libre de Bruxelles, Belgium, 2013.
[ bib ]

[2055]

Franco Mascia, Manuel López-Ibáñez, Jérémie Dubois-Lacoste, and Thomas Stützle. Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools. Technical Report TR/IRIDIA/2013-015, IRIDIA, Université Libre de Bruxelles, Belgium, 2013.
[ bib ]

[2056]

Manuel López-Ibáñez, Arnaud Liefooghe, and Sébastien Verel. Local Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with Correlated Objectives. Technical Report TR/IRIDIA/2014-009, IRIDIA, Université Libre de Bruxelles, Belgium, 2014.
[ bib ]

[2057]

Vito Trianni and Manuel López-Ibáñez. Advantages of Multi-Objective Optimisation in Evolutionary Robotics: Survey and Case Studies. Technical Report TR/IRIDIA/2014-014, IRIDIA, Université Libre de Bruxelles, Belgium, 2014.
[ bib | http ]

[2058]

Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms. Technical Report TR/IRIDIA/2017-005, IRIDIA, Université Libre de Bruxelles, Belgium, November 2017.
[ bib ]

[2059]

Alberto Franzin, Leslie Pérez Cáceres, and Thomas Stützle. Effect of Transformations of Numerical Parameters in Automatic Algorithm Configuration. Technical Report TR/IRIDIA/2017-006, IRIDIA, Université Libre de Bruxelles, Belgium, March 2017.
[ bib | http ]

[2060]

Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration. Technical Report TR/IRIDIA/2017-011, IRIDIA, Université Libre de Bruxelles, Belgium, November 2017. Published as a book chapter [1571].
[ bib | http ]

[2061]

Manuel López-Ibáñez, Marie-Eléonore Kessaci, and Thomas Stützle. Automatic Design of Hybrid Metaheuristics from Algorithmic Components. Technical Report TR/IRIDIA/2017-012, IRIDIA, Université Libre de Bruxelles, Belgium, December 2017.
[ bib | http ]

[2062]

Leonardo C. T. Bezerra, Manuel López-Ibáñez, and Thomas Stützle. Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms. Technical Report TR/IRIDIA/2018-001, IRIDIA, Université Libre de Bruxelles, Belgium, January 2018. Published in Evolutionary Computation journal [133].
[ bib | http ]

[2063]

Alberto Franzin and Thomas Stützle. Revisiting Simulated Annealing: a Component-Based Analysis. Technical Report TR/IRIDIA/2018-010, IRIDIA, Université Libre de Bruxelles, Belgium, 2018.
[ bib | http ]

[2064]

Christian Leonardo Camacho-Villalón, Thomas Stützle, and Marco Dorigo. PSO-X: A Component-Based Framework for the Automatic Design of Particle Swarm Optimization Algorithms. Technical Report TR/IRIDIA/2021-002, IRIDIA, Université Libre de Bruxelles, Belgium, 2021.
[ bib | http ]

Published as [228]

[2065]

Alberto Franzin and Thomas Stützle. A Landscape-based Analysis of Fixed Temperature and Simulated Annealing. Technical Report TR/IRIDIA/2021-005, IRIDIA, Université Libre de Bruxelles, Belgium, 2021.
[ bib | http ]

[2066]

Christian Leonardo Camacho-Villalón, Thomas Stützle, and Marco Dorigo. Cuckoo Search ≡(μ+ λ)-Evolution Strategy – A Rigorous Analysis of an Algorithm That Has Been Misleading the Research Community for More Than 10 Years and Nobody Seems to Have Noticed. Technical Report TR/IRIDIA/2021-006, IRIDIA, Université Libre de Bruxelles, Belgium, 2021.
[ bib | http ]

[2067]

Christian Igel. Multi-objective Model Selection for Support Vector Machines. In C. A. Coello Coello, A. Hernández Aguirre, and E. Zitzler, editors, Evolutionary Multi-criterion Optimization, EMO 2005, volume 3410 of Lecture Notes in Computer Science, pp.  534–546. Springer, Berlin/Heidelberg, 2005.
[ bib | DOI ]

Early work on multi-objective hyper-parameter optimization (AutoML)

[2068]

Kokolo Ikeda, Hajime Kita, and Shigenobu Kobayashi. Failure of Pareto-based MOEAs: Does non-dominated really mean near to optimal? In Proceedings of the 2001 Congress on Evolutionary Computation (CEC'01), pp.  957–962, Piscataway, NJ, 2001. IEEE Press.
[ bib ]

Keywords: dominance resistance

[2069]

Janine Illian, Antti Penttinen, Helga Stoyan, and Dietrich Stoyan. Statistical Analysis and Modelling of Spatial Point Patterns. Wiley, 2008.
[ bib ]

[2070]

S. Iredi, D. Merkle, and Martin Middendorf. Bi-Criterion Optimization with Multi Colony Ant Algorithms. In E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, and D. Corne, editors, Evolutionary Multi-criterion Optimization, EMO 2001, volume 1993 of Lecture Notes in Computer Science, pp.  359–372. Springer, Berlin/Heidelberg, 2001.
[ bib ]

Keywords: BicriterionAnt

[2071]

Manuel López-Ibáñez and Thomas Stützle. Automatically Improving the Anytime Behaviour of Optimisation Algorithms: Supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/, 2012.
[ bib ]

[2072]

Ekhine Irurozki and Manuel López-Ibáñez. Unbalanced Mallows Models for Optimizing Expensive Black-Box Permutation Problems. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021, pp.  225–233. ACM Press, New York, NY, 2021.
[ bib | DOI | supplementary material ]

Expensive black-box combinatorial optimization problems arise in practice when the objective function is evaluated by means of a simulator or a real-world experiment. Since each fitness evaluation is expensive in terms of time or resources, only a limited number of evaluations is possible, typically several orders of magnitude smaller than in non-expensive problems. In this scenario, classical optimization methods such as mixed-integer programming and local search are not useful. In the continuous case, Bayesian optimization, in particular using Gaussian processes, has proven very effective under these conditions. Much less research is available in the combinatorial case. In this paper, we propose and analyze UMM, an estimation-of-distribution (EDA) algorithm based on a Mallows probabilistic model and unbalanced rank aggregation (uBorda). Experimental results on black-box versions of LOP and PFSP show that UMM is able to match, and sometimes surpass, the solutions obtained by CEGO, a Bayesian optimization algorithm for combinatorial optimization. Moreover, the computational complexity of UMM increases linearly with both the number of function evaluations and the permutation size.

Keywords: UMM, Permutation, Expensive, Black-box

[2073]

Hisao Ishibuchi, Hiroyuki Masuda, and Yusuke Nojima. A Study on Performance Evaluation Ability of a Modified Inverted Generational Distance Indicator. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  695–702. ACM Press, New York, NY, 2015.
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[2074]

Hisao Ishibuchi, Hiroyuki Masuda, Yuki Tanigaki, and Yusuke Nojima. Modified Distance Calculation in Generational Distance and Inverted Generational Distance. In A. Gaspar-Cunha, C. H. Antunes, and C. A. Coello Coello, editors, Evolutionary Multi-criterion Optimization, EMO 2015 Part I, volume 9018 of Lecture Notes in Computer Science, pp.  110–125. Springer, Heidelberg, Germany, 2015.
[ bib ]

Proposed IGD+

Keywords: Performance metrics, multi-objective, IGD, IGD+

[2075]

Hisao Ishibuchi, N. Tsukamoto, and Y. Nojima. Evolutionary many-objective optimization: A short review. In Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008), pp.  2419–2426, Piscataway, NJ, 2008. IEEE Press.
[ bib | DOI ]

[2076]

Dario Izzo, Ingmar Getzner, Daniel Hennes, and Luís F. Simões. Evolving solutions to TSP variants for active space debris removal. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  1207–1214. ACM Press, New York, NY, 2015.
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[2077]

Dario Izzo, Luís F. Simões, Marcus Märtens, Guido C.H.E. de Croon, Aurelie Heritier, and Chit Hong Yam. Search for a Grand Tour of the Jupiter Galilean Moons. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013, pp.  1301–1308. ACM Press, New York, NY, 2013.
[ bib | DOI ]

[2078]

Sophie Jacquin, Laetitia Jourdan, and El-Ghazali Talbi. Dynamic Programming Based Metaheuristic for Energy Planning Problems. In A. I. Esparcia-Alcázar and A. M. Mora, editors, Applications of Evolutionary Computation, volume 8602 of Lecture Notes in Computer Science, pp.  165–176. Springer, Heidelberg, Germany, 2014.
[ bib | DOI ]

Keywords: irace

[2079]

Antonio López Jaimes, Carlos A. Coello Coello, and Debrup Chakraborty. Objective reduction using a feature selection technique. In C. Ryan, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008, pp.  673–680. ACM Press, New York, NY, 2008.
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[2080]

Antonio López Jaimes, Carlos A. Coello Coello, and Jesús E. Urías Barrientos. Online Objective Reduction to Deal with Many-Objective Problems. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, Evolutionary Multi-criterion Optimization, EMO 2009, volume 5467 of Lecture Notes in Computer Science, pp.  423–437. Springer, Heidelberg, Germany, 2009.
[ bib ]

In this paper, we propose and analyze two schemes to integrate an objective reduction technique into a multi-objective evolutionary algorithm (moea) in order to cope with many-objective problems. One scheme reduces periodically the number objectives during the search until the required objective subset size has been reached and, towards the end of the search, the original objective set is used again. The second approach is a more conservative scheme that alternately uses the reduced and the entire set of objectives to carry out the search. Besides improving computational efficiency by removing some objectives, the experimental results showed that both objective reduction schemes also considerably improve the convergence of a moea in many-objective problems.

[2081]

Kevin G. Jamieson and Ameet Talwalkar. Non-stochastic Best Arm Identification and Hyperparameter Optimization. In A. Gretton and C. C. Robert, editors, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016, volume 51 of JMLR Workshop and Conference Proceedings, pp.  240–248. JMLR.org, 2016.
[ bib | http ]

[2082]

Andrzej Jaszkiewicz and Jürgen Branke. Interactive Multiobjective Evolutionary Algorithms. In J. Branke, K. Deb, K. Miettinen, and R. Slowiński, editors, Multiobjective Optimization: Interactive and Evolutionary Approaches, volume 5252 of Lecture Notes in Computer Science, pp.  179–193. Springer, Heidelberg, Germany, 2008.
[ bib | DOI ]

[2083]

Andrzej Jaszkiewicz, Hisao Ishibuchi, and Qingfu Zhang. Multiobjective memetic algorithms. In F. Neri, C. Cotta, and P. Moscato, editors, Handbook of Memetic Algorithms, volume 379 of Studies in Computational Intelligence, pp.  201–217. Springer, 2011.
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[2084]

Frank Hutter and Steve Ramage. Manual for SMAC. University of British Columbia, 2015. SMAC version 2.10.03.
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[2085]

Mark Jerrum and Alistair Sinclair. The Markov chain Monte Carlo method: an approach to approximate counting and integration. In D. S. Hochbaum, editor, Approximation Algorithms For NP-hard Problems, pp.  482–520. PWS Publishing Co., 1996.
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[2086]

Alexandre D. Jesus, Arnaud Liefooghe, Bilel Derbel, and Luís Paquete. Algorithm Selection of Anytime Algorithms. In C. A. Coello Coello, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020, pp.  850—858. ACM Press, New York, NY, 2020.
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[2087]

Journal of Heuristics. Policies on Heuristic Search Research. http://www.springer.com/journal/10732, 2015. Version visited last on June 10, 2015.
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[2088]

David S. Johnson, G. Gutin, Lyle A. McGeoch, A. Yeo, W. Zhang, and A. Zverovitch. Experimental Analysis of Heuristics for the ATSP. In G. Gutin and A. Punnen, editors, The Traveling Salesman Problem and its Variations, pp.  445–487. Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002.
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[2089]

David S. Johnson and Lyle A. McGeoch. Experimental Analysis of Heuristics for the STSP. In G. Gutin and A. Punnen, editors, The Traveling Salesman Problem and its Variations, pp.  369–443. Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002.
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[2090]

David S. Johnson and Lyle A. McGeoch. The Traveling Salesman Problem: A Case Study in Local Optimization. In E. H. L. Aarts and J. K. Lenstra, editors, Local Search in Combinatorial Optimization, pp.  215–310. John Wiley & Sons, Chichester, UK, 1997.
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[2091]

David S. Johnson. Local Optimization and the Traveling Salesman Problem. In M. Paterson, editor, Automata, Languages and Programming, 17th International Colloquium, volume 443 of Lecture Notes in Computer Science, pp.  446–461, Heidelberg, Germany, 1990. Springer.
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[2092]

David S. Johnson, Lyle A. McGeoch, C. Rego, and Fred Glover. 8th DIMACS Implementation Challenge: The Traveling Salesman Problem. http://dimacs.rutgers.edu/archive/Challenges/TSP, 2001.
[ bib ]

Keywords: TSP Challenge, RUE, RCE, generators

[2093]

David S. Johnson. A Theoretician's Guide to the Experimental Analysis of Algorithms. In M. H. Goldwasser, D. S. Johnson, and C. C. McGeoch, editors, Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, volume 59 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science, pp.  215–250. American Mathematical Society, Providence, RI, 2002.
[ bib | DOI ]

[2094]

Kenneth A. De Jong. Evolutionary computation: a unified approach. MIT Press, Cambridge, MA, 2006.
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Keywords: Safe Optimization, evolutionary computation, constraint violation, experiment-based evolutionary multiobjective optimization, evolutionary algorithm, risky-constraint violation, Constraint optimization, Diesel engines, Calibration, Evolutionary computation, Electric breakdown, Optimization methods, Uncertainty, Computational fluid dynamics

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Youngmin Kim, Richard Allmendinger, and Manuel López-Ibáñez. Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art. In F. Heintz, M. Milano, and B. O'Sullivan, editors, Trustworthy AI – Integrating Learning, Optimization and Reasoning. TAILOR 2020, volume 12641 of Lecture Notes in Computer Science, pp.  123–139. Springer, Cham, Switzerland, 2021.
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Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e.g., breakage of a machine or equipment, or life threat). Although a comprehensive survey of safe reinforcement learning algorithms was published in 2015, a number of new algorithms have been proposed thereafter, and related works in active learning and in optimization were not considered. This paper reviews those algorithms from a number of domains including reinforcement learning, Gaussian process regression and classification, evolutionary computing, and active learning. We provide the fundamental concepts on which the reviewed algorithms are based and a characterization of the individual algorithms. We conclude by explaining how the algorithms are connected and suggestions for future research.

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Youngmin Kim, Richard Allmendinger, and Manuel López-Ibáñez. Are Evolutionary Algorithms Safe Optimizers? In J. E. Fieldsend and M. Wagner, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022, pp.  814–822. ACM Press, New York, NY, 2022.
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We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platform or loss of human life. Such problems are referred to as safe optimization problems (SafeOPs). While SafeOPs have received attention in the machine learning community in recent years, there was little interest in the evolutionary computation (EC) community despite some early attempts between 2009 and 2011. Moreover, there is a lack of acceptable guidelines on how to benchmark different algorithms for SafeOPs, an area where the EC community has significant experience in. Driven by the need for more eficient algorithms and benchmark guidelines for SafeOPs, the objective of this paper is to reignite the interest of the EC community in this problem class. To achieve this we (i) provide a formal definition of SafeOPs and contrast it to other types of optimization problems that the EC community is familiar with, (ii) investigate the impact of key SafeOP parameters on the performance of selected safe optimization algorithms, (iii) benchmark EC against state-of-the-art safe optimization algorithms from the machine learning community, and (iv) provide an open-source Python framework to replicate and extend our work.

Keywords: Bayesian optimization, constrained optimization, benchmarking, safety constraints, safe optimization

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Minsu Kim, Jinkyoo Park, and Joungho Kim. Learning Collaborative Policies to Solve NP-hard Routing Problems. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, and J. W. Vaughan, editors, Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 2021.
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Keywords: Deep RL, TSP, prize collecting, PCTSP, CVRP, routing, attention model

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Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In Y. Bengio and Y. LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
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Joshua D. Knowles. A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. In A. Abraham and M. Paprzycki, editors, Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, pp.  552–557, 2005.
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When evaluating the performance of a stochastic optimizer it is sometimes desirable to express performance in terms of the quality attained in a certain fraction of sample runs. For example, the sample median quality is the best estimator of what one would expect to achieve in 50% of runs, and similarly for other quantiles. In multiobjective optimization, the notion still applies but the outcome of a run is measured not as a scalar (i.e. the cost of the best solution), but as an attainment surface in k-dimensional space (where k is the number of objectives). In this paper we report an algorithm that can be conveniently used to plot summary attainment surfaces in any number of dimensions (though it is particularly suited for three). A summary attainment surface is defined as the union of all tightest goals that have been attained (independently) in precisely s of the runs of a sample of n runs, for any s ∈ 1...n, and for any k. We also discuss the computational complexity of the algorithm and give some examples of its use. C code for the algorithm is available from the author.

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Joshua D. Knowles and David Corne. The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multiobjective Optimisation. In Proceedings of the 1999 Congress on Evolutionary Computation (CEC 1999), pp.  98–105, Piscataway, NJ, 1999. IEEE Press.
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Joshua D. Knowles and David Corne. M-PAES: A memetic algorithm for multiobjective optimization. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC'00), pp.  325–332, Piscataway, NJ, July 2000. IEEE Press.
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Joshua D. Knowles and David Corne. On Metrics for Comparing Non-Dominated Sets. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC'02), pp.  711–716, Piscataway, NJ, 2002. IEEE Press.
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Joshua D. Knowles and David Corne. Instance Generators and Test Suites for the Multiobjective Quadratic Assignment Problem. In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors, Evolutionary Multi-criterion Optimization, EMO 2003, volume 2632 of Lecture Notes in Computer Science, pp.  295–310. Springer, Heidelberg, Germany, 2003.
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Joshua D. Knowles and David Corne. Bounded Pareto Archiving: Theory and Practice. In X. Gandibleux, M. Sevaux, K. Sörensen, and V. T'Kindt, editors, Metaheuristics for Multiobjective Optimisation, volume 535 of Lecture Notes in Economics and Mathematical Systems, pp.  39–64. Springer, Berlin/Heidelberg, 2004.
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[2138]

Joshua D. Knowles and David Corne. Memetic algorithms for multiobjective optimization: issues, methods and prospects. In H. W. E., S. J. E., and K. N., editors, Recent Advances in Memetic Algorithms, volume 166 of Studies in Fuzziness and Soft Computing, pp.  313–352. Springer, Berlin/Heidelberg, 2005.
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Joshua D. Knowles and David Corne. Quantifying the Effects of Objective Space Dimension in Evolutionary Multiobjective Optimization. In S. Obayashi et al., editors, Evolutionary Multi-criterion Optimization, EMO 2007, volume 4403 of Lecture Notes in Computer Science, pp.  757–771. Springer, Heidelberg, Germany, 2007.
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The scalability of EMO algorithms is an issue of significant concern for both algorithm developers and users. A key aspect of the issue is scalability to objective space dimension, other things being equal. Here, we make some observations about the efficiency of search in discrete spaces as a function of the number of objectives, considering both uncorrelated and correlated objective values. Efficiency is expressed in terms of a cardinality-based (scaling-independent) performance indicator. Considering random sampling of the search space, we measure, empirically, the fraction of the true PF covered after p iterations, as the number of objectives grows, and for different correlations. A general analytical expression for the expected performance of random search is derived, and is shown to agree with the empirical results. We postulate that for even moderately large numbers of objectives, random search will be competitive with an EMO algorithm and show that this is the case empirically: on a function where each objective is relatively easy for an EA to optimize (an NK-landscape with K=2), random search compares favourably to a well-known EMO algorithm when objective space dimension is ten, for a range of inter-objective correlation values. The analytical methods presented here may be useful for benchmarking of other EMO algorithms.

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Joshua D. Knowles, David Corne, and Kalyanmoy Deb. Introduction: Problem solving, EC and EMO. In J. D. Knowles, D. Corne, K. Deb, and D. R. Chair, editors, Multiobjective Problem Solving from Nature, Natural Computing Series, pp.  1–28. Springer, Berlin/Heidelberg, 2008.
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[2141]

Joshua D. Knowles, David Corne, and Mark Fleischer. Bounded archiving using the Lebesgue measure. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03), pp.  2490–2497, Piscataway, NJ, December 2003. IEEE Press.
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Joshua D. Knowles, David Corne, and Alan P. Reynolds. Noisy Multiobjective Optimization on a Budget of 250 Evaluations. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, and M. Sevaux, editors, Evolutionary Multi-criterion Optimization, EMO 2009, volume 5467 of Lecture Notes in Computer Science, pp.  36–50. Springer, Heidelberg, Germany, 2009.
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Joshua D. Knowles, Lothar Thiele, and Eckart Zitzler. A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK-Report 214, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland, February 2006. Revised version.
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Joshua D. Knowles, Richard A. Watson, and David Corne. Reducing Local Optima in Single-Objective Problems by Multi-objectivization. In E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, and D. Corne, editors, Evolutionary Multi-criterion Optimization, EMO 2001, volume 1993 of Lecture Notes in Computer Science, pp.  269–283. Springer, Berlin/Heidelberg, 2001.
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Joshua D. Knowles. Local-Search and Hybrid Evolutionary Algorithms for Pareto Optimization. PhD thesis, University of Reading, UK, 2002.
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Behavior decision theorists have studied human decision making in great detail. Since the late 1960's, Einhorn, Edwards, Kahneman, Roy, Trevsky, and others have developed new thoeries to explain choice and decision behavior. Thus far this behavior research has had little impact on multiple criteria decision making (MCDM). Only a handful of MCDM-research have critically examined the behavioral underpinnings of our field. To improve the success of decision tools in practice, MCDM-research should pay more attention to the behavioral realities of decision making. In this paper, we discuss various behavioral issues relevent for MCDM based on our personal observations and experiments with human subjects. The spirit of our paper is to pose questions rather than provide definite answers.

Keywords: Aspiration Level, Decision Tool, Nondominated Solution, Prefer Solution, Prospect Theory

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Ana Kostovska, Diederick Vermetten, Carola Doerr, Sašo Džeroski, Panče Panov, and Tome Eftimov. OPTION: optimization algorithm benchmarking ontology. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021, pp.  239–240. ACM Press, New York, NY, 2021.
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Proposed KUR benchmark

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Pedro Larrañaga and José A. Lozano. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Boston, MA, 2002.
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Marco Laumanns, Lothar Thiele, Eckart Zitzler, and Kalyanmoy Deb. Archiving with guaranteed convergence and diversity in multi-objective optimization. In W. B. Langdon et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2002, pp.  439–447. Morgan Kaufmann Publishers, San Francisco, CA, 2002.
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Marco Laumanns and Rico Zenklusen. Stochastic convergence of random search methods to fixed size Pareto front approximations. (submitted), November 2010.
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Guillermo Leguizamón and Zbigniew Michalewicz. A New Version of Ant System for Subset Problems. In Proceedings of the 1999 Congress on Evolutionary Computation (CEC 1999), pp.  1459–1464, Piscataway, NJ, 1999. IEEE Press.
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R. J. Lempert, S. Popper, and Steven C. Bankes. Shaping the Next One Hundred Years: New Methods for Quantitative, Long Term Policy Analysis. RAND, 2003.
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L. Lessing, Irina Dumitrescu, and Thomas Stützle. A Comparison Between ACO Algorithms for the Set Covering Problem. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  1–12. Springer, Heidelberg, Germany, 2004.
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Rhyd M. R. Lewis. A Guide to Graph Colouring: Algorithms and Applications. Springer, Cham, Switzerland, 2016.
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Rhyd M. R. Lewis. Suite of Graph Colouring Algorithms – Supplementary Material to the Book “A Guide to Graph Colouring: Algorithms and Applications”. http://rhydlewis.eu/resources/gCol.zip, 2016.
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Kevin Leyton-Brown, Eugene Nudelman, Galen Andrew, Jim McFadden, and Yoav Shoham. A Portfolio Approach to Algorithm Selection. In G. Gottlob and T. Walsh, editors, Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03), pp.  1542–1543. Morgan Kaufmann Publishers, 2003.
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Kevin Leyton-Brown, Eugene Nudelman, and Yoav Shoham. Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions. In P. van Hentenryck, editor, Principles and Practice of Constraint Programming, CP 2002, Lecture Notes in Computer Science, pp.  556–572. Springer, Heidelberg, Germany, 2002.
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Kevin Leyton-Brown, M. Pearson, and Y. Shoham. Towards a Universal Test Suite for Combinatorial Auction Algorithms. In A. Jhingran et al., editors, ACM Conference on Electronic Commerce (EC-00), pp.  66–76. ACM Press, New York, NY, 2000.
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CPLEX-regions200 benchmark set, http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/results.html

[2187]

Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao. An Improved Two Archive Algorithm for Many-Objective Optimization. In Proceedings of the 2014 Congress on Evolutionary Computation (CEC 2014), pp.  2869–2876, Piscataway, NJ, 2014. IEEE Press.
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Z. Li, Y. Wang, J. Yu, Y. Zhang, and X. Li. A Novel Cloud-Based Fuzzy Self-Adaptive Ant Colony System. In ICNC'08: Proceedings of the 2008 Fourth International Conference on Natural Computation, volume 7, pp.  460–465, Washington, DC, 2008. IEEE Computer Society.
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Miqing Li, Shengxiang Yang, and Xiaohui Liu. A performance comparison indicator for Pareto front approximations in many-objective optimization. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  703–710. ACM Press, New York, NY, 2015.
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Proposed PCI indicator

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Miqing Li, Shengxiang Yang, Xiaohui Liu, and Ruimin Shen. A Comparative Study on Evolutionary Algorithms for Many-Objective Optimization. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, editors, Evolutionary Multi-criterion Optimization, EMO 2013, volume 7811 of Lecture Notes in Computer Science, pp.  261–275. Springer, Heidelberg, Germany, 2013.
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Miqing Li and Xin Yao. An Empirical Investigation of the Optimality and Monotonicity Properties of Multiobjective Archiving Methods. In K. Deb, E. D. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, and P. Reed, editors, Evolutionary Multi-criterion Optimization, EMO 2019, volume 11411 of Lecture Notes in Computer Science, pp.  15–26. Springer International Publishing, Cham, Switzerland, 2019.
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Longmei Li, Iryna Yevseyeva, Vitor Basto-Fernandes, Heike Trautmann, Ning Jing, and Michael T. M. Emmerich. Building and using an ontology of preference-based multiobjective evolutionary algorithms. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. M. Wiecek, Y. Jin, and C. Grimme, editors, Evolutionary Multi-criterion Optimization, EMO 2017, volume 10173 of Lecture Notes in Computer Science, pp.  406–421. Springer International Publishing, Cham, Switzerland, 2017.
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Tianjun Liao, Marco A. Montes de Oca, Doǧan Aydın, Thomas Stützle, and Marco Dorigo. An Incremental Ant Colony Algorithm with Local Search for Continuous Optimization. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  125–132. ACM Press, New York, NY, 2011.
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Tianjun Liao, Marco A. Montes de Oca, and Thomas Stützle. Tuning Parameters across Mixed Dimensional Instances: A Performance Scalability Study of Sep-G-CMA-ES. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2011, pp.  703–706. ACM Press, New York, NY, 2011.
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Workshop on Scaling Behaviours of Landscapes, Parameters and Algorithms

[2195]

Tianjun Liao and Thomas Stützle. Benchmark results for a simple hybrid algorithm on the CEC 2013 benchmark set for real-parameter optimization. In Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013), pp.  1938–1944, Piscataway, NJ, 2013. IEEE Press.
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Tianjun Liao. Population-based Heuristic Algorithms for Continuous and Mixed Discrete-Continuous Optimization Problem. PhD thesis, IRIDIA, École polytechnique, Université Libre de Bruxelles, Belgium, 2013.
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Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Hernán E. Aguirre, and Kiyoshi Tanaka. Towards Landscape-Aware Automatic Algorithm Configuration: Preliminary Experiments on Neutral and Rugged Landscapes. In B. Hu and M. López-Ibáñez, editors, Proceedings of EvoCOP 2017 – 17th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 10197 of Lecture Notes in Computer Science, pp.  215–232. Springer, Heidelberg, Germany, 2017.
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[2198]

Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Manuel López-Ibáñez, Hernán E. Aguirre, and Kiyoshi Tanaka. On Pareto Local Optimal Solutions Networks. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, and D. Whitley, editors, Parallel Problem Solving from Nature – PPSN XV, volume 11102 of Lecture Notes in Computer Science, pp.  232–244. Springer, Cham, Switzerland, 2018.
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[2199]

Arnaud Liefooghe and Manuel López-Ibáñez. Many-objective (Combinatorial) Optimization is Easy. In S. Silva and L. Paquete, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023, pp.  704–712. ACM Press, New York, NY, 2023.
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It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives tends to reduce the difficulty of the landscape being optimized. Of course, increasing the number of objectives brings about other challenges, such as an increase in the computational effort of many operations, or the memory requirements for storing non-dominated solutions. More precisely, we consider a broad range of multi- and many-objective combinatorial benchmark problems, and we measure how the number of objectives impacts the dominance relation among solutions, the connectedness of the Pareto set, and the landscape multimodality in terms of local optimal solutions and sets. Our analysis shows the limit behavior of various landscape features when adding more objectives to a problem. Our conclusions do not contradict previous observations about the inability of Pareto-optimality to drive search, but we explain these observations from a different perspective. Our findings have important implications for the design and analysis of many-objective optimization algorithms.

ISBN: 9798400701191

[2200]

Arnaud Liefooghe, Manuel López-Ibáñez, Luís Paquete, and Sébastien Verel. Dominance, Epsilon, and Hypervolume Local Optimal Sets in Multi-objective Optimization, and How to Tell the Difference. In H. E. Aguirre and K. Takadama, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, pp.  324–331. ACM Press, New York, NY, 2018.
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[2201]

Arnaud Liefooghe, Salma Mesmoudi, Jérémie Humeau, Laetitia Jourdan, and El-Ghazali Talbi. A Study on Dominance-based Local Search Approaches for Multiobjective Combinatorial Optimization. In T. Stützle, M. Birattari, and H. H. Hoos, editors, Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2009, volume 5752 of Lecture Notes in Computer Science, pp.  120–124. Springer, Heidelberg, Germany, 2009.
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[2202]

Arnaud Liefooghe, Luís Paquete, Marco Simōes, and José Rui Figueira. Connectedness and Local Search for Bicriteria Knapsack Problems. In P. Merz and J.-K. Hao, editors, Proceedings of EvoCOP 2011 – 11th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 6622 of Lecture Notes in Computer Science, pp.  48–59. Springer, Heidelberg, Germany, 2011.
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[2203]

Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Hernán E. Aguirre, and Kiyoshi Tanaka. What Makes an Instance Difficult for Black-box 0–1 Evolutionary Multiobjective Optimizers? In P. Legrand et al., editors, Artificial Evolution: 11th International Conference, Evolution Artificielle, EA, 2013, volume 8752 of Lecture Notes in Computer Science, pp.  3–15. Springer, Heidelberg, Germany, 2013.
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[2204]

Arnaud Liefooghe, Sébastien Verel, Benjamin Lacroix, Alexandru-Ciprian Zavoianu, and John McCall. Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021, pp.  421–429. ACM Press, New York, NY, 2021.
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[2205]

Arnaud Liefooghe, Sébastien Verel, Luís Paquete, and Jin-Kao Hao. Experiments on Local Search for Bi-objective Unconstrained Binary Quadratic Programming. In A. Gaspar-Cunha, C. H. Antunes, and C. A. Coello Coello, editors, Evolutionary Multi-criterion Optimization, EMO 2015 Part I, volume 9018 of Lecture Notes in Computer Science, pp.  171–186. Springer, Heidelberg, Germany, 2015.
[ bib ]

This article reports an experimental analysis on stochastic local search for approximating the Pareto set of bi-objective unconstrained binary quadratic programming problems. First, we investigate two scalarizing strategies that iteratively identify a high-quality solution for a sequence of sub-problems. Each sub-problem is based on a static or adaptive definition of weighted-sum aggregation coefficients, and is addressed by means of a state-of-the-art single-objective tabu search procedure. Next, we design a Pareto local search that iteratively improves a set of solutions based on a neighborhood structure and on the Pareto dominance relation. At last, we hybridize both classes of algorithms by combining a scalarizing and a Pareto local search in a sequential way. A comprehensive experimental analysis reveals the high performance of the proposed approaches, which substantially improve upon previous best-known solutions. Moreover, the obtained results show the superiority of the hybrid algorithm over non-hybrid ones in terms of solution quality, while requiring a competitive computational cost. In addition, a number of structural properties of the problem instances allow us to explain the main difficulties that the different classes of local search algorithms have to face.

[2206]

David J. Lilja. Measuring Computer Performance: A Practitioner's Guide. Cambridge University Press, 2000.
[ bib | DOI ]

Measuring Computer Performance sets out the fundamental techniques used in analyzing and understanding the performance of computer systems. Throughout the book, the emphasis is on practical methods of measurement, simulation, and analytical modeling. The author discusses performance metrics and provides detailed coverage of the strategies used in benchmark programmes. He gives intuitive explanations of the key statistical tools needed to interpret measured performance data. He also describes the general 'design of experiments' technique, and shows how the maximum amount of information can be obtained for the minimum effort. The book closes with a chapter on the technique of queueing analysis. Appendices listing common probability distributions and statistical tables are included, along with a glossary of important technical terms. This practically-oriented book will be of great interest to anyone who wants a detailed, yet intuitive, understanding of computer systems performance analysis.

[2207]

Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, and Torsten Schaub. AutoFolio: Algorithm Configuration for Algorithm Selection. In B. Bonet and S. Koenig, editors, Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, 2015.
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[2208]

W. Ling and H. Luo. An Adaptive Parameter Control Strategy for Ant Colony Optimization. In CIS'07: Proceedings of the 2007 International Conference on Computational Intelligence and Security, pp.  142–146, Washington, DC, 2007. IEEE Computer Society.
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[2209]

Innovation 24. LocalSolver. http://www.localsolver.com, 2016. Last visited, August 15, 2016.
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[2210]

Andrea Lodi and Andrea Tramontani. Performance Variability in Mixed-Integer Programming. In H. Topaluglu, editor, Theory Driven by Influential Applications, pp.  1–12. INFORMS, 2013.
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[2211]

Andrea Lodi, Silvano Martello, and Daniele Vigo. Two- and Three-Dimensional Bin Packing – Source Code of TSpack. https://site.unibo.it/operations-research/en/research/library-of-codes-and-instances-1/tspack-tar.gz/@@download/file/TSpack.tar.gz, 2004.
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[2212]

Po-Ling Loh and Sebastian Nowozin. Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates. In S. Jain, R. Munos, F. Stephan, and T. Zeugmann, editors, Proceedings of Algorithmic Learning Theory, volume 8139 of Lecture Notes in Computer Science, pp.  203–217. Springer, Berlin, Germany, 2013.
[ bib | DOI ]

[2213]

Manuel López-Ibáñez. High Performance Ant Colony Optimisation of the Pump Scheduling Problem. In P. Alberigo, G. Erbacci, F. Garofalo, and S. Monfardini, editors, Science and Sumpercomputing in Europe, pp.  371–375. CINECA, 2007.
[ bib ]

[2214]

Manuel López-Ibáñez and Christian Blum. Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows. Technical Report LSI-08-28, Department LSI, Universitat Politècnica de Catalunya, 2008. Extended version published in Computers & Operations Research [865].
[ bib ]

[2215]

Manuel López-Ibáñez, Christian Blum, Dhananjay Thiruvady, Andreas T. Ernst, and Bernd Meyer. Beam-ACO based on stochastic sampling for makespan optimization concerning the TSP with time windows. In C. Cotta and P. Cowling, editors, Proceedings of EvoCOP 2009 – 9th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 5482 of Lecture Notes in Computer Science, pp.  97–108. Springer, Heidelberg, Germany, 2009.
[ bib | DOI ]

[2216]

Manuel López-Ibáñez and Christian Blum. Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows. In T. Stützle, editor, Learning and Intelligent Optimization, Third International Conference, LION 3, volume 5851 of Lecture Notes in Computer Science, pp.  59–73. Springer, Heidelberg, Germany, 2009.
[ bib | DOI ]

[2217]

Manuel López-Ibáñez, Francisco Chicano, and Rodrigo Gil-Merino. The Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation Optimization. In J. L. Jiménez Laredo et al., editors, EvoApplications 2022: Applications of Evolutionary Computation, volume 13224 of Lecture Notes in Computer Science, pp.  124–140. Springer Nature, Switzerland, 2022.
[ bib | DOI | epub | supplementary material ]

Inspired by the recent 11th Global Trajectory Optimisation Competition, this paper presents the asteroid routing problem (ARP) as a realistic benchmark of algorithms for expensive bound-constrained black-box optimization in permutation space. Given a set of asteroids' orbits and a departure epoch, the goal of the ARP is to find the optimal sequence for visiting the asteroids, starting from Earth's orbit, in order to minimize both the cost, measured as the sum of the magnitude of velocity changes required to complete the trip, and the time, measured as the time elapsed from the departure epoch until visiting the last asteroid. We provide open-source code for generating instances of arbitrary sizes and evaluating solutions to the problem. As a preliminary analysis, we compare the results of two methods for expensive black-box optimization in permutation spaces, namely, Combinatorial Efficient Global Optimization (CEGO), a Bayesian optimizer based on Gaussian processes, and Unbalanced Mallows Model (UMM), an estimation-of-distribution algorithm based on probabilistic Mallows models. We investigate the best permutation representation for each algorithm, either rank-based or order-based. Moreover, we analyze the effect of providing a good initial solution, generated by a greedy nearest neighbor heuristic, on the performance of the algorithms. The results suggest directions for improvements in the algorithms being compared.

Keywords: Spacecraft Trajectory Optimization, Unbalanced Mallows Model, Combinatorial Efficient Global Optimization, Estimation of Distribution Algorithms, Bayesian Optimization

[2218]

Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Leslie Pérez Cáceres, Thomas Stützle, and Mauro Birattari. The irace Package: Iterated Racing for Automatic Algorithm Configuration (Supplementary Material). http://iridia.ulb.ac.be/supp/IridiaSupp2016-003, 2016.
[ bib ]

[2219]

Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Thomas Stützle, and Mauro Birattari. The irace package, Iterated Race for Automatic Algorithm Configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium, 2011. Published in Operations Research Perspectives [869].
[ bib | http ]

[2220]

Manuel López-Ibáñez and Joshua D. Knowles. Machine Decision Makers as a Laboratory for Interactive EMO. In A. Gaspar-Cunha, C. H. Antunes, and C. A. Coello Coello, editors, Evolutionary Multi-criterion Optimization, EMO 2015 Part II, volume 9019 of Lecture Notes in Computer Science, pp.  295–309. Springer, Heidelberg, Germany, 2015.
[ bib | DOI ]

A key challenge, perhaps the central challenge, of multi-objective optimization is how to deal with candidate solutions that are ultimately evaluated by the hidden or unknown preferences of a human decision maker (DM) who understands and cares about the optimization problem. Alternative ways of addressing this challenge exist but perhaps the favoured one currently is the interactive approach (proposed in various forms). Here, an evolutionary multi-objective optimization algorithm (EMOA) is controlled by a series of interactions with the DM so that preferences can be elicited and the direction of search controlled. MCDM has a key role to play in designing and evaluating these approaches, particularly in testing them with real DMs, but so far quantitative assessment of interactive EMOAs has been limited. In this paper, we propose a conceptual framework for this problem of quantitative assessment, based on the definition of machine decision makers (machine DMs), made somewhat realistic by the incorporation of various non-idealities. The machine DM proposed here draws from earlier models of DM biases and inconsistencies in the MCDM literature. As a practical illustration of our approach, we use the proposed machine DM to study the performance of an interactive EMOA, and discuss how this framework could help in the evaluation and development of better interactive EMOAs.

[2221]

Manuel López-Ibáñez, Joshua D. Knowles, and Marco Laumanns. On Sequential Online Archiving of Objective Vectors. In R. H. C. Takahashi, K. Deb, E. F. Wanner, and S. Greco, editors, Evolutionary Multi-criterion Optimization, EMO 2011, volume 6576 of Lecture Notes in Computer Science, pp.  46–60. Springer, Berlin/Heidelberg, 2011.
[ bib | DOI ]

In this paper, we examine the problem of maintaining an approximation of the set of nondominated points visited during a multiobjective optimization, a problem commonly known as archiving. Most of the currently available archiving algorithms are reviewed, and what is known about their convergence and approximation properties is summarized. The main scenario considered is the restricted case where the archive must be updated online as points are generated one by one, and at most a fixed number of points are to be stored in the archive at any one time. In this scenario, the better-monotonicity of an archiving algorithm is proposed as a weaker, but more practical, property than negative efficiency preservation. This paper shows that hypervolume-based archivers and a recently proposed multi-level grid archiver have this property. On the other hand, the archiving methods used by SPEA2 and NSGA-II do not, and they may better-deteriorate with time. The better-monotonicity property has meaning on any input sequence of points. We also classify archivers according to limit properties, i.e. convergence and approximation properties of the archiver in the limit of infinite (input) samples from a finite space with strictly positive generation probabilities for all points. This paper establishes a number of research questions, and provides the initial framework and analysis for answering them.

Revised version available at http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-001.pdf

[2222]

Manuel López-Ibáñez, Tianjun Liao, and Thomas Stützle. On the anytime behavior of IPOP-CMA-ES. In C. A. Coello Coello et al., editors, Parallel Problem Solving from Nature – PPSN XII, Part I, volume 7491 of Lecture Notes in Computer Science, pp.  357–366. Springer, Heidelberg, Germany, 2012.
[ bib | DOI ]

[2223]

Manuel López-Ibáñez, Tianjun Liao, and Thomas Stützle. On the anytime behavior of IPOP-CMA-ES: Supplementary material. https://iridia.ulb.ac.be/supp/IridiaSupp2012-010/IridiaSupp2012-010.pdf, 2012.
[ bib ]

[2224]

Manuel López-Ibáñez, Arnaud Liefooghe, and Sébastien Verel. Local Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with Correlated Objectives. In T. Bartz-Beielstein, J. Branke, B. Filipič, and J. Smith, editors, Parallel Problem Solving from Nature – PPSN XIII, volume 8672 of Lecture Notes in Computer Science, pp.  621–630. Springer, Heidelberg, Germany, 2014.
[ bib | DOI ]

The properties of local optimal solutions in multi-objective combinatorial optimization problems are crucial for the effectiveness of local search algorithms, particularly when these algorithms are based on Pareto dominance. Such local search algorithms typically return a set of mutually nondominated Pareto local optimal (PLO) solutions, that is, a PLO-set. This paper investigates two aspects of PLO-sets by means of experiments with Pareto local search (PLS). First, we examine the impact of several problem characteristics on the properties of PLO-sets for multi-objective NK-landscapes with correlated objectives. In particular, we report that either increasing the number of objectives or decreasing the correlation between objectives leads to an exponential increment on the size of PLO-sets, whereas the variable correlation has only a minor effect. Second, we study the running time and the quality reached when using bounding archiving methods to limit the size of the archive handled by PLS, and thus, the maximum size of the PLO-set found. We argue that there is a clear relationship between the running time of PLS and the difficulty of a problem instance.

[2225]

Manuel López-Ibáñez, Franco Mascia, Marie-Eléonore Marmion, and Thomas Stützle. Automatic Design of a Hybrid Iterated Local Search for the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem. In G. Kendall, G. Vanden Berghe, and B. McCollum, editors, Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA 2013), pp.  1–6, Gent, Belgium, 2013.
[ bib | epub ]

[2226]

Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. On the Design of ACO for the Biobjective Quadratic Assignment Problem. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  214–225. Springer, Heidelberg, Germany, 2004.
[ bib | DOI ]

[2227]

Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. Hybrid Population-based Algorithms for the Bi-objective Quadratic Assignment Problem. Technical Report AIDA–04–11, FG Intellektik, FB Informatik, TU Darmstadt, December 2004. Published in Journal of Mathematical Modelling and Algorithms [871].
[ bib ]

First use of EAF differences

[2228]

Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pp.  209–222. Springer, Berlin/Heidelberg, 2010.
[ bib | DOI ]

This chapter introduces two Perl programs that implement graphical tools for exploring the performance of stochastic local search algorithms for biobjective optimization problems. These tools are based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. In particular, we consider the visualization of attainment surfaces and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice.

[2229]

Manuel López-Ibáñez, Luís Paquete, and Thomas Stützle. EAF Graphical Tools. http://lopez-ibanez.eu/eaftools, 2010. These tools are described in the book chapter “Exploratory analysis of stochastic local search algorithms in biobjective optimization” [2228].
[ bib ]

Please cite the book chapter, not this.

[2230]

Manuel López-Ibáñez, Leslie Pérez Cáceres, Jérémie Dubois-Lacoste, Thomas Stützle, and Mauro Birattari. The irace package: User Guide. Technical Report TR/IRIDIA/2016-004, IRIDIA, Université Libre de Bruxelles, Belgium, 2016.
[ bib | http ]

[2231]

Manuel López-Ibáñez, T. Devi Prasad, and Ben Paechter. Parallel Optimisation Of Pump Schedules With A Thread-Safe Variant Of EPANET Toolkit. In J. E. van Zyl, A. A. Ilemobade, and H. E. Jacobs, editors, Proceedings of the 10th Annual Water Distribution Systems Analysis Conference (WDSA 2008). ASCE, August 2008.
[ bib | DOI ]

[2232]

Manuel López-Ibáñez, T. Devi Prasad, and Ben Paechter. Solving Optimal Pump Control Problem using Max-Min Ant System. In D. Thierens et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007, volume 1, p.  176. ACM Press, New York, NY, 2007.
[ bib | DOI ]

[2233]

Manuel López-Ibáñez, T. Devi Prasad, and Ben Paechter. Multi-objective Optimisation of the Pump Scheduling Problem using SPEA2. In Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005), volume 1, pp.  435–442, Piscataway, NJ, September 2005. IEEE Press.
[ bib | DOI ]

[2234]

Manuel López-Ibáñez, T. Devi Prasad, and Ben Paechter. Optimal Pump Scheduling: Representation and Multiple Objectives. In D. A. Savic, G. A. Walters, R. King, and S. Thiam-Khu, editors, Proceedings of the Eighth International Conference on Computing and Control for the Water Industry (CCWI 2005), volume 1, pp.  117–122, University of Exeter, UK, September 2005.
[ bib ]

[2235]

Manuel López-Ibáñez and Thomas Stützle. An Analysis of Algorithmic Components for Multiobjective Ant Colony Optimization: A Case Study on the Biobjective TSP. In P. Collet, N. Monmarché, P. Legrand, M. Schoenauer, and E. Lutton, editors, Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009, volume 5975 of Lecture Notes in Computer Science, pp.  134–145. Springer, Heidelberg, Germany, 2010.
[ bib | DOI ]

[2236]

Manuel López-Ibáñez and Thomas Stützle. Automatic Configuration of Multi-Objective ACO Algorithms. In M. Dorigo et al., editors, Swarm Intelligence, 7th International Conference, ANTS 2010, volume 6234 of Lecture Notes in Computer Science, pp.  95–106. Springer, Heidelberg, Germany, 2010.
[ bib | DOI ]

In the last few years a significant number of ant colony optimization (ACO) algorithms have been proposed for tackling multi-objective optimization problems. In this paper, we propose a software framework that allows to instantiate the most prominent multi-objective ACO (MOACO) algorithms. More importantly, the flexibility of this MOACO framework allows the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that such an automatic configuration of MOACO algorithms is highly desirable, given that our automatically configured algorithms clearly outperform the best performing MOACO algorithms that have been proposed in the literature. As far as we are aware, this paper is also the first to apply automatic algorithm configuration techniques to multi-objective stochastic local search algorithms.

[2237]

Manuel López-Ibáñez and Thomas Stützle. The impact of design choices of multi-objective ant colony optimization algorithms on performance: An experimental study on the biobjective TSP. In M. Pelikan and J. Branke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010, pp.  71–78. ACM Press, New York, NY, 2010.
[ bib | DOI ]

Over the last few years, there have been a number of proposals of ant colony optimization (ACO) algorithms for tackling multiobjective combinatorial optimization problems. These proposals adapt ACO concepts in various ways, for example, some use multiple pheromone matrices and multiple heuristic matrices and others use multiple ant colonies.
In this article, we carefully examine several of the most prominent of these proposals. In particular, we identify commonalities among the approaches by recasting the original formulation of the algorithms in different terms. For example, several proposals described in terms of multiple colonies can be cast equivalently using a single ant colony, where ants use different weights for aggregating the pheromone and/or the heuristic information. We study algorithmic choices for the various proposals and we identify previously undetected trade-offs in their performance.

[2238]

Manuel López-Ibáñez and Thomas Stützle. The impact of design choices of multi-objective ant colony optimization algorithms on performance: An experimental study on the biobjective TSP. http://iridia.ulb.ac.be/supp/IridiaSupp2010-003/, 2010. Supplementary material of [2237].
[ bib ]

[2239]

Manuel López-Ibáñez and Thomas Stützle. The Automatic Design of Multi-Objective Ant Colony Optimization Algorithms: Supplementary material, 2011.
[ bib | http ]

[2240]

Manuel López-Ibáñez and Thomas Stützle. An experimental analysis of design choices of multi-objective ant colony optimization algorithms: Supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/, 2012.
[ bib ]

[2241]

Manuel López-Ibáñez, Thomas Stützle, and Marco Dorigo. Ant Colony Optimization: A Component-Wise Overview. In R. Martí, P. M. Pardalos, and M. G. C. Resende, editors, Handbook of Heuristics, pp.  371–407. Springer International Publishing, 2018.
[ bib | DOI | supplementary material ]

Proposed ACOTSPQAP software

[2242]

Manuel López-Ibáñez. Multi-objective Ant Colony Optimization. Diploma thesis, Intellectics Group, Computer Science Department, Technische Universität Darmstadt, Germany, 2004.
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[2243]

Manuel López-Ibáñez. Operational Optimisation of Water Distribution Networks. PhD thesis, School of Engineering and the Built Environment, Edinburgh Napier University, UK, 2009.
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[2244]

Ilya Loshchilov, Marc Schoenauer, and Michèle Sebag. Alternative Restart Strategies for CMA-ES. In C. A. Coello Coello et al., editors, Parallel Problem Solving from Nature – PPSN XII, Part I, volume 7491 of Lecture Notes in Computer Science, pp.  296–305. Springer, Heidelberg, Germany, 2012.
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[2245]

A. V. Lotov and Kaisa Miettinen. Visualizing the Pareto Frontier. In J. Branke, K. Deb, K. Miettinen, and R. Slowiński, editors, Multiobjective Optimization: Interactive and Evolutionary Approaches, volume 5252 of Lecture Notes in Computer Science, pp.  213–243. Springer, Heidelberg, Germany, 2008.
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[2246]

Helena R. Lourenço, Olivier Martin, and Thomas Stützle. Iterated Local Search. In F. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pp.  321–353. Kluwer Academic Publishers, Norwell, MA, 2002.
[ bib | DOI ]

[2247]

Helena R. Lourenço, Olivier Martin, and Thomas Stützle. Iterated Local Search: Framework and Applications. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 146 of International Series in Operations Research & Management Science, chapter 9, pp.  363–397. Springer, New York, NY, 2nd edition, 2010.
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[2248]

Helena R. Lourenço, Olivier Martin, and Thomas Stützle. Iterated Local Search: Framework and Applications. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 272 of International Series in Operations Research & Management Science, chapter 5, pp.  129–168. Springer, 2019.
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[2249]

Scott M. Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems (NIPS 30), pp.  4765–4774, 2016.
[ bib | epub ]

Keywords: SHAP, interpretable AI

[2250]

Hoang N. Luong and Peter A. N. Bosman. Elitist Archiving for Multi-Objective Evolutionary Algorithms: To Adapt or Not to Adapt. In C. A. Coello Coello et al., editors, Parallel Problem Solving from Nature – PPSN XII, Part II, volume 7492 of Lecture Notes in Computer Science, pp.  72–81. Springer, Heidelberg, Germany, 2012.
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[2251]

Thibaut Lust and Jacques Teghem. The multiobjective traveling salesman problem: A survey and a new approach. In C. A. Coello Coello, C. Dhaenens, and L. Jourdan, editors, Advances in Multi-Objective Nature Inspired Computing, volume 272 of Studies in Computational Intelligence, pp.  119–141. Springer, 2010.
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Khin Lwin, Rong Qu, and Jianhua Zheng. Multi-objective Scatter Search with External Archive for Portfolio Optimization. In Proceedings of the 5th International Joint Conference on Computational Intelligence - ECTA (IJCCI 2013), pp.  111–119. SciTePress, 2013.
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Crowding archive

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Robert John Lygoe. Complexity reduction in high-dimensional multi-objective optimisation. PhD thesis, University of Sheffield Sheffield, UK, 2010.
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Kate Smith-Miles, Mario A. Muñoz, and Neelofar. Melbourne Algorithm Test Instance Library with Data Analytics (MATILDA), 2020.
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Gunther Mäckle, Dragan A. Savic, and Godfrey A. Walters. Application of Genetic Algorithms to Pump Scheduling for Water Supply. In Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA'95, volume 414, pp.  400–405, Sheffield, UK, September 1995. IEE Conference Publication.
[ bib | DOI ]

A simple Genetic Algorithm has been applied to the scheduling of multiple pumping units in a water supply system with the objective of minimising the overall cost of the pumping operation, taking advantage of storage capacity in the system and the availability of off peak electricity tariffs. A simple example shows that the method is easy to apply and has produced encouraging preliminary results

[2256]

Nateri K. Madavan. Multiobjective optimization using a Pareto differential evolution approach. In D. B. Fogel et al., editors, Proceedings of the 2002 World Congress on Computational Intelligence (WCCI 2002), pp.  1145–1150, Piscataway, NJ, 2002. IEEE Press.
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D. R. Broad, Graeme C. Dandy, and Holger R. Maier. A Metamodeling Approach to Water Distribution System Optimization. In 6th Annual Symposium on Water Distribution Systems Analysis. ASCE, June 2004.
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Yuri Malitsky and Meinolf Sellmann. Instance-specific algorithm configuration as a method for non-model-based portfolio generation. In N. Beldiceanu, N. Jussien, and E. Pinson, editors, Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2012, volume 7298 of Lecture Notes in Computer Science, pp.  244–259. Springer, Heidelberg, Germany, 2012.
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Yuri Malitsky, Deepak Mehta, Barry O'Sullivan, and Helmut Simonis. Tuning parameters of large neighborhood search for the machine reassignment problem. In C. Gomes and M. Sellmann, editors, Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2013, volume 7874 of Lecture Notes in Computer Science, pp.  176–192. Springer, Heidelberg, Germany, 2013.
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Vittorio Maniezzo, M. Boschetti, and M. Jelasity. An Ant Approach to Membership Overlay Design. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  37–48. Springer, Heidelberg, Germany, 2004.
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Vittorio Maniezzo and M. Milandri. An Ant-Based Framework for Very Strongly Constrained Problems. In M. Dorigo et al., editors, Ant Algorithms, Third International Workshop, ANTS 2002, volume 2463 of Lecture Notes in Computer Science, pp.  222–227. Springer, Heidelberg, Germany, 2002.
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Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven J. Bethard, and David McClosky. The Stanford CoreNLP Natural Language Processing Toolkit. In Association for Computational Linguistics (ACL) System Demonstrations, pp.  55–60, 2014.
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http://www.aclweb.org/anthology/P/P14/P14-5010

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F. Martínez, V. Bou, V. Hernández, F. Alvarruiz, and J. M. Alonso. ANN Architectures for Simulating Water Distribution Networks. In D. A. Savic, G. A. Walters, R. King, and S. Thiam-Khu, editors, Proceedings of the Eighth International Conference on Computing and Control for the Water Industry (CCWI 2005), volume 1, pp.  251–256, University of Exeter, UK, September 2005.
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Marie-Eléonore Marmion, Clarisse Dhaenens, Laetitia Jourdan, Arnaud Liefooghe, and Sébastien Verel. NILS: A Neutrality-Based Iterated Local Search and Its Application to Flowshop Scheduling. In P. Merz and J.-K. Hao, editors, Proceedings of EvoCOP 2011 – 11th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 6622 of Lecture Notes in Computer Science, pp.  191–202. Springer, Heidelberg, Germany, 2011.
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Marie-Eléonore Marmion, Franco Mascia, Manuel López-Ibáñez, and Thomas Stützle. Automatic Design of Hybrid Stochastic Local Search Algorithms. In M. J. Blesa, C. Blum, P. Festa, A. Roli, and M. Sampels, editors, Hybrid Metaheuristics, volume 7919 of Lecture Notes in Computer Science, pp.  144–158. Springer, Heidelberg, Germany, 2013.
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Oded Maron and Andrew W. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems, volume 6, pp.  59–66. Morgan Kaufmann Publishers, San Francisco, CA, 1994.
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C. E. Mariano and E. Morales. MOAQ: An Ant-Q Algorithm for Multiple Objective Optimization Problems. In W. Banzhaf, J. M. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. J. Jakiela, and R. E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 1999, pp.  894–901. Morgan Kaufmann Publishers, San Francisco, CA, 1999.
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Elena Marchiori and Adri G. Steenbeek. An Iterated Heuristic Algorithm for the Set Covering Problem. In K. Mehlhorn, editor, Algorithm Engineering, 2nd International Workshop, WAE'92, pp.  155–166. Max-Planck-Institut für Informatik, Saarbrücken, Germany, 1998.
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Elena Marchiori and Adri G. Steenbeek. An Evolutionary Algorithm for Large Scale Set Covering Problems with Application to Airline Crew Scheduling. In S. Cagnoni et al., editors, Real-World Applications of Evolutionary Computing, EvoWorkshops 2000, volume 1803 of Lecture Notes in Computer Science, pp.  367–381. Springer, Heidelberg, Germany, 2000.
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K. Marriott and P. Stuckey. Programming With Constraints. MIT Press, Cambridge, MA, 1998.
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Silvano Martello and Paolo Toth. Knapsack Problems: Algorithms and Computer Implementations. John Wiley & Sons, Chichester, UK, 1990.
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Keywords: bin packing

[2272]

Oded Maron. Hoeffding Races: Model selection for MRI classification. Master's thesis, Massachusetts Institute of Technology, 1994.
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[2273]

Franco Mascia, Mauro Birattari, and Thomas Stützle. Tuning Algorithms for Tackling Large Instances: An Experimental Protocol. In P. M. Pardalos and G. Nicosia, editors, Learning and Intelligent Optimization, 7th International Conference, LION 7, volume 7997 of Lecture Notes in Computer Science, pp.  410–422. Springer, Heidelberg, Germany, 2013.
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Florence Massen, Yves Deville, and Pascal van Hentenryck. Pheromone-Based Heuristic Column Generation for Vehicle Routing Problems with Black Box Feasibility. In N. Beldiceanu, N. Jussien, and E. Pinson, editors, Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2012, volume 7298 of Lecture Notes in Computer Science, pp.  260–274. Springer, Heidelberg, Germany, 2012.
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[2275]

Franco Mascia, Manuel López-Ibáñez, Jérémie Dubois-Lacoste, and Thomas Stützle. Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools: Supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2013-009/, 2013.
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Franco Mascia, Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Marie-Eléonore Marmion, and Thomas Stützle. Algorithm Comparison by Automatically Configurable Stochastic Local Search Frameworks: A Case Study Using Flow-Shop Scheduling Problems. In M. J. Blesa, C. Blum, and S. Voß, editors, Hybrid Metaheuristics, volume 8457 of Lecture Notes in Computer Science, pp.  30–44. Springer, Heidelberg, Germany, 2014.
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[2277]

Franco Mascia, Manuel López-Ibáñez, Jérémie Dubois-Lacoste, and Thomas Stützle. From Grammars to Parameters: Automatic Iterated Greedy Design for the Permutation Flow-shop Problem with Weighted Tardiness. In P. M. Pardalos and G. Nicosia, editors, Learning and Intelligent Optimization, 7th International Conference, LION 7, volume 7997 of Lecture Notes in Computer Science, pp.  321–334. Springer, Heidelberg, Germany, 2013.
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[2278]

Florence Massen, Manuel López-Ibáñez, Thomas Stützle, and Yves Deville. Experimental Analysis of Pheromone-Based Heuristic Column Generation Using irace. In M. J. Blesa, C. Blum, P. Festa, A. Roli, and M. Sampels, editors, Hybrid Metaheuristics, volume 7919 of Lecture Notes in Computer Science, pp.  92–106. Springer, Heidelberg, Germany, 2013.
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Renzo Massobrio, Sergio Nesmachnow, and Bernabé Dorronsoro. Virtual Savant: learning for optimization. In M. Vlastelica, J. Song, A. Ferber, B. Amos, G. Martius, B. Dilkina, and Y. Yue, editors, Learning Meets Combinatorial Algorithms Workshop at NeurIPS 2020, pp.  1–5, 2020.
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Satoshi Matsubara, Motomu Takatsu, Toshiyuki Miyazawa, Takayuki Shibasaki, Yasuhiro Watanabe, Kazuya Takemoto, and Hirotaka Tamura. Digital Annealer for High-Speed Solving of Combinatorial optimization Problems and Its Applications. In 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), pp.  667–672. IEEE, 2020.
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A Digital Annealer (DA) is a dedicated architecture for high-speed solving of combinatorial optimization problems mapped to an Ising model. With fully coupled bit connectivity and high coupling resolution as a major feature, it can be used to express a wide variety of combinatorial optimization problems. The DA uses Markov Chain Monte Carlo as a basic search mechanism, accelerated by the hardware implementation of multiple speed-enhancement techniques such as parallel search, escape from a local solution, and replica exchange. It is currently being offered as a cloud service using a second-generation chip operating on a scale of 8,192 bits. This paper presents an overview of the DA, its performance against benchmarks, and application examples.

[2281]

Michael Maur, Manuel López-Ibáñez, and Thomas Stützle. Pre-scheduled and adaptive parameter variation in Max-Min Ant System. In H. Ishibuchi et al., editors, Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010), pp.  3823–3830, Piscataway, NJ, 2010. IEEE Press.
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Atanu Mazumdar, Tinkle Chugh, Kaisa Miettinen, and Manuel López-Ibáñez. On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization. In K. Deb, E. D. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, and P. Reed, editors, Evolutionary Multi-criterion Optimization, EMO 2019, volume 11411 of Lecture Notes in Computer Science, pp.  463–474. Springer International Publishing, Cham, Switzerland, 2019.
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G. McCormick and R. S. Powell. A progressive mixed integer-programming method for pump scheduling. In C. Maksimović, D. Butler, and F. A. Memon, editors, Advances in Water Supply Management, pp.  307–313. CRC Press, 2003.
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Catherine C. McGeoch and Pau Farré. The D-Wave Advantage System: An Overview. Technical report, D-Wave Systems Inc., Burnaby, BC, Canada, 2020.
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Hudson Geovane de Medeiros, Elizabeth Ferreira Gouvêa Goldbarg, and Marco Cesar Goldbarg. Analyzing Limited Size Archivers of Multi-objective Optimizers. In 2014 Brazilian Conference on Intelligent Systems, pp.  85–90, 2014.
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Keywords: archiving

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J. Fabian Meier and Uwe Clausen. A versatile heuristic approach for generalized hub location problems. Preprint, Provided upon personal request, 2014.
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Keywords: irace

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L. Melo, F. Pereira, and E. Costa. MC-ANT: a Multi-colony Ant Algorithm. In P. Collet, N. Monmarché, P. Legrand, M. Schoenauer, and E. Lutton, editors, Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009, volume 5975 of Lecture Notes in Computer Science. Springer, Heidelberg, Germany, 2010.
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Adriana Menchaca-Mendez and Carlos A. Coello Coello. GD-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Generational Distance Indicator. In A. Gaspar-Cunha, C. H. Antunes, and C. A. Coello Coello, editors, Evolutionary Multi-criterion Optimization, EMO 2015 Part I, volume 9018 of Lecture Notes in Computer Science, pp.  156–170. Springer, Heidelberg, Germany, 2015.
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Adriana Menchaca-Mendez and Carlos A. Coello Coello. GDE-MOEA: A New MOEA based on the generational distance indicator and ε-dominance. In Proceedings of the 2015 Congress on Evolutionary Computation (CEC 2015), pp.  947–955, Piscataway, NJ, 2015. IEEE Press.
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Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, and Frank Hutter. Towards automatically-tuned neural networks. In Workshop on Automatic Machine Learning, pp.  58–65, 2016.
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Olaf Mersmann, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Günther Rudolph. Exploratory Landscape Analysis. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  829–836. ACM Press, New York, NY, 2011.
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Keywords: continuous optimization, landscape analysis, instance features

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Peter Merz and Jutta Huhse. An Iterated Local Search Approach for Finding Provably Good Solutions for Very Large TSP Instances. In G. Rudolph et al., editors, Parallel Problem Solving from Nature – PPSN X, volume 5199 of Lecture Notes in Computer Science, pp.  929–939. Springer, Heidelberg, Germany, 2008.
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We give an overview of interactive methods developed for solving nonlinear multiobjective optimization problems. In interactive methods, a decision maker plays an important part and the idea is to support her/him in the search for the most preferred solution. In interactive methods, steps of an iterative solution algorithm are repeated and the decision maker progressively provides preference information so that the most preferred solution can be found. We identify three types of specifying preference information in interactive methods and give some examples of methods representing each type. The types are methods based on trade-off information, reference points and classification of objective functions.

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Keywords: JMetal, Multi-objective metaheuristics, Open source, Optimization framework

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In this research, we proposed to build an automated framework for testing interactive multiobjective optimization methods, without utilizing a value function to represent the DM's preferences. This was achieved by replacing the human DM with an artificial DM constructed from two distinct parts: the steady part and the current context. With the steady part the artificial DM tries to maintain the search towards its preferences, while at the same time the current context allows changing the direction as well as ending the solution process prematurely, mimicking actions of a human DM.

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Sabrina M. Oliveira, Mohamed Saifullah Hussin, Andrea Roli, Marco Dorigo, and Thomas Stützle. Analysis of the Population-based Ant Colony Optimization Algorithm for the TSP and the QAP. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017), pp.  1734–1741, Piscataway, NJ, 2017. IEEE Press.
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Lie Meng Pang, Hisao Ishibuchi, and Ke Shang. Algorithm configurations of MOEA/D with an unbounded external archive. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp.  1087–1094. IEEE, 2020.
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Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez, Thomas Bäck, and Hao Wang. Transfer Learning of Surrogate Models via Domain Affine Transformation. In J. Handl and X. Li, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024. ACM Press, New York, NY, 2024.
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Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez, Thomas Bäck, and Hao Wang. Transfer Learning of Surrogate Models via Domain Affine Transformation: Supplementary Material. https://doi.org/10.5281/zenodo.10608095, 2024.
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Christos H. Papadimitriou and Mihalis Yannakakis. On the Approximability of Trade-offs and Optimal Access of Web Sources. In A. Blum, editor, 41st Annual Symposium on Foundations of Computer Science, pp.  86–92. IEEE Computer Society Press, 2000.
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[2387]

Luís Paquete. Algoritmos Evolutivos Multiobjectivo para Afectação de Recursos e sua Aplicação à Geração de Horários em Universidades (Multiobjective Evolutionary Algorithms for Resource Allocation and their Application to University Timetabling). Master's thesis, University of Algarve, 2001. In Portuguese.
[ bib ]

The aim of this study is the application of multiobjective evolutionary algorithms to resource allocation problems, such as university examination timetabling and course timetabling problems. Usually, these problems are characterized by multiple conflicting objectives. A multiobjective formalization of these problems is presented, based on goals and priorities. Various aspects of evolutionary algorithms are proposed and studied for these problems, particulary, selection methods and types and parameters of mutation operator. The choice of both representation and operators is made so as not to favour excessively certain objectives with respect to others at the level of the exploration mechanism. A comparative study of performance is presented for the proposed algorithms by means of statistical inference, based on real problems of the University of Algarve. The notion of attainment functions is used as a base for the assessment of performance of multiobjective evolutionary algorithms. Finally, the evolution of the solution cost during the runs is analysed by means of attainment functions, as well.

[2388]

Luís Paquete. Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimization: Methods and Analysis. PhD thesis, FB Informatik, TU Darmstadt, Germany, 2005.
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[2389]

Luís Paquete, Marco Chiarandini, and Thomas Stützle. Pareto Local Optimum Sets in the Biobjective Traveling Salesman Problem: An Experimental Study. In X. Gandibleux, M. Sevaux, K. Sörensen, and V. T'Kindt, editors, Metaheuristics for Multiobjective Optimisation, volume 535 of Lecture Notes in Economics and Mathematical Systems, pp.  177–199. Springer, Berlin/Heidelberg, 2004.
[ bib | DOI ]

In this article, we study Pareto local optimum sets for the biobjective Traveling Salesman Problem applying straightforward extensions of local search algorithms for the single objective case. The performance of the local search algorithms is illustrated by experimental results obtained for well known benchmark instances and comparisons to methods from literature. In fact, a 3-opt local search is able to compete with the best performing metaheuristics in terms of solution quality. Finally, we also present an empirical study of the features of the solutions found by 3-opt on a set of randomly generated instances. The results indicate the existence of several clusters of near-optimal solutions that are separated by only a few edges.

Keywords: Pareto local search, PLS

[2390]

Luís Paquete, Carlos M. Fonseca, and Manuel López-Ibáñez. An optimal algorithm for a special case of Klee's measure problem in three dimensions. Technical Report CSI-RT-I-01/2006, CSI, Universidade do Algarve, 2006. Superseded by paper in IEEE Transactions on Evolutionary Computation [127].
[ bib ]

The measure of the region dominated by (the maxima of) a set of n points in the positive d-orthant has been proposed as an indicator of performance in multiobjective optimization, known as the hypervolume indicator, and the problem of computing it efficiently is attracting increasing attention. In this report, this problem is formulated as a special case of Klee's measure problem in d dimensions, which immediately establishes O(n^d/2log n) as a, possibly conservative, upper bound on the required computation time. Then, an O(n log n) algorithm for the 3-dimensional version of this special case is constructed, based on an existing dimension-sweep algorithm for the related maxima problem. Finally, O(n log n) is shown to remain a lower bound on the time required by the hypervolume indicator for d>1, which attests the optimality of the algorithm proposed.

Proof of Theorem 3.1 is incorrect

[2391]

Luís Paquete and Thomas Stützle. Clusters of non-dominated solutions in multiobjective combinatorial optimization: An experimental analysis. In V. Barichard, M. Ehrgott, X. Gandibleux, and V. T'Kindt, editors, Multiobjective Programming and Goal Programming: Theoretical Results and Practical Applications, volume 618 of Lecture Notes in Economics and Mathematical Systems, pp.  69–77. Springer, Berlin, Germany, 2009.
[ bib | DOI ]

[2392]

Luís Paquete and Thomas Stützle. An Experimental Investigation of Iterated Local Search for Coloring Graphs. In S. Cagnoni et al., editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2002, volume 2279 of Lecture Notes in Computer Science, pp.  122–131. Springer, Heidelberg, Germany, 2002.
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Luís Paquete and Thomas Stützle. A Two-Phase Local Search for the Biobjective Traveling Salesman Problem. In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors, Evolutionary Multi-criterion Optimization, EMO 2003, volume 2632 of Lecture Notes in Computer Science, pp.  479–493. Springer, Heidelberg, Germany, 2003.
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Luís Paquete and Thomas Stützle. Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimization: A Review. In T. F. Gonzalez, editor, Handbook of Approximation Algorithms and Metaheuristics, pp.  411–425. Chapman & Hall/CRC, Boca Raton, FL, 2018.
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[2395]

Luís Paquete, Thomas Stützle, and Manuel López-Ibáñez. On the design and analysis of SLS algorithms for multiobjective combinatorial optimization problems. Technical Report TR/IRIDIA/2005-029, IRIDIA, Université Libre de Bruxelles, Belgium, 2005.
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Effective Stochastic Local Search (SLS) algorithms can be seen as being composed of several algorithmic components, each of which plays some specific role with respect to overall performance. In this article, we explore the application of experimental design techniques to analyze the effect of different choices for these algorithmic components on SLS algorithms applied to Multiobjective Combinatorial Optimization Problems that are solved in terms of Pareto optimality. This analysis is done using the example application of SLS algorithms to the biobjective Quadratic Assignment Problem and we show also that the same choices for algorithmic components can lead to different behavior in dependence of various instance features, such as the structure of input data and the correlation between objectives.

[2396]

Luís Paquete, Thomas Stützle, and Manuel López-Ibáñez. Towards the Empirical Analysis of SLS Algorithms for Multiobjective Combinatorial Optimization Problems through Experimental Design. In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F. Hartl, and M. Reimann, editors, 6th Metaheuristics International Conference (MIC 2005), pp.  739–746, Vienna, Austria, 2005.
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Stochastic Local Search (SLS) algorithms for Multiobjective Combinatorial Optimization Problems (MCOPs) typically involve the selection and parameterization of many algorithm components whose role with respect to their overall performance and relation to certain instance features is often not clear. In this abstract, we use a modular approach for the design of SLS algorithms for MCOPs defined in terms of Pareto optimality and we present an extensive analysis of SLS algorithms through experimental design techniques, where each algorithm component is considered a factor. The experimental analysis is based on a sound experimental methodology for analyzing the output of algorithms for MCOPs. We show that different choices for algorithm components can lead to different behavior in dependence of various instance features.

[2397]

Luís Paquete, Thomas Stützle, and Manuel López-Ibáñez. Using experimental design to analyze stochastic local search algorithms for multiobjective problems. In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F. Hartl, and M. Reimann, editors, Metaheuristics: Progress in Complex Systems Optimization, volume 39 of Operations Research / Computer Science Interfaces, pp.  325–344. Springer, New York, NY, 2007.
[ bib | DOI ]

Stochastic Local Search (SLS) algorithms can be seen as being composed of several algorithmic components, each playing some specific role with respect to overall performance. This article explores the application of experimental design techniques to analyze the effect of components of SLS algorithms for Multiobjective Combinatorial Optimization problems, in particular for the Biobjective Quadratic Assignment Problem. The analysis shows that there exists a strong dependence between the choices for these components and various instance features, such as the structure of the input data and the correlation between the objectives.

Post-Conference Proceedings of the 6th Metaheuristics International Conference (MIC 2005)

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Juan A. Pedraza, Carlos García-Martínez, Alberto Cano, and Sebastián Ventura. Classification Rule Mining with Iterated Greedy. In M. M. Polycarpou, A. C. P. L. F. de Carvalho, J. Pan, M. Wozniak, H. Quintián, and E. Corchado, editors, Hybrid Artificial Intelligence Systems - 9th International Conference, HAIS 2014, Salamanca, Spain, June 11-13, 2014. Proceedings, volume 8480 of Lecture Notes in Computer Science, pp.  585–596. Springer, Heidelberg, Germany, 2014.
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Luciana R. Pedro and R. H. C. Takahashi. Decision-Maker Preference Modeling in Interactive Multiobjective Optimization. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, editors, Evolutionary Multi-criterion Optimization, EMO 2013, volume 7811 of Lecture Notes in Computer Science, pp.  811–824. Springer, Heidelberg, Germany, 2013.
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Keywords: decision-maker, interactive, neural networks

[2408]

Paola Pellegrini and Mauro Birattari. Implementation Effort and Performance. In T. Stützle, M. Birattari, and H. H. Hoos, editors, Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2007, volume 4638 of Lecture Notes in Computer Science, pp.  31–45. Springer, Heidelberg, Germany, 2007.
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Paola Pellegrini, D. Favaretto, and E. Moretti. On Max-Min Ant System's Parameters. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, volume 4150 of Lecture Notes in Computer Science, pp.  203–214. Springer, Heidelberg, Germany, 2006.
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Paola Pellegrini, D. Favaretto, and E. Moretti. Exploration in stochastic algorithms: An application on Max-Min Ant System. In N. Krasnogor, B. Melián-Batista, J. A. Moreno-Pérez, J. M. Moreno-Vega, and D. A. Pelta, editors, Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), volume 236 of Studies in Computational Intelligence, pp.  1–13. Springer, Berlin, Germany, 2009.
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Paola Pellegrini, Thomas Stützle, and Mauro Birattari. Off-line vs. On-line Tuning: A Study on Max-Min Ant System for the TSP. In M. Dorigo et al., editors, Swarm Intelligence, 7th International Conference, ANTS 2010, volume 6234 of Lecture Notes in Computer Science, pp.  239–250. Springer, Heidelberg, Germany, 2010.
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Leslie Pérez Cáceres, Bernd Bischl, and Thomas Stützle. Evaluating random forest models for irace. In P. A. N. Bosman, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017, pp.  1146–1153. ACM Press, New York, NY, 2017.
[ bib | DOI ]

[2413]

Leslie Pérez Cáceres, Manuel López-Ibáñez, Holger H. Hoos, and Thomas Stützle. An Experimental Study of Adaptive Capping in irace. In R. Battiti, D. E. Kvasov, and Y. D. Sergeyev, editors, Learning and Intelligent Optimization, 11th International Conference, LION 11, volume 10556 of Lecture Notes in Computer Science, pp.  235–250. Springer, Cham, Switzerland, 2017.
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Leslie Pérez Cáceres, Manuel López-Ibáñez, Holger H. Hoos, and Thomas Stützle. An experimental study of adaptive capping in irace: Supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2016-007/, 2017.
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Leslie Pérez Cáceres, Manuel López-Ibáñez, and Thomas Stützle. Ant Colony Optimization on a Budget of 1000. In M. Dorigo et al., editors, Swarm Intelligence, 9th International Conference, ANTS 2014, volume 8667 of Lecture Notes in Computer Science, pp.  50–61. Springer, Heidelberg, Germany, 2014.
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Leslie Pérez Cáceres, Manuel López-Ibáñez, and Thomas Stützle. An Analysis of Parameters of irace. In C. Blum and G. Ochoa, editors, Proceedings of EvoCOP 2014 – 14th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 8600 of Lecture Notes in Computer Science, pp.  37–48. Springer, Heidelberg, Germany, 2014.
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[2417]

Leslie Pérez Cáceres, Manuel López-Ibáñez, and Thomas Stützle. Ant Colony Optimization on a Budget of 1000: Supplementary material, 2015.
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Leslie Pérez Cáceres, Federico Pagnozzi, Alberto Franzin, and Thomas Stützle. Automatic Configuration of GCC Using irace. In E. Lutton, P. Legrand, P. Parrend, N. Monmarché, and M. Schoenauer, editors, EA 2017: Artificial Evolution, volume 10764 of Lecture Notes in Computer Science, pp.  202–216. Springer, Heidelberg, Germany, 2017.
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Automatic algorithm configuration techniques have proved to be successful in finding performance-optimizing parameter settings of many search-based decision and optimization algorithms. A recurrent, important step in software development is the compilation of source code written in some programming language into machine-executable code. The generation of performance-optimized machine code itself is a difficult task that can be parametrized in many different possible ways. While modern compilers usually offer different levels of optimization as possible defaults, they have a larger number of other flags and numerical parameters that impact properties of the generated machine-code. While the generation of performance-optimized machine code has received large attention and is dealt with in the research area of auto-tuning, the usage of standard automatic algorithm configuration software has not been explored, even though, as we show in this article, the performance of the compiled code has significant stochasticity, just as standard optimization algorithms. As a practical case study, we consider the configuration of the well-known GNU compiler collection (GCC) for minimizing the run-time of machine code for various heuristic search methods. Our experimental results show that, depending on the specific code to be optimized, improvements of up to 40% of execution time when compared to the -O2 and -O3 optimization flags is possible.

[2419]

Leslie Pérez Cáceres, Federico Pagnozzi, Alberto Franzin, and Thomas Stützle. Automatic configuration of GCC using irace: Supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2017-009/, 2017.
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Leslie Pérez Cáceres and Thomas Stützle. Automatic Algorithm Configuration: Analysis, Improvements and Applications. PhD thesis, IRIDIA, École polytechnique, Université Libre de Bruxelles, Belgium, 2017.
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Supervised by Thomas Stützle and Manuel López-Ibáñez

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James E. Pettinger and Richard M. Everson. Controlling genetic algorithms with reinforcement learning. In W. B. Langdon et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2002, pp.  692–692. Morgan Kaufmann Publishers, San Francisco, CA, 2002.
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Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. MIT Press, 2017.
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Frank Phillipson and Harshil Singh Bhatia. Portfolio Optimisation Using the D-Wave Quantum Annealer. In M. Paszynski, D. Kranzlmüller, V. V. Krzhizhanovskaya, J. J. Dongarra, and P. M. A. Sloot, editors, Computational Science – ICCS 2021, pp.  45–59. Springer International Publishing, Cham, Switzerland, 2021.
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Josef Pihera and Nysret Musliu. Application of Machine Learning to Algorithm Selection for TSP. In G. A. Papadopoulos, editor, 26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014, Limassol, Cyprus, November 10-12, 2014, pp.  47–54. IEEE Press, 2014.
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Pedro Pinto, Thomas Runkler, and João Sousa. Ant Colony Optimization and its Application to Regular and Dynamic MAX-SAT Problems. In Advances in Biologically Inspired Information Systems, volume 69 of Studies in Computational Intelligence, pp.  285–304. Springer, Berlin, Germany, 2007.
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In this chapter we discuss the ant colony optimization meta-heuristic (ACO) and its application to static and dynamic constraint satisfaction optimization problems, in particular the static and dynamic maximum satisfiability problems (MAX-SAT). In the first part of the chapter we give an introduction to meta-heuristics in general and ant colony optimization in particular, followed by an introduction to constraint satisfaction and static and dynamic constraint satisfaction optimization problems. Then, we describe how to apply the ACO algorithm to the problems, and do an analysis of the results obtained for several benchmarks. The adapted ant colony optimization accomplishes very well the task of dealing with systematic changes of dynamic MAX-SAT instances derived from static problems.

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Joelle Pineau and Koustuv Sinha. The Machine Learning Reproducibility Checklist (v2.0). https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist-v2.0.pdf, 2020.
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Used in NeurIPS 2020

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David Pisinger and Stefan Ropke. Large Neighborhood Search. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 146 of International Series in Operations Research & Management Science, pp.  399–419. Springer, New York, NY, 2nd edition, 2010.
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Erik Pitzer, Andreas Beham, and Michael Affenzeller. Automatic Algorithm Selection for the Quadratic Assignment Problem Using Fitness Landscape Analysis. In M. Middendorf and C. Blum, editors, Proceedings of EvoCOP 2013 – 13th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 7832 of Lecture Notes in Computer Science, pp.  109–120. Springer, Heidelberg, Germany, 2013.
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Dmitry Plotnikov, Dmitry Melnik, Mamikon Vardanyan, Ruben Buchatskiy, Roman Zhuykov, and Je-Hyung Lee. Automatic Tuning of Compiler Optimizations and Analysis of their Impact. In V. Alexandrov, M. Lees, V. Krzhizhanovskaya, J. Dongarra, and P. M. A. Sloot, editors, 2013 International Conference on Computational Science, volume 18 of Procedia Computer Science, pp.  1312–1321. Elsevier, 2013.
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Robert Plotnick. The Genie in the Machine: How Computer-Automated Inventing Is Revolutionizing Law and Business. Stanford Law Books, 2009.
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Mentions evolutionary optimization of Oral-B toothbrushes

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Proposed COBYLA

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Raphael Patrick Prager, Heike Trautmann, Hao Wang, Thomas Bäck, and Pascal Kerschke. Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. In C. A. Coello Coello, editor, 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, Canberra, Australia, December 1-4, 2020, pp.  996–1003. IEEE Press, 2020.
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Stefan Pricopie, Richard Allmendinger, Manuel López-Ibáñez, Clyde Fare, Matt Benatan, and Joshua D. Knowles. Expensive Optimization with Production-Graph Resource Constraints: A First Look at a New Problem Class. In J. E. Fieldsend and M. Wagner, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022, pp.  840–848. ACM Press, New York, NY, 2022.
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We consider a new class of expensive, resource-constrained optimization problems (here arising from molecular discovery) where costs are associated with the experiments (or evaluations) to be carried out during the optimization process. In the molecular discovery problem, candidate compounds to be optimized must be synthesized in an iterative process that starts from a set of purchasable items and builds up to larger molecules. To produce target molecules, their required resources are either used from already-synthesized items in storage or produced themselves on-demand at an additional cost. Any remaining resources from the production process are stored for reuse for the next evaluations. We model these resource dependencies with a directed acyclic production graph describing the development process from granular purchasable items to evaluable target compounds. Moreover, we develop several resource-eficient algorithms to address this problem. In particular, we develop resource-aware variants of Random Search heuristics and of Bayesian Optimization and analyze their performance in terms of anytime behavior. The experimental results were obtained from a real-world molecular optimization problem. Our results suggest that algorithms that encourage exploitation by reusing existing resources achieve satisfactory results while using fewer resources overall.

Keywords: molecular discovery, resource constraints, expensive optimization, production costs

[2440]

Stefan Pricopie, Richard Allmendinger, Manuel López-Ibáñez, Clyde Fare, Matt Benatan, and Joshua D. Knowles. An Adaptive Approach to Bayesian Optimization with Setup Switching Costs. In M. Affenzeller, S. M. Winkler, A. V. Kononova, H. Trautmann, T. Tušar, P. Machado, and T. Bäck, editors, Parallel Problem Solving from Nature – PPSN XVIII, volume 15149 of Lecture Notes in Computer Science, pp.  340–355. Springer, Cham, Switzerland, 2024.
[ bib | DOI ]

Black-box optimization methods typically assume that evaluations of the black-box objective function are equally costly to evaluate. We investigate here a resource-constrained setting where changes to certain decision variables of the search space incur a higher switching cost, e.g., due to expensive changes to the experimental setup. In this scenario, there is a trade-off between fixing the values of those costly variables or accepting this additional cost to explore more of the search space. We adapt two process-constrained batch algorithms to this sequential problem formulation, and propose two new methods: one one cost-aware and one cost-ignorant. We validate and compare the algorithms using a set of 7 scalable test functions with different switching-cost settings. Our proposed cost-aware parameter-free algorithm yields comparable results to tuned process-constrained algorithms in all settings we considered, suggesting some degree of robustness to varying landscape features and cost trade-offs. This method starts to outperform the other algorithms with increasing switching cost. Our work expands on other recent Bayesian Optimization studies in resource-constrained settings that consider a batch setting only. Although the contributions of this work are relevant to the general class of resource-constrained problems, they are particularly relevant to problems where adaptability to varying resource availability is of high importance.

[2441]

Kenneth Price, Rainer M. Storn, and Jouni A. Lampinen. Differential Evolution: A Practical Approach to Global Optimization. Springer, New York, NY, 2005.
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Andy Pryke, Sanaz Mostaghim, and Alireza Nazemi. Heatmap visualization of population based multi objective algorithms. In S. Obayashi et al., editors, Evolutionary Multi-criterion Optimization, EMO 2007, volume 4403 of Lecture Notes in Computer Science, pp.  361–375. Springer, Heidelberg, Germany, 2007.
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Gregorio Toscano Pulido and Carlos A. Coello Coello. The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization. In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors, Evolutionary Multi-criterion Optimization, EMO 2003, volume 2632 of Lecture Notes in Computer Science, pp.  252–266. Springer, Heidelberg, Germany, 2003.
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Robin C. Purshouse, Kalyanmoy Deb, Maszatul M. Mansor, Sanaz Mostaghim, and Rui Wang. A review of hybrid evolutionary multiple criteria decision making methods. COIN Report 2014005, Computational Optimization and Innovation (COIN) Laboratory, University of Michigan, USA, January 2014.
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Robin C. Purshouse and Peter J. Fleming. Evolutionary many-objective optimisation: an exploratory analysis. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03), pp.  2066–2073, Piscataway, NJ, December 2003. IEEE Press.
[ bib | DOI ]

First to mention NSGA-II failure to deal with many-objectives. Mentions exponential number of nondominated solutions with respect to many objectives (but [1842] already did).

[2446]

Markus Püschel, Franz Franchetti, and Yevgen Voronenko. Spiral. In D. Padua, editor, Encyclopedia of Parallel Computing, pp.  1920–1933. Springer, US, 2011.
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Yasha Pushak and Holger H. Hoos. Algorithm Configuration Landscapes: More Benign Than Expected? In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, and D. Whitley, editors, Parallel Problem Solving from Nature – PPSN XV, volume 11101 of Lecture Notes in Computer Science, pp.  271–283. Springer, Cham, Switzerland, 2018.
[ bib | DOI | supplementary material ]

Best paper award at PPSN2018

[2448]

Yasha Pushak and Holger H. Hoos. Golden parameter search: exploiting structure to quickly configure parameters in parallel. In C. A. Coello Coello, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020, pp.  245–253. ACM Press, New York, NY, 2020.
[ bib | DOI | epub ]

Keywords: algorithm configuration

[2449]

Bernd Bischl, Michel Lang, Jakob Bossek, Daniel Horn, Karin Schork, Jakob Richter, and Pascal Kerschke. ParamHelpers : Helpers for Parameters in Black-Box Optimization, Tuning and Machine Learning, 2017. R package version 1.10.
[ bib | http ]

[2450]

Hao Yu. Rmpi: Interface (Wrapper) to MPI (Message-Passing Interface), 2010. R package version 0.5-8.
[ bib | http ]

[2451]

Thomas Bartz-Beielstein, J. Ziegenhirt, W. Konen, O. Flasch, P. Koch, and Martin Zaefferer. SPOT: Sequential Parameter Optimization, 2011. R package.
[ bib | http ]

[2452]

Heike Trautmann, Olaf Mersmann, and David Arnu. cmaes: Covariance Matrix Adapting Evolutionary Strategy, 2011. R package.
[ bib | http ]

[2453]

Rob Carnell. lhs: Latin Hypercube Samples, 2016. R package version 0.14.
[ bib | http ]

[2454]

Olaf Mersmann. mco: Multiple Criteria Optimization Algorithms and Related Functions, 2014. R package version 1.0-15.1.
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[2455]

Bernd Bischl, Michel Lang, Jakob Bossek, Leonard Judt, Jakob Richter, Tobias Kuehn, and Erich Studerus. mlr: Machine Learning in R, 2013. R package.
[ bib | http ]

[2456]

Simon Urbanek. multicore: Parallel Processing of R Code on Machines with Multiple Cores or CPUs, 2010. R package version 0.1-3.
[ bib | http ]

[2457]

Jakob Bossek. smoof: Single and Multi-Objective Optimization Test Functions, 2016. R package version 1.2.
[ bib | http ]

[2458]

L. Rachmawati and D. Srinivasan. Preference incorporation in multiobjective evolutionary algorithms: A survey. In Proceedings of the 2006 Congress on Evolutionary Computation (CEC 2006), pp.  3385–3391, Piscataway, NJ, July 2006. IEEE Press.
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[2459]

Andreea Radulescu, Manuel López-Ibáñez, and Thomas Stützle. Automatically Improving the Anytime Behaviour of Multiobjective Evolutionary Algorithms. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, editors, Evolutionary Multi-criterion Optimization, EMO 2013, volume 7811 of Lecture Notes in Computer Science, pp.  825–840. Springer, Heidelberg, Germany, 2013.
[ bib | DOI ]

[2460]

Alma A. M. Rahat, Richard M. Everson, and Jonathan E. Fieldsend. Alternative infill strategies for expensive multi-objective optimisation. In P. A. N. Bosman, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pp.  873–880. ACM Press, New York, NY, 2017.
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[2461]

Marcus Randall. Near Parameter Free Ant Colony Optimisation. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  374–381. Springer, Heidelberg, Germany, 2004.
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[2462]

Marcus Randall and James Montgomery. Candidate Set Strategies for Ant Colony Optimisation. In M. Dorigo et al., editors, Ant Algorithms, Third International Workshop, ANTS 2002, volume 2463 of Lecture Notes in Computer Science, pp.  243–249. Springer, Heidelberg, Germany, 2002.
[ bib ]

[2463]

Zhengfu Rao, Jon Wicks, and Sue West. ENCOMS - An Energy Cost Minimisation System for Real-Time, Operational Control of Water Distribution Networks. In D. A. Savic, G. A. Walters, R. King, and S. Thiam-Khu, editors, Proceedings of the Eighth International Conference on Computing and Control for the Water Industry (CCWI 2005), volume 1, pp.  85–90, University of Exeter, UK, September 2005.
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[2464]

Elena Raponi, Hao Wang, Mariusz Bujny, Simonetta Boria, and Carola Doerr. High Dimensional Bayesian Optimization Assisted by Principal Component Analysis. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. T. M. Emmerich, and H. Trautmann, editors, Parallel Problem Solving from Nature – PPSN XVI, volume 12269 of Lecture Notes in Computer Science, pp.  169–183. Springer, Cham, Switzerland, 2020.
[ bib | DOI ]

[2465]

Jussi Rasku, Nysret Musliu, and Tommi Kärkkäinen. Automating the Parameter Selection in VRP: An Off-line Parameter Tuning Tool Comparison. In W. Fitzgibbon, Y. A. Kuznetsov, P. Neittaanmäki, and O. Pironneau, editors, Modeling, Simulation and Optimization for Science and Technology, volume 34 of Computational Methods in Applied Sciences, pp.  191–209. Springer, 2014.
[ bib | DOI ]

Keywords: irace

[2466]

Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, 2006.
[ bib ]

Keywords: Gaussian processes, data processing

[2467]

N. Rayner. Maverick Research: Judgment Day, or Why We Should Let Machines Automate Decision Making. Gartner research note, Gartner, Inc, October 2011.
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[2468]

Ingo Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. PhD thesis, Department of Process Engineering, Technical University of Berlin, 1971.
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[2469]

Ingo Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart, Germany, 1973.
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[2470]

Colin R. Reeves. Genetic algorithms. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 146 of International Series in Operations Research & Management Science, chapter 5, pp.  109–140. Springer, New York, NY, 2nd edition, 2010.
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[2471]

Patrick M. Reed. Many-Objective Visual Analytics: Rethinking the Design of Complex Engineered Systems. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, editors, Evolutionary Multi-criterion Optimization, EMO 2013, volume 7811 of Lecture Notes in Computer Science, pp.  1–1. Springer, Heidelberg, Germany, 2013.
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[2472]

Marc Reimann. Guiding ACO by Problem Relaxation: A Case Study on the Symmetric TSP. In T. Bartz-Beielstein, M. J. Blesa, C. Blum, B. Naujoks, A. Roli, G. Rudolph, and M. Sampels, editors, Hybrid Metaheuristics, volume 4771 of Lecture Notes in Computer Science, pp.  45–56. Springer, Heidelberg, Germany, 2007.
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[2473]

Gerhard Reinelt. The Traveling Salesman: Computational Solutions for TSP Applications, volume 840 of Lecture Notes in Computer Science. Springer, Heidelberg, Germany, 1994.
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[2474]

Mauricio G. C. Resende and Celso C. Ribeiro. Greedy Randomized Adaptive Search Procedures. In F. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pp.  219–249. Kluwer Academic Publishers, Norwell, MA, 2002.
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[2475]

Mauricio G. C. Resende and Celso C. Ribeiro. Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 146 of International Series in Operations Research & Management Science, pp.  283–319. Springer, New York, NY, 2nd edition, 2010.
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[2476]

Margarita Reyes Sierra and Carlos A. Coello Coello. Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ε-Dominance. In C. A. Coello Coello, A. Hernández Aguirre, and E. Zitzler, editors, Evolutionary Multi-criterion Optimization, EMO 2005, volume 3410 of Lecture Notes in Computer Science, pp.  505–519. Springer, Berlin/Heidelberg, 2005.
[ bib ]

Keywords: OMOPSO

[2477]

Mona Riabacke, Mats Danielson, Love Ekenberg, and Aron Larsson. A Prescriptive Approach for Eliciting Imprecise Weight Statements in an MCDA Process. In F. Rossi and A. Tsoukiàs, editors, Algorithmic Decision Theory, First International Conference, ADT 2009, volume 5783 of Lecture Notes in Computer Science, pp.  168–179. Springer, Heidelberg, Germany, 2009.
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[2478]

Enda Ridge and Daniel Kudenko. Tuning the Performance of the MMAS Heuristic. In T. Stützle, M. Birattari, and H. H. Hoos, editors, Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2007, volume 4638 of Lecture Notes in Computer Science, pp.  46–60. Springer, Heidelberg, Germany, 2007.
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[2479]

Enda Ridge and Daniel Kudenko. Tuning an Algorithm Using Design of Experiments. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pp.  265–286. Springer, Berlin/Heidelberg, 2010.
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[2480]

Sander van Rijn, Hao Wang, Matthijs van Leeuwen, and Thomas Bäck. Evolving the structure of Evolution Strategies. In X. Chen and A. Stafylopatis, editors, Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, pp.  1–8, 2016.
[ bib | DOI ]

Keywords: automated design, automatic configuration, CMA-ES, Gaussian distribution

[2481]

R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2008.
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[2482]

Tea Robič and Bogdan Filipič. DEMO: Differential Evolution for Multiobjective Optimization. In C. A. Coello Coello, A. Hernández Aguirre, and E. Zitzler, editors, Evolutionary Multi-criterion Optimization, EMO 2005, volume 3410 of Lecture Notes in Computer Science, pp.  520–533. Springer, Berlin/Heidelberg, 2005.
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Francisco J. Rodríguez, Christian Blum, Manuel Lozano, and Carlos García-Martínez. Iterated Greedy Algorithms for the Maximal Covering Location Problem. In J.-K. Hao and M. Middendorf, editors, Proceedings of EvoCOP 2012 – 12th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 7245 of Lecture Notes in Computer Science, pp.  172–181. Springer, Heidelberg, Germany, 2012.
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[2484]

Cynthia A. Rodríguez Villalobos and Carlos A. Coello Coello. A new multi-objective evolutionary algorithm based on a performance assessment indicator. In T. Soule and J. H. Moore, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012, pp.  505–512. ACM Press, New York, NY, 2012.
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Peter Ross. Hyper-Heuristics. In E. K. Burke and G. Kendall, editors, Search Methodologies, pp.  529–556. Springer, Boston, MA, 2005.
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[2486]

Frank Rubin. An Iterative Technique for Printed Wire Routing. In DAC'74, Proceedings of the 11th Design Automation Workshop, pp.  308–313. IEEE Press, 1974.
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Günther Rudolph and Alexandru Agapie. Convergence Properties of Some Multi-Objective Evolutionary Algorithms. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC'00), volume 2, pp.  1010–1016, Piscataway, NJ, July 2000. IEEE Press.
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[2488]

Isaac Rudich, Quentin Cappart, and Louis-Martin Rousseau. Peel-And-Bound: Generating Stronger Relaxed Bounds with Multivalued Decision Diagrams. In C. Solnon, editor, Principles and Practice of Constraint Programming, volume 235 of LIPIcs, pp.  35:1–35:20. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Germany, 2022.
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[2489]

Günther Rudolph, Heike Trautmann, Soumyadip Sengupta, and Oliver Schütze. Evenly spaced Pareto front approximations for tricriteria problems based on triangulation. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, editors, Evolutionary Multi-criterion Optimization, EMO 2013, volume 7811 of Lecture Notes in Computer Science, pp.  443–458. Springer, Heidelberg, Germany, 2013.
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unbounded archiver, AA_{Δ_1}

[2490]

Günther Rudolph. Globale Optimierung mit parallelen Evolutionsstrategien. Diplomarbeit, Department of Computer Science, University of Dortmund, July 1990.
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Proposed the generalized Rastrigin function

[2491]

Günther Rudolph. On Correlated Mutations in Evolution Strategies. In R. Männer and B. Manderick, editors, Parallel Problem Solving from Nature – PPSN II, pp.  107–116. Elsevier, 1992.
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[2492]

Günther Rudolph. Convergence of non-elitist strategies. In Z. Michalewicz, editor, Proceedings of the First IEEE International Conference on Evolutionary Computation (ICEC'94), pp.  63–66. IEEE Press, Piscataway, NJ, 1994.
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Günther Rudolph. Evolutionary Search for Minimal Elements in Partially Ordered Finite Sets. In V. W. Porto, N. Saravanan, D. E. Waagen, and A. E. Eiben, editors, International Conference on Evolutionary Programming, volume 1447 of Lecture Notes in Computer Science, pp.  345–353. Springer, 1998.
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[2494]

Ana Belén Ruiz, Mariano Luque, Kaisa Miettinen, and Rubén Saborido. An Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA. In A. Gaspar-Cunha, C. H. Antunes, and C. A. Coello Coello, editors, Evolutionary Multi-criterion Optimization, EMO 2015 Part II, volume 9019 of Lecture Notes in Computer Science, pp.  249–263. Springer, Heidelberg, Germany, 2015.
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[2495]

Rubén Ruiz, Eva Vallada, and Carlos Fernández-Martínez. Scheduling in flowshops with no-idle machines. In Computational intelligence in flow shop and job shop scheduling, pp.  21–51. Springer, 2009.
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W. Ruml. Incomplete Tree Search using Adaptive Probing. In B. Nebel, editor, Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01), pp.  235–241. IEEE Press, 2001.
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Stuart J. Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, volume 2. Prentice Hall, Englewood Cliffs, NJ, 2003.
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John Rust. Structural estimation of Markov decision processes. In Handbook of Econometrics, volume 4, pp.  3081–3143. Elsevier, 1994.
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[2499]

Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Krajzewicz. SUMO - Simulation of Urban MObility: An Overview. In SIMUL 2011, The Third International Conference on Advances in System Simulation, pp.  63–68, Barcelona, Spain, 2011. ThinkMind.
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[2500]

Bhupinder Singh Saini, Manuel López-Ibáñez, and Kaisa Miettinen. Automatic Surrogate Modelling Technique Selection based on Features of Optimization Problems. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019, pp.  1765–1772. ACM Press, New York, NY, 2019.
[ bib | DOI ]

A typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the literature, there is no standard procedure that will select the best technique for a given problem. In this work, we propose the automatic selection of a surrogate modelling technique based on exploratory landscape features of the optimization problem that underlies the given dataset. The overall idea is to learn offline from a large pool of benchmark problems, on which we can evaluate a large number of surrogate modelling techniques. When given a new dataset, features are used to select the most appropriate surrogate modelling technique. The preliminary experiments reported here suggest that the proposed automatic selector is able to identify high-accuracy surrogate models as long as an appropriate classifier is used for selection.

[2501]

Yoshitaka Sakurai, Kouhei Takada, Takashi Kawabe, and Setsuo Tsuruta. A method to control parameters of evolutionary algorithms by using reinforcement learning. In 2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems, pp.  74–79. IEEE, 2010.
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[2502]

A. Burcu Altan Sakarya, Fred E. Goldman, and Larry W. Mays. Models for the optimal scheduling of pumps to meet water quality. In D. A. Savic and G. A. Walters, editors, Water Industry Systems: Modelling and Optimization Applications, volume 2, pp.  379–391. Research Studies Press Ltd., Baldock, United Kingdom, 1999.
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[2503]

Francesco Sambo, Barbara Di Camillo, Alberto Franzin, Andrea Facchinetti, Liisa Hakaste, Jasmina Kravic, Giuseppe Fico, Jaakko Tuomilehto, Leif Groop, Rafael Gabriel, Tiinamaija Tuomi, and Claudio Cobelli. A Bayesian Network analysis of the probabilistic relations between risk factors in the predisposition to type 2 diabetes. In N. Lovell and L. Mainardi, editors, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Proceedings, Annual International Conference of the IEEE Engineering in Medicine and Biology, pp.  2119–2122. IEEE Press, 2015.
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[2504]

Valentino Santucci and Marco Baioletti. A Fast Randomized Local Search for Low Budget Optimization in Black-Box Permutation Problems. In Proceedings of the 2022 World Congress on Computational Intelligence (WCCI 2022), Piscataway, NJ, 2022. IEEE Press.
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Keywords: UMM, CEGO

[2505]

Thomas J. Santner, Brian J. Williams, and William I. Notz. The Design and Analysis of Computer Experiments. Springer Verlag, New York, NY, 2003.
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[2506]

Kaz Sato and Cliff Young. An in-depth look at Google's first Tensor Processing Unit (TPU). https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu, 2017.
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Dragan A. Savic, Godfrey A. Walters, and Martin Schwab. Multiobjective Genetic Algorithms for Pump Scheduling in Water Supply. In D. Corne and J. L. Shapiro, editors, Evolutionary Computing Workshop, AISB'97, volume 1305 of Lecture Notes in Computer Science, pp.  227–236. Berlin, Germany, 1997.
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Y. Sawaragi, H. Nakayama, and T. Tanino. Theory of multiobjective optimization. Elsevier, 1985.
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[2509]

Dhish Kumar Saxena and Kalyanmoy Deb. Non-linear Dimensionality Reduction Procedures for Certain Large-Dimensional Multi-objective Optimization Problems: Employing Correntropy and a Novel Maximum Variance Unfolding. In S. Obayashi et al., editors, Evolutionary Multi-criterion Optimization, EMO 2007, volume 4403 of Lecture Notes in Computer Science, pp.  772–787. Springer, Heidelberg, Germany, 2007.
[ bib | DOI ]

In our recent publication, we began with an understanding that many real-world applications of multi-objective optimization involve a large number (10 or more) of objectives but then, existing evolutionary multi-objective optimization (EMO) methods have primarily been applied to problems having smaller number of objectives (5 or less). After highlighting the major impediments in handling large number of objectives, we proposed a principal component analysis (PCA) based EMO procedure, for dimensionality reduction, whose efficacy was demonstrated by solving upto 50-objective optimization problems. Here, we are addressing the fact that, when the data points live on a non-linear manifold or that the data structure is non-gaussian, PCA which yields a smaller dimensional 'linear' subspace may be ineffective in revealing the underlying dimensionality. To overcome this, we propose two new non-linear dimensionality reduction algorithms for evolutionary multi-objective optimization, namely C-PCA-NSGA-II and MVU-PCA-NSGA-II. While the former is based on the newly introduced correntropy PCA [2], the later implements maximum variance unfolding principle [3,4,5] in a novel way. We also establish the superiority of these new EMO procedures over the earlier PCA-based procedure, both in terms of accuracy and computational time, by solving upto 50-objective optimization problems.

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Dhish Kumar Saxena and Kalyanmoy Deb. Trading on infeasibility by exploiting constraint's criticality through multi-objectivization: A system design perspective. In Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007), pp.  919–926, Piscataway, NJ, 2007. IEEE Press.
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Keywords: multi-objectivization

[2511]

Dhish Kumar Saxena and Kalyanmoy Deb. Dimensionality reduction of objectives and constraints in multi-objective optimization problems: A system design perspective. In Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008), pp.  3204–3211, Piscataway, NJ, 2008. IEEE Press.
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Download a scanned copy from: http://gpbib.cs.ucl.ac.uk/icga/

Keywords: VEGA

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Jeff G. Schneider. Exploiting model uncertainty estimates for safe dynamic control learning. In M. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems (NIPS 9), pp.  1047–1053. MIT Press, 1996.
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Oliver Schütze, X. Esquivel, A. Lara, and Carlos A. Coello Coello. Some Comments on GD and IGD and Relations to the Hausdorff Distance. In M. Pelikan and J. Branke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010, pp.  1971–1974. ACM Press, New York, NY, 2010.
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Oliver Schütze and Carlos Hernández. Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms. Springer, 2021.
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Marius Schneider and Holger H. Hoos. Quantifying Homogeneity of Instance Sets for Algorithm Configuration. In Y. Hamadi and M. Schoenauer, editors, Learning and Intelligent Optimization, 6th International Conference, LION 6, volume 7219 of Lecture Notes in Computer Science, pp.  190–204. Springer, Heidelberg, Germany, 2012.
[ bib | DOI ]

Keywords: Quantifying Homogeneity; Empirical Analysis; Parameter Optimization; Algorithm Configuration

[2517]

Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.  815–823, 2015.
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Michael Schmidt and Hod Lipson. Age-Fitness Pareto Optimization. In Genetic Programming Theory and Practice VIII. Genetic and Evolutionary Computation, pp.  129–146. Springer, 2011.
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Jens Schreiter, Duy Nguyen-Tuong, Mona Eberts, Bastian Bischoff, Heiner Markert, and Marc Toussaint. Safe Exploration for Active Learning with Gaussian Processes. In Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, volume 9286 of Lecture Notes in Computer Science, pp.  133–149. Springer, 2015.
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Proposed Safe Active Learning (SAL) algorithm

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Mark Schillinger, Benedikt Ortelt, Benjamin Hartmann, Jens Schreiter, Mona Meister, Duy Nguyen-Tuong, and Oliver Nelles. Safe active learning of a high pressure fuel supply system. In Proceedings of the 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016 and the 57th SIMS Conference on Simulation and Modelling SIMS 2016, pp.  286–292. Linköping University Electronic Press, 2018.
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Andrea Schaerf. Combining Local Search and Look-Ahead for Scheduling and Constraint Satisfaction Problems. In M. E. Pollack, editor, Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), volume 2, pp.  1254–1259. Morgan Kaufmann Publishers, 1997.
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Haitham Seada and Kalyanmoy Deb. U-NSGA-III: A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives: Proof-of-Principle Results. In A. Gaspar-Cunha, C. H. Antunes, and C. A. Coello Coello, editors, Evolutionary Multi-criterion Optimization, EMO 2015 Part I, volume 9018 of Lecture Notes in Computer Science, pp.  34–49. Springer, Heidelberg, Germany, 2015.
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Moritz Seiler, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. T. M. Emmerich, and H. Trautmann, editors, Parallel Problem Solving from Nature – PPSN XVI, volume 12269 of Lecture Notes in Computer Science, pp.  48–64. Springer, Cham, Switzerland, 2020.
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P. Serafini. Some Considerations About Computational Complexity for Multiobjective Combinatorial Problems. In J. Jahn and W. Krabs, editors, Recent Advances and Historical Development of Vector Optimization, volume 294 of Lecture Notes in Economics and Mathematical Systems, pp.  222–231. Springer, Berlin, Germany, 1986.
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Ke Shang, Hisao Ishibuchi, and Weiyu Chen. Greedy approximated hypervolume subset selection for many-objective optimization. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021, pp.  448–456. ACM Press, New York, NY, 2021.
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Ke Shang, Hisao Ishibuchi, and Yang Nan. Distance-based subset selection revisited. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021, pp.  439–447. ACM Press, New York, NY, 2021.
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Mudita Sharma, Alexandros Komninos, Manuel López-Ibáñez, and Dimitar Kazakov. Deep Reinforcement Learning-Based Parameter Control in Differential Evolution. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp.  709–717. ACM Press, New York, NY, 2019.
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Seyed Mahdi Shavarani, Manuel López-Ibáñez, Richard Allmendinger, and Joshua D. Knowles. An Interactive Decision Tree-Based Evolutionary Multi-Objective Algorithm: Supplementary material. In M. T. M. Emmerich et al., editors, Evolutionary Multi-criterion Optimization, EMO 2023, volume 13970 of Lecture Notes in Computer Science, pp.  620–634. Springer International Publishing, Cham, Switzerland, 2023.
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Seyed Mahdi Shavarani, Manuel López-Ibáñez, Richard Allmendinger, and Joshua D. Knowles. An Interactive Decision Tree-Based Evolutionary Multi-Objective Algorithm: Supplementary material. Zenodo, 2023.
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Mudita Sharma, Manuel López-Ibáñez, and Dimitar Kazakov. Performance Assessment of Recursive Probability Matching for Adaptive Operator Selection in Differential Evolution. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, and D. Whitley, editors, Parallel Problem Solving from Nature – PPSN XV, volume 11102 of Lecture Notes in Computer Science, pp.  321–333. Springer, Cham, Switzerland, 2018.
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Mudita Sharma, Manuel López-Ibáñez, and Dimitar Kazakov. Performance Assessment of Recursive Probability Matching for Adaptive Operator Selection in Differential Evolution: Supplementary material. https://github.com/mudita11/AOS-comparisons, 2018.
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Seyed Mahdi Shavarani, Manuel López-Ibáñez, and Joshua D. Knowles. Realistic Utility Functions Prove Difficult for State-of-the-Art Interactive Multiobjective Optimization Algorithms. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021, pp.  457–465. ACM Press, New York, NY, 2021.
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Selmar K. Smit and Agoston E. Eiben. Beating the 'world champion' evolutionary algorithm via REVAC tuning. In H. Ishibuchi et al., editors, Proceedings of the 2010 Congress on Evolutionary Computation (CEC 2010), pp.  1–8, Piscataway, NJ, 2010. IEEE Press.
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Selmar K. Smit and Agoston E. Eiben. Parameter Tuning of Evolutionary Algorithms: Generalist vs. Specialist. In C. D. Chio, S. Cagnoni, C. Cotta, M. Ebner, A. Ekárt, A. I. Esparcia-Alcázar, C. K. Goh, J.-J. Merelo, F. Neri, M. Preuss, J. Togelius, and G. N. Yannakakis, editors, Applications of Evolutionary Computation, volume 6024 of Lecture Notes in Computer Science, pp.  542–551. Springer, Heidelberg, Germany, 2010.
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Selmar K. Smit and Agoston E. Eiben. Multi-Problem Parameter Tuning using BONESA. In J.-K. Hao, P. Legrand, P. Collet, N. Monmarché, E. Lutton, and M. Schoenauer, editors, Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011, volume 7401 of Lecture Notes in Computer Science, pp.  222–233. Springer, Heidelberg, Germany, 2012.
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Jim Smith, Christopher Stone, and Martin Serpell. Exploiting Diverse Distance Metrics for Surrogate-Based Optimisation of Ordering Problems. In T. Friedrich, F. Neumann, and A. M. Sutton, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2016, pp.  701–708. ACM Press, New York, NY, 2016.
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Kate Smith-Miles, Jano I. van Hemert, and Xin Yu Lim. Understanding TSP difficulty by Learning from evolved instances. In C. Blum and R. Battiti, editors, Learning and Intelligent Optimization, 4th International Conference, LION 4, volume 6073 of Lecture Notes in Computer Science, pp.  266–280. Springer, Heidelberg, Germany, 2010.
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Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. Practical Bayesian Optimization of Machine Learning Algorithms. In P. L. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems (NIPS 25), pp.  2960–2968. Curran Associates, Red Hook, NY, 2012.
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Jasper Snoek, Kevin Swersky, Richard Zemel, and Ryan P. Adams. Input Warping for Bayesian Optimization of Non-Stationary Functions. In E. P. Xing and T. Jebara, editors, Proceedings of the 31st International Conference on Machine Learning, ICML 2014, volume 32, pp.  1674–1682. PMLR, 2014.
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Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions. The ability to accurately model distributions over functions is critical to the effectiveness of Bayesian optimization. Although Gaussian processes provide a flexible prior over functions, there are various classes of functions that remain difficult to model. One of the most frequently occurring of these is the class of non-stationary functions. The optimization of the hyperparameters of machine learning algorithms is a problem domain in which parameters are often manually transformed a priori, for example by optimizing in "log-space", to mitigate the effects of spatially-varying length scale. We develop a methodology for automatically learning a wide family of bijective transformations or warpings of the input space using the Beta cumulative distribution function. We further extend the warping framework to multi-task Bayesian optimization so that multiple tasks can be warped into a jointly stationary space. On a set of challenging benchmark optimization tasks, we observe that the inclusion of warping greatly improves on the state-of-the-art, producing better results faster and more reliably.

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Krzysztof Socha, Joshua D. Knowles, and M. Sampels. A Max-Min Ant System for the University Course Timetabling Problem. In M. Dorigo et al., editors, Ant Algorithms, Third International Workshop, ANTS 2002, volume 2463 of Lecture Notes in Computer Science, pp.  1–13. Springer, Heidelberg, Germany, 2002.
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Krzysztof Socha, M. Sampels, and M. Manfrin. Ant algorithms for the university course timetabling problem with regard to the state-of-the-art. In S. Cagnoni et al., editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2003, volume 2611 of Lecture Notes in Computer Science, pp.  334–345. Springer, Heidelberg, Germany, 2003.
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Krzysztof Socha. ACO for Continuous and Mixed-Variable Optimization. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of Lecture Notes in Computer Science, pp.  25–36. Springer, Heidelberg, Germany, 2004.
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Aldo Sotelo, Julio Basulado, Pedro Doldán, and Benjamín Barán. Algoritmos Evolutivos Multiobjetivo Combinados para la Optimización de la Programación de Bombeo en Sistemas de Suministro de Agua. In Congreso Internacional de Tecnologías y Aplicaciones Informáticas, JIT-CITA 2001, Asunción, Paraguay, 2001. (In Spanish).
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Aldo Sotelo, C. von Lücken, and Benjamín Barán. Multiobjective Evolutionary Algorithms in Pump Scheduling Optimisation. In B. H. V. Topping and Z. Bittnar, editors, Proceedings of the Third International Conference on Engineering Computational Technology. Civil-Comp Press, Stirling, Scotland, 2002.
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Operation of pumping stations represents high costs to water supply companies. Therefore, reducing such costs through an optimal pump scheduling becomes an important issue. This work presents the use of Multiobjective Evolutionary Algorithms (MOEAs) to solve an optimal pump-scheduling problem. For the first time, six different approaches were implemented and compared. These algorithms aim to minimise four objectives: electric energy cost, pumps' maintenance cost, maximum power peak, and level variation in the reservoir. In order to consider hydraulic and technical constrains, a heuristic constrain algorithm was developed and combined with each MOEA utilised. Evaluation of experimental results of a set of metrics shows that the Strength Pareto Evolutionary Algorithm (SPEA) achieves the best performance for this problem. Moreover, SPEA's set of solutions provide pumping station operation engineers with a wide range of optimal pump schedules to chose from.

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Marcelo De Souza, Marcus Ritt, and Manuel López-Ibáñez. CAPOPT: Capping Methods for the Automatic Configuration of Optimization Algorithms. https://github.com/souzamarcelo/capopt, 2020.
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Marcelo De Souza, Marcus Ritt, and Manuel López-Ibáñez. Capping Methods for the Automatic Configuration of Optimization Algorithms – Supplementary Material. https://github.com/souzamarcelo/supp-cor-capopt, 2021.
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Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright. Optimization for machine learning. MIT Press, Cambridge, MA, 2012.
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Fernando Stefanello, Vaneet Aggarwal, Luciana Salete Buriol, José Fernando Gonçalves, and Mauricio G. C. Resende. A Biased Random-key Genetic Algorithm for Placement of Virtual Machines Across Geo-Separated Data Centers. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  919–926. ACM Press, New York, NY, 2015.
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Keywords: irace

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Proposed difference between ad hoc and non-ad hoc interactive multi-objective optimization methods

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Christian Steinruecken, Emma Smith, David Janz, James Lloyd, and Zoubin Ghahramani. The Automatic Statistician. In F. Hutter, L. Kotthoff, and J. Vanschoren, editors, Automated Machine Learning, pp.  161–173. Springer, 2019.
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R. E. Steuer. Multiple Criteria Optimization: Theory, Computation and Application. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, New York, NY, 1986.
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Keywords: Maximally dispersed weights

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Daniel H. Stolfi and Enrique Alba. An Evolutionary Algorithm to Generate Real Urban Traffic Flows. In J. M. Puerta, J. A. Gámez, B. Dorronsoro, E. Barrenechea, A. Troncoso, B. Baruque, and M. Galar, editors, Advances in Artificial Intelligence, CAEPIA 2015, volume 9422 of Lecture Notes in Computer Science, pp.  332–343. Springer, Heidelberg, Germany, 2015.
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In this article we present a strategy based on an evolution- ary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being able to work with a traffic distribution close to reality. We have compared the results of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%.

Keywords: Evolutionary algorithm,SUMO,Smart city,Smart mobility,Traffic simulation

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Thomas Stützle. Applying Iterated Local Search to the Permutation Flow Shop Problem. Technical Report AIDA–98–04, FG Intellektik, FB Informatik, TU Darmstadt, Germany, August 1998.
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Thomas Stützle. ACOTSP: A Software Package of Various Ant Colony Optimization Algorithms Applied to the Symmetric Traveling Salesman Problem, 2002.
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Thomas Stützle. Some Thoughts on Engineering Stochastic Local Search Algorithms. In A. Viana et al., editors, Proceedings of the EU/MEeting 2009: Debating the future: new areas of application and innovative approaches, pp.  47–52, 2009.
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Thomas Stützle. Max-Min Ant System for the Quadratic Assignment Problem. Technical Report AIDA–97–4, FG Intellektik, FB Informatik, TU Darmstadt, Germany, July 1997.
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Thomas Stützle. An Ant Approach to the Flow Shop Problem. In Proceedings of the 6th European Congress on Intelligent Techniques & Soft Computing (EUFIT'98), volume 3, pp.  1560–1564. Verlag Mainz, Aachen, Germany, 1998.
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Thomas Stützle and Marco Dorigo. ACO Algorithms for the Quadratic Assignment Problem. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pp.  33–50. McGraw Hill, London, UK, 1999.
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Thomas Stützle and Susana Fernandes. New Benchmark Instances for the QAP and the Experimental Analysis of Algorithms. In J. Gottlieb and G. R. Raidl, editors, Proceedings of EvoCOP 2004 – 4th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 3004 of Lecture Notes in Computer Science, pp.  199–209. Springer, Heidelberg, Germany, 2004.
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The quadratic assignment problem arises in a variety of practical settings. It is known to be among the hardest combinatorial problems for exact algorithms. Therefore, a large number of heuristic approaches have been proposed for its solution. In this article we introduce a new, large set of QAP instances that is intended to allow the systematic study of the performance of metaheuristics in dependence of QAP instance characteristics. Additionally, we give computational results with several high performing algorithms known from literature and give exemplary results on the influence of instance characteristics on the performance of these algorithms.

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Thomas Stützle and Holger H. Hoos. Analysing the Run-time Behaviour of Iterated Local Search for the Travelling Salesman Problem. In P. Hansen and C. Ribeiro, editors, Essays and Surveys on Metaheuristics, Operations Research/Computer Science Interfaces Series, pp.  589–611. Kluwer Academic Publishers, Boston, MA, 2001.
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Thomas Stützle and Holger H. Hoos. Improving the Ant System: A Detailed Report on the Max-Min Ant System. Technical Report AIDA–96–12, FG Intellektik, FB Informatik, TU Darmstadt, Germany, August 1996.
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Thomas Stützle and Holger H. Hoos. The Max-Min Ant System and Local Search for the Traveling Salesman Problem. In T. Bäck, Z. Michalewicz, and X. Yao, editors, Proceedings of the 1997 IEEE International Conference on Evolutionary Computation (ICEC'97), pp.  309–314. IEEE Press, Piscataway, NJ, 1997.
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Thomas Stützle and Holger H. Hoos. Max-Min Ant System and Local Search for Combinatorial Optimization Problems. In S. Voß, S. Martello, I. H. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pp.  137–154. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1999.
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Thomas Stützle and Manuel López-Ibáñez. Automatic (Offline) Configuration of Algorithms. In J. L. Jiménez Laredo, S. Silva, and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2015, pp.  681–702. ACM Press, New York, NY, 2015.
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Thomas Stützle and Manuel López-Ibáñez. Automated Offline Design of Algorithms. In P. A. N. Bosman, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017, pp.  1038–1065. ACM Press, New York, NY, 2017.
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Thomas Stützle and Manuel López-Ibáñez. Automated Design of Metaheuristic Algorithms. In M. Gendreau and J.-Y. Potvin, editors, Handbook of Metaheuristics, volume 272 of International Series in Operations Research & Management Science, pp.  541–579. Springer, 2019.
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Keywords: automatic design, automatic configuration

[2599]

Thomas Stützle, Manuel López-Ibáñez, and Marco Dorigo. A Concise Overview of Applications of Ant Colony Optimization. In J. J. Cochran, editor, Wiley Encyclopedia of Operations Research and Management Science, volume 2, pp.  896–911. John Wiley & Sons, 2011.
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Thomas Stützle, Manuel López-Ibáñez, Paola Pellegrini, Michael Maur, Marco A. Montes de Oca, Mauro Birattari, and Marco Dorigo. Parameter Adaptation in Ant Colony Optimization. In Y. Hamadi, E. Monfroy, and F. Saubion, editors, Autonomous Search, pp.  191–215. Springer, Berlin, Germany, 2012.
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Thomas Stützle and Rubén Ruiz. Iterated Greedy. In R. Martí, P. M. Pardalos, and M. G. C. Resende, editors, Handbook of Heuristics, pp.  1–31. Springer International Publishing, 2018.
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Thomas Stützle and Rubén Ruiz. Iterated Local Search. In R. Martí, P. M. Pardalos, and M. G. C. Resende, editors, Handbook of Heuristics, pp.  1–27. Springer International Publishing, 2018.
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[2603]

Thomas Stützle. Local Search Algorithms for Combinatorial Problems — Analysis, Improvements, and New Applications. PhD thesis, FB Informatik, TU Darmstadt, Germany, 1998.
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James Styles and Holger H. Hoos. Ordered racing protocols for automatically configuring algorithms for scaling performance. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013, pp.  551–558. ACM Press, New York, NY, 2013.
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[2605]

James Styles, Holger H. Hoos, and Martin Müller. Automatically Configuring Algorithms for Scaling Performance. In Y. Hamadi and M. Schoenauer, editors, Learning and Intelligent Optimization, 6th International Conference, LION 6, volume 7219 of Lecture Notes in Computer Science, pp.  205–219. Springer, Heidelberg, Germany, 2012.
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Ponnuthurai N. Suganthan, Nikolaus Hansen, J. J. Liang, Kalyanmoy Deb, Y. P. Chen, Anne Auger, and S. Tiwari. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore, 2005.
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Also known as KanGAL Report Number 2005005 (Kanpur Genetic Algorithms Laboratory, IIT Kanpur)

Keywords: CEC'05 benchmark

[2607]

Yanan Sui, Alkis Gotovos, Joel W. Burdick, and Andreas Krause. Safe Exploration for Optimization with Gaussian Processes. In F. Bach and D. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, volume 37, pp.  997–1005. PMLR, 2015.
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We consider sequential decision problems under uncertainty, where we seek to optimize an unknown function from noisy samples. This requires balancing exploration (learning about the objective) and exploitation (localizing the maximum), a problem well-studied in the multi-armed bandit literature. In many applications, however, we require that the sampled function values exceed some prespecified "safety" threshold, a requirement that existing algorithms fail to meet. Examples include medical applications where patient comfort must be guaranteed, recommender systems aiming to avoid user dissatisfaction, and robotic control, where one seeks to avoid controls causing physical harm to the platform. We tackle this novel, yet rich, set of problems under the assumption that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop an efficient algorithm called SafeOpt, and theoretically guarantee its convergence to a natural notion of optimum reachable under safety constraints. We evaluate SafeOpt on synthetic data, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation.

Keywords: Safe Optimization, SafeOpt

[2608]

Yanan Sui, Vincent Zhuang, Joel W. Burdick, and Yisong Yue. Stagewise Safe Bayesian Optimization with Gaussian Processes. In J. G. Dy and A. Krause, editors, Proceedings of the 35th International Conference on Machine Learning, ICML 2018, volume 80 of Proceedings of Machine Learning Research, pp.  4788–4796. PMLR, 2018.
[ bib | epub ]

Keywords: StageOpt

[2609]

Zhaoxu Sun and Min Han. Multi-criteria Decision Making Based on PROMETHEE Method. In Proceedings of the 2010 International Conference on Computing, Control and Industrial Engineering, pp.  416–418, Los Alamitos, CA, 2010. IEEE Computer Society Press.
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Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 2nd edition, 2018.
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D. C. Sutton, D. S. Keane, and S. J. Sherriff. Optimizing the Real Time Operation of a Pumping Station at a Water Filtration Plant using Genetic Algorithms. Honors thesis, Department of Civil and Environmental Engineering, The University of Adelaide, 1998.
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Jerry Swan, Ender Özcan, and Graham Kendall. Hyperion: A Recursive Hyper-heuristic Framework. In C. A. Coello Coello, editor, Learning and Intelligent Optimization, 5th International Conference, LION 5, volume 6683 of Lecture Notes in Computer Science, pp.  616–630. Springer, Heidelberg, Germany, 2011.
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[2614]

Jerry Swan et al. A Research Agenda for Metaheuristic Standardization. In E.-G. Talbi, editor, Proceedings of MIC 2015, the 11th Metaheuristics International Conference, 2015.
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Keywords: uniform crossover

[2616]

Taeyoung Lee, Melvin Leok, and N. Harris McClamroch. A combinatorial optimal control problem for spacecraft formation reconfiguration. In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007.
[ bib | DOI ]

Keywords: bilevel

[2617]

Kiyoharu Tagawa, Hidehito Shimizu, and Hiroyuki Nakamura. Indicator-based Differential Evolution Using Exclusive Hypervolume Approximation and Parallelization for Multi-core Processors. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  657–664. ACM Press, New York, NY, 2011.
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Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  1701–1708, 2014.
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Ryoji Tanabe and Akira Oyama. Benchmarking MOEAs for multi-and many-objective optimization using an unbounded external archive. In P. A. N. Bosman, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pp.  633–640. ACM Press, New York, NY, 2017.
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M. Fatih Tasgetiren, Ozge Buyukdagli, Quan-Ke Pan, and Ponnuthurai N. Suganthan. A general variable neighborhood search algorithm for the no-idle permutation flowshop scheduling problem. In B. K. Panigrahi, P. N. Suganthan, S. Das, and S. S. Dash, editors, Swarm, Evolutionary, and Memetic Computing, volume 8298 of Theoretical Computer Science and General Issues, pp.  24–34. Springer International Publishing, 2013.
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Jorge Tavares and Francisco B. Pereira. Automatic Design of Ant Algorithms with Grammatical Evolution. In A. Moraglio, S. Silva, K. Krawiec, P. Machado, and C. Cotta, editors, Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012, volume 7244 of Lecture Notes in Computer Science, pp.  206–217. Springer, Heidelberg, Germany, 2012.
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[2622]

Cristina Teixeira, José Covas, Thomas Stützle, and António Gaspar-Cunha. Application of Pareto Local Search and Multi-Objective Ant Colony Algorithms to the Optimization of Co-Rotating Twin Screw Extruders. In A. Viana et al., editors, Proceedings of the EU/MEeting 2009: Debating the future: new areas of application and innovative approaches, pp.  115–120, 2009.
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Google. TensorFlow. https://www.tensorflow.org, 2017.
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K. T. K. Teo, W. Y. Kow, and Y. K. Chin. Optimization of traffic flow within an urban traffic light intersection with genetic algorithm. In Proceedings - 2nd International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2010, pp.  172–177. IEEE, IEEE Press, 2010.
[ bib ]

Keywords: Genetic algorithm,T-junction,Traffic control system,Traffic flows

[2625]

Hugo Terashima-Marín, Peter Ross, and Manuel Valenzuela-Rendón. Evolution of Constraint Satisfaction Strategies in Examination Timetabling. In W. Banzhaf, J. M. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. J. Jakiela, and R. E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 1999, pp.  635–642. Morgan Kaufmann Publishers, San Francisco, CA, 1999.
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Dirk Thierens. Adaptive strategies for operator allocation. In F. Lobo, C. F. Lima, and Z. Michalewicz, editors, Parameter Setting in Evolutionary Algorithms, pp.  77–90. Springer, Berlin, Germany, 2007.
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Dirk Thierens. Adaptive operator selection for iterated local search. In T. Stützle, M. Birattari, and H. H. Hoos, editors, Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2009, volume 5752 of Lecture Notes in Computer Science, pp.  140–144. Springer, Heidelberg, Germany, 2009.
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Dirk Thierens. Population-based Iterated Local Search: Restricting the Neighborhood Search by Crossover. In K. Deb et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, Part II, volume 3103 of Lecture Notes in Computer Science, pp.  234–245. Springer, Heidelberg, Germany, 2004.
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[2629]

Dirk Thierens. An Adaptive Pursuit Strategy for Allocating Operator Probabilities. In H.-G. Beyer and U.-M. O'Reilly, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2005, pp.  1539–1546. ACM Press, New York, NY, 2005.
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Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. In I. S. Dhillon, Y. Koren, R. Ghani, T. E. Senator, P. Bradley, R. Parekh, J. He, R. L. Grossman, and R. Uthurusamy, editors, The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp.  847–855. ACM Press, New York, NY, 2013.
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[2631]

Sebastian Thrun and Lorien Pratt. Learning to learn. Springer, 1998.
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[2632]

Renato Tinós, Darrell Whitley, and Gabriela Ochoa. Generalized Asymmetric Partition Crossover (GAPX) for the Asymmetric TSP. In C. Igel and D. V. Arnold, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014, pp.  501–508. ACM Press, New York, NY, 2014.
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Michal K Tomczyk and Milosz Kadziński. Robust Indicator-Based Algorithm for Interactive Evolutionary Multiple Objective Optimization. In M. López-Ibáñez, A. Auger, and T. Stützle, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp.  629–637. ACM Press, New York, NY, 2019.
[ bib | DOI ]

We propose a novel robust indicator-based algorithm, called IEMO/I, for interactive evolutionary multiple objective optimization. During the optimization run, IEMO/I selects at regular intervals a pair of solutions from the current population to be compared by the Decision Maker. The successively provided holistic judgements are employed to divide the population into fronts of potential optimality. These fronts are, in turn, used to bias the evolutionary search toward a subset of Pareto-optimal solutions being most relevant to the Decision Maker. To ensure a fine approximation of such a subset, IEMO/I employs a hypervolume metric within a steady-state indicator-based evolutionary framework. The extensive experimental evaluation involving a number of benchmark problems confirms that IEMO/I is able to construct solutions being highly preferred by the Decision Maker after a reasonable number of interactions. We also compare IEMO/I with some selected state-of-the-art interactive evolutionary hybrids incorporating preference information in form of pairwise comparisons, proving its competitiveness.

Keywords: preference learning, indicator-based algorithms, interactive algorithms, multiple objective optimization, pairwise comparisons, evolutionary algorithms

[2634]

Paolo Toth and Daniele Vigo. The vehicle routing problem. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2002.
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F. Toyama, K. Shoji, H. Mori, and J. Miyamichi. An Iterated Greedy Algorithm for the Binary Quadratic Programming Problem. In Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012, pp.  2183–2188. IEEE Press, 2012.
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[2636]

Risto Trajanov, Ana Nikolikj, Gjorgjina Cenikj, Fabien Teytaud, Mathurin Videau, Olivier Teytaud, Tome Eftimov, Manuel López-Ibáñez, and Carola Doerr. Improving Nevergrad's Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration. In G. Rudolph, A. V. Kononova, H. E. Aguirre, P. Kerschke, G. Ochoa, and T. Tušar, editors, Parallel Problem Solving from Nature – PPSN XVII, volume 13398 of Lecture Notes in Computer Science, pp.  18–31. Springer, Cham, Switzerland, 2022.
[ bib | DOI ]

Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc. State-of-the-art algorithm selection wizards are complex and difficult to improve. We propose in this work the use of automated configuration methods for improving their performance by finding better configurations of the algorithms that compose them. In particular, we use elitist iterated racing (irace) to find CMA configurations for specific artificial benchmarks that replace the hand-crafted CMA configurations currently used in the NGOpt wizard provided by the Nevergrad platform. We discuss in detail the setup of irace for the purpose of generating configurations that work well over the diverse set of problem instances within each benchmark. Our approach improves the performance of the NGOpt wizard, even on benchmark suites that were not part of the tuning by irace.

[2637]

Christoph Treude and Markus Wagner. Predicting Good Configurations for GitHub and Stack Overflow Topic Models. In Proceedings of the 16th International Conference on Mining Software Repositories, MSR '19, pp.  84–95, Piscataway, NJ, 2019. IEEE Press.
[ bib | DOI ]

Keywords: algorithm portfolio, corpus features, topic modelling

[2638]

Michael A. Trick. Graph Coloring Instances. https://mat.gsia.cmu.edu/COLOR/instances.html, 2018.
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[2639]

S. Tsutsui. An Enhanced Aggregation Pheromone System for Real-Parameter Optimization in the ACO Metaphor. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, volume 4150 of Lecture Notes in Computer Science, pp.  60–71. Springer, Heidelberg, Germany, 2006.
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[2640]

S. Tsutsui. cAS: Ant Colony Optimization with Cunning Ants. In T. P. Runarsson, H.-G. Beyer, E. K. Burke, J.-J. Merelo, D. Whitley, and X. Yao, editors, Parallel Problem Solving from Nature – PPSN IX, volume 4193 of Lecture Notes in Computer Science, pp.  162–171. Springer, Heidelberg, Germany, 2006.
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[2641]

Edward R. Tufte. The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT, 2nd edition, 2001.
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The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

Keywords: data visualization, information graphics, cognitive science

[2642]

Matteo Turchetta, Felix Berkenkamp, and Andreas Krause. Safe Exploration in Finite Markov Decision Processes with Gaussian Processes. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems (NIPS 29), pp.  4312–4320, 2016.
[ bib | DOI | epub ]

Keywords: SafeMDP

[2643]

Matteo Turchetta, Felix Berkenkamp, and Andreas Krause. Safe Exploration for Interactive Machine Learning. In H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. B. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems (NeurIPS 32), pp.  2887–2897, 2019.
[ bib | epub ]

Keywords: Reinforcement Learning; Markov Decision Process; SafeML

[2644]

The Turing Way Community, Becky Arnold, Louise Bowler, Sarah Gibson, Patricia Herterich, Rosie Higman, Anna Krystalli, Alexander Morley, Martin O'Reilly, and Kirstie Whitaker. The Turing Way: A Handbook for Reproducible Data Science. Zenodo, March 2019.
[ bib | DOI ]

Available from https://the-turing-way.netlify.app. This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1.

[2645]

Tea Tušar and Bogdan Filipič. Differential Evolution versus Genetic Algorithms in Multiobjective Optimization. In S. Obayashi et al., editors, Evolutionary Multi-criterion Optimization, EMO 2007, volume 4403 of Lecture Notes in Computer Science, pp.  257–271. Springer, Heidelberg, Germany, 2007.
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Tea Tušar and Bogdan Filipič. Visualizing 4D approximation sets of multiobjective optimizers with prosections. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  737–744. ACM Press, New York, NY, 2011.
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[2647]

Tea Tušar. Design of an Algorithm for Multiobjective Optimization with Differential Evolution. M.sc. thesis, Faculty of Computer and Information Science, University of Ljubljana, 2007.
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N. L. J. Ulder, Emile H. L. Aarts, H.-J. Bandelt, Peter J. M. van Laarhoven, and Erwin Pesch. Genetic Local Search Algorithms for the Travelling Salesman Problem. In H.-P. Schwefel and R. Männer, editors, Parallel Problem Solving from Nature – PPSN I, pp.  109–116. Springer, Berlin/Heidelberg, 1991.
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Tamara Ulrich, Johannes Bader, and Lothar Thiele. Defining and Optimizing Indicator-Based Diversity Measures in Multiobjective Search. In R. Schaefer, C. Cotta, J. Kolodziej, and G. Rudolph, editors, Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture Notes in Computer Science, pp.  707–717. Springer, Heidelberg, Germany, 2010.
[ bib | DOI ]

Two archive; two populations; decision space diversity

[2650]

Andrea Valsecchi, Jérémie Dubois-Lacoste, Thomas Stützle, Sergio Damas, José Santamaría, and Linda Marrakchi-Kacem. Evolutionary Medical Image Registration using Automatic Parameter Tuning. In Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013), pp.  1326–1333, Piscataway, NJ, 2013. IEEE Press.
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Mauro Vallati, Chris Fawcett, Alfonso E. Gerevini, Holger H. Hoos, and Alessandro Saetti. Generating Fast Domain-Optimized Planners by Automatically Configuring a Generic Parameterised Planner. In E. Karpas, S. Jiménez Celorrio, and S. Kambhampati, editors, Proceedings of ICAPS-PAL11, 2011.
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Peter J. M. van Laarhoven and Emile H. L. Aarts. Simulated Annealing: Theory and Applications, volume 37. Springer, 1987.
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Jan N. van Rijn and Frank Hutter. Hyperparameter Importance Across Datasets. In Y. Guo and F. Farooq, editors, 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.  2367–2376. ACM Press, New York, NY, July 2018.
[ bib | DOI ]

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.

Keywords: hyperparameter optimization, meta-learning, hyperparameter importance

[2654]

Elia Van Wolputte, Evgeniya Korneva, and Hendrik Blockeel. MERCS: multi-directional ensembles of regression and classification trees. In S. A. McIlraith and K. Q. Weinberger, editors, Proceedings of the AAAI Conference on Artificial Intelligence, pp.  4276–4283. AAAI Press, February 2018.
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Andrea Vedaldi and Brian Fulkerson. VLFeat: An open and portable library of computer vision algorithms. In Proceedings of the 18th ACM international conference on Multimedia, pp.  1469–1472. ACM, 2010.
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David A. Van Veldhuizen and Gary B. Lamont. Evolutionary Computation and Convergence to a Pareto Front. In J. R. Koza, editor, Late Breaking Papers at the Genetic Programming 1998 Conference, pp.  221–228, Stanford University, California, July 1998. Stanford University Bookstore.
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Keywords: generational distance

[2657]

David A. Van Veldhuizen. Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. PhD thesis, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, 1999.
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Diederick Vermetten, Fabio Caraffini, Bas van Stein, and Anna V. Kononova. Using Structural Bias to Analyse the Behaviour of Modular CMA-ES. In J. E. Fieldsend and M. Wagner, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2022, pp.  1674–1682. ACM Press, New York, NY, 2022.
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[2659]

Sébastien Verel, Arnaud Liefooghe, and Clarisse Dhaenens. Set-based Multiobjective Fitness Landscapes: A Preliminary Study. In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp.  769–776. ACM Press, New York, NY, 2011.
[ bib | DOI ]

[2660]

Diederick Vermetten, Hao Wang, Carola Doerr, and Thomas Bäck. Integrated vs. Sequential Approaches for Selecting and Tuning CMA-ES Variants. In C. A. Coello Coello, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2020. ACM Press, New York, NY, 2020.
[ bib | DOI | epub ]

[2661]

Diederick Vermetten, Hao Wang, Manuel López-Ibáñez, Carola Doerr, and Thomas Bäck. Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms. In J. E. Fieldsend and M. Wagner, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022, pp.  867–875. ACM Press, New York, NY, 2022.
[ bib | DOI ]

The stochastic nature of iterative optimization heuristics leads to inherently noisy performance measurements. Since these measurements are often gathered once and then used repeatedly, the number of collected samples will have a significant impact on the reliability of algorithm comparisons. We show that care should be taken when making decisions based on limited data. Particularly, we show that the number of runs used in many benchmarking studies, e.g., the default value of 15 suggested by the COCO environment, can be insufficient to reliably rank algorithms on well-known numerical optimization benchmarks.Additionally, methods for automated algorithm configuration are sensitive to insufficient sample sizes. This may result in the configurator choosing a "lucky" but poor-performing configuration despite exploring better ones. We show that relying on mean performance values, as many configurators do, can require a large number of runs to provide accurate comparisons between the considered configurations. Common statistical tests can greatly improve the situation in most cases but not always. We show examples of performance losses of more than 20%, even when using statistical races to dynamically adjust the number of runs, as done by irace. Our results underline the importance of appropriately considering the statistical distribution of performance values.

Keywords: parameter tuning, evolution strategies, algorithm configuration, performance measures

[2662]

Mathurin Videau, Alessandro Leite, Olivier Teytaud, and Marc Schoenauer. Multi-Objective Genetic Programming for Explainable Reinforcement Learning. In E. Medvet, G. Pappa, and B. Xue, editors, Proceedings of the 25th European Conference on Genetic Programming, EuroGP 2022, Lecture Notes in Computer Science, pp.  256–281. Springer Nature, Cham, Switzerland, 2022.
[ bib ]

Keywords: genetic algorithms, genetic programming: Poster

[2663]

Carlos Vieira, Leslie Pérez Cáceres, and Leonardo C. T. Bezerra. Evaluating Anytime Performance on NAS-Bench-101. In Proceedings of the 2021 Congress on Evolutionary Computation (CEC 2021), pp.  1249–1256, Piscataway, NJ, 2021. IEEE Press.
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[2664]

Alessia Violin. Mathematical Programming Approaches to Pricing Problems. PhD thesis, Faculté de Sciences, Université Libre de Bruxelles and Dipartimento di Ingegneria e Architettura, Università degli studi di Trieste, 2014.
[ bib ]

Supervised by Dr. Martine Labbé and Dr. Lorenzo Castelli

[2665]

Thomas Voß, Nikolaus Hansen, and Christian Igel. Improved Step Size Adaptation for the MO-CMA-ES. In M. Pelikan and J. Branke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010, pp.  487–494. ACM Press, New York, NY, 2010.
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Christos Voudouris and Edward P. K. Tsang. Guided Local Search. In F. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pp.  185–218. Kluwer Academic Publishers, Norwell, MA, 2002.
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D. A. Savic and G. A. Walters, editors. Water Industry Systems: Modelling and Optimization Applications, volume 2. Research Studies Press Ltd., Baldock, United Kingdom, 1999.
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Akifumi Wachi, Yanan Sui, Yisong Yue, and Masahiro Ono. Safe Exploration and Optimization of Constrained MDPs Using Gaussian Processes. In S. A. McIlraith and K. Q. Weinberger, editors, Proceedings of the AAAI Conference on Artificial Intelligence, pp.  6548–6556. AAAI Press, February 2018.
[ bib | DOI ]

We present a reinforcement learning approach to explore and optimize a safety-constrained Markov Decision Process(MDP). In this setting, the agent must maximize discounted cumulative reward while constraining the probability of entering unsafe states, defined using a safety function being within some tolerance. The safety values of all states are not known a priori, and we probabilistically model them via a Gaussian Process (GP) prior. As such, properly behaving in such an environment requires balancing a three-way trade-off of exploring the safety function, exploring the reward function, and exploiting acquired knowledge to maximize reward. We propose a novel approach to balance this trade-off. Specifically, our approach explores unvisited states selectively; that is, it prioritizes the exploration of a state if visiting that state significantly improves the knowledge on the achievable cumulative reward. Our approach relies on a novel information gain criterion based on Gaussian Process representations of the reward and safety functions. We demonstrate the effectiveness of our approach on a range of experiments, including a simulation using the real Martian terrain data.

Keywords: Markov Decision Process, Gaussian Processes

[2669]

Tobias Wagner, Nicola Beume, and Boris Naujoks. Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization. In S. Obayashi et al., editors, Evolutionary Multi-criterion Optimization, EMO 2007, volume 4403 of Lecture Notes in Computer Science, pp.  742–756. Springer, Heidelberg, Germany, 2007.
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Markus Wagner, Tobias Friedrich, and Marius Thomas Lindauer. Improving local search in a minimum vertex cover solver for classes of networks. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017), pp.  1704–1711, Piscataway, NJ, 2017. IEEE Press.
[ bib | DOI ]

Keywords: graph theory;search problems;local search;minimum vertex cover solver;network classes;straightforward alternative approach;benchmark sets;graphs;algorithm portfolio;single integrated approach;Training;Portfolios;Algorithm design and analysis;Prediction algorithms;Machine learning algorithms;Optimization;Benchmark testing,smac,paramils

[2671]

Markus Wagner and Frank Neumann. A Fast Approximation-guided Evolutionary Multi-objective Algorithm. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  687–694. ACM Press, New York, NY, 2015.
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Benjamin W. Wah and Yi Xin Chen. Optimal Anytime Constrained Simulated Annealing for Constrained Global Optimization. In R. Dechter, editor, Principles and Practice of Constraint Programming, CP 2000, volume 1894 of Lecture Notes in Computer Science, pp.  425–440. Springer, Heidelberg, Germany, 2000.
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J. P. Walser. Solving Linear Pseudo-Boolean Constraint Problems with Local Search. In B. Kuipers and B. L. Webber, editors, Proceedings of AAAI 1997 – Fourteenth National Conference on Artificial Intelligence, pp.  269–274. AAAI Press/MIT Press, Menlo Park, CA, 1997.
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J. P. Walser. Integer Optimization by Local Search: A Domain-Independent Approach, volume 1637 of Lecture Notes in Computer Science. Springer, Heidelberg, Germany, 1999.
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J. P. Walser, R. Iyer, and N. Venkatasubramanyan. An Integer Local Search Method with Application to Capacitated Production Planning. In J. Mostow and C. Rich, editors, Proceedings of AAAI 1998 – Fifteenth National Conference on Artificial Intelligence, pp.  373–379. AAAI Press/MIT Press, Menlo Park, CA, 1998.
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Toby Walsh. Depth-bounded Discrepancy Search. In M. E. Pollack, editor, Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), pp.  1388–1395. Morgan Kaufmann Publishers, 1997.
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Handing Wang, John Doherty, and Yaochu Jin. Hierarchical surrogate-assisted evolutionary multi-scenario airfoil shape optimization. In Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018), pp.  1–8, Piscataway, NJ, 2018. IEEE Press.
[ bib ]

Keywords: scenario-based

[2678]

Yanqi Wang, Xingye Dong, Ping Chen, and Youfang Lin. Iterated local search algorithms for the sequence-dependent setup times flow shop scheduling problem minimizing makespan. In Foundations of Intelligent Systems, pp.  329–338. Springer, 2014.
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Shaolin Wang, Yi Mei, and Mengjie Zhang. Two-stage multi-objective genetic programming with archive for uncertain capacitated arc routing problem. In F. Chicano and K. Krawiec, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2021, pp.  287–295. ACM Press, New York, NY, 2021.
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[2680]

Matthew O. Ward. Multivariate data glyphs: Principles and practice. In C.-h. Chen, W. K. Härdle, and A. Unwin, editors, Handbook of Data Visualization, pp.  179–198. Springer, 2008.
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Tony Wauters. 10 years of Eternity II–from $2 million puzzle to challenging optimization problem. In International Workshop on Cutting, Packing and Related Topics, Gent, Belgium, 2017.
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The Eternity II (EII) puzzle is a commercial edge matching puzzle in which 256 square tiles with four coloured edges must be arranged on a 16 by 16 grid such that all tile edges are matched. In addition, a complete solution requires that the `grey' patterns, which appear only on a subset of the tiles, should be matched to the outer edges of the grid. The puzzle belongs to the more general class of Edge Matching Puzzles, which have been shown to be NP-complete. In July 2007, toy distributor Tomy UK Ltd. released this challenging edge matching puzzle with a $2 million prize. However, to the best of our knowledge, no complete solution has ever been found. Meanwhile, the final scrutiny date for the cash price, 31 December 2010, has passed, leaving the large money prize unclaimed. In its 10 years of existence many people tried to solve EII and some are still trying. Many approaches to Edge Matching Puzzles are reported in the literature. Among these approaches are constraint programming and backtracking, metaheuristics, and evolutionary methods. Other approaches translate the problem into SAT, MILP or max-clique and then solve it with appropriate state of the art solvers. Some approaches have also been implemented on parallel computing or dedicated hardware.

[2682]

Ingo Wegener. Simulated annealing beats metropolis in combinatorial optimization. In L. Caires, G. F. Italiano, L. Monteiro, C. Palamidessi, and M. Yung, editors, Proceedings of the 32nd International Colloquium on Automata, Languages and Programming, ICALP 2005, volume 3580 of Lecture Notes in Computer Science, pp.  589–601. Springer, Heidelberg, Germany, 2005.
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Chad Wegley, Muzaffar Eusuff, and Kevin E. Lansey. Determining Pump Operations Using Particle Swarm Optimization. In R. H. Hotchkiss and M. Glade, editors, Building Partnerships, Proceedings of the Joint Conference on Water Resources Engineering and Water Resources Planning and Management, Minneapolis, USA, 2000.
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Peter Wegner. Research paradigms in computer science. In ICSE'76: Proceedings of the 2nd international conference on Software engineering, pp.  322–330, October 1976.
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Kilian Q. Weinberger and Lawrence K. Saul. An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding. In A. Cohn, editor, Proceedings of the 21st National Conference on Artificial Intelligence, volume 6, pp.  1683–1686. AAAI Press/MIT Press, Menlo Park, CA, 2006.
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Kilian Q. Weinberger, Fei Sha, and Lawrence K. Saul. Learning a kernel matrix for nonlinear dimensionality reduction. In C. E. Brodley, editor, Proceedings of the 21st International Conference on Machine Learning, ICML 2004, New York, NY, 2004. ACM Press.
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We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear feature space, we show how to discover a mapping that "unfolds" the underlying manifold from which the data was sampled. The kernel matrix is constructed by maximizing the variance in feature space subject to local constraints that preserve the angles and distances between nearest neighbors. The main optimization involves an instance of semidefinite programming—a fundamentally different computation than previous algorithms for manifold learning, such as Isomap and locally linear embedding. The optimized kernels perform better than polynomial and Gaussian kernels for problems in manifold learning, but worse for problems in large margin classification. We explain these results in terms of the geometric properties of different kernels and comment on various interpretations of other manifold learning algorithms as kernel methods.

[2687]

Simon Wessing, Nicola Beume, Günther Rudolph, and Boris Naujoks. Parameter Tuning Boosts Performance of Variation Operators in Multiobjective Optimization. In R. Schaefer, C. Cotta, J. Kolodziej, and G. Rudolph, editors, Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture Notes in Computer Science, pp.  728–737. Springer, Heidelberg, Germany, 2010.
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[2688]

Clint R. Whaley. ATLAS: Automatically Tuned Linear Algebra Software. In D. Padua, editor, Encyclopedia of Parallel Computing, pp.  95–101. Springer, US, 2011.
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L. While and L. Bradstreet. Applying the WFG Algorithm to Calculate Incremental Hypervolumes. In Proceedings of the 2012 Congress on Evolutionary Computation (CEC 2012), pp.  1–8, Piscataway, NJ, 2012. IEEE Press.
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T. White, B. Pagurek, and F. Oppacher. Connection Management Using Adaptive Mobile Agents. In H. R. Arabnia, editor, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'98), pp.  802–809. CSREA Press, 1998.
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W. Wiesemann and Thomas Stützle. Iterated Ants: An Experimental Study for the Quadratic Assignment Problem. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, volume 4150 of Lecture Notes in Computer Science, pp.  179–190. Springer, Heidelberg, Germany, 2006.
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Angelika Wiegele. Biq Mac Library – A collection of Max-Cut and quadratic 0-1 programming instances of medium size. Technical report, Institut für Mathematik, Alpen-Adria-Universität Klagenfurt, 2007.
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Angelika Wiegele. Biq Mac Library – Binary Quadratic and Max Cut Library. http://biqmac.aau.at/biqmaclib.html, 2007.
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Andrzej P. Wierzbicki. The Use of Reference Objectives in Multiobjective Optimisation. In G. Fandel and T. Gal, editors, Multiple Criteria Decision Making Theory and Application, number 177 in Lecture Notes in Economics and Mathematical Systems, pp.  468–486. Springer, Heidelberg, Germany, 1980.
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David P. Williamson and David B. Shmoys. The design of approximation algorithms. Cambridge University Press, 2011.
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Steffen Wolf and Peter Merz. Iterated Local Search for Minimum Power Symmetric Connectivity in Wireless Networks. In C. Cotta and P. Cowling, editors, Proceedings of EvoCOP 2009 – 9th European Conference on Evolutionary Computation in Combinatorial Optimization, volume 5482 of Lecture Notes in Computer Science, pp.  192–203. Springer, Heidelberg, Germany, 2009.
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Lin Xu, Holger H. Hoos, and Kevin Leyton-Brown. Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection. In M. Fox and D. Poole, editors, Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, 2010.
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Keywords: automated algorithm design; portfolio-based algorithm selection; automated algorithm configuration; SAT; stochastic local search

[2698]

Lin Xu, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Hydra-MIP: Automated Algorithm Configuration and Selection for Mixed Integer Programming. Technical Report TR-2011-01, Department of Computer Science, University of British Columbia, Canada, 2011.
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Lin Xu, A. R. KhudaBukhsh, Holger H. Hoos, and Kevin Leyton-Brown. Quantifying the similarity of algorithm configurations. In P. Festa, M. Sellmann, and J. Vanschoren, editors, Learning and Intelligent Optimization, 10th International Conference, LION 10, volume 10079 of Lecture Notes in Computer Science, pp.  203–217. Springer, Cham, Switzerland, 2016.
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Jian-Wu Xu, Puskal P. Pokharel, António R. C. Paiva, and José C. Príncipe. Nonlinear Component Analysis Based on Correntropy. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, pp.  1851–1855. IEEE, 2006.
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Anil Yaman, Ahmed Hallawa, Matt Coler, and Giovanni Iacca. Presenting the ECO: evolutionary computation ontology. In G. Squillero and K. Sim, editors, Applications of Evolutionary Computation, volume 10199 of Lecture Notes in Computer Science, pp.  603–619. Springer, Heidelberg, Germany, 2017.
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[2702]

Xin Yao. Evolutionary Computation: Theory and Applications. World Scientific Singapore, River Edge, NJ, 1999.
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Keywords: Evolutionary programming (Computer science); Neural networks (Computer science); Evolutionary computation

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A. Yarimcam, S. Asta, Ender Özcan, and Andrew J. Parkes. Heuristic Generation via Parameter Tuning for Online Bin Packing. In P. Angelov et al., editors, Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on, pp.  102–108. IEEE, 2014.
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Keywords: irace

[2704]

Carlos Yasojima, Tiago Araújo, Bianchi Meiguins, Nelson Neto, and Jefferson Morais. A Comparison of Genetic Algorithms and Particle Swarm Optimization to Estimate Cluster-Based Kriging Parameters. In P. Moura Oliveira, P. Novais, and L. P. Reis, editors, Progress in Artificial Intelligence, pp.  750–761. Springer International Publishing, Cham, Switzerland, 2019.
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Kriging is one of the most used spatial estimation methods in real-world applications. Some kriging parameters must be estimated in order to reach a good accuracy in the interpolation process, however, this task remains a challenge. Various optimization methods have been tested to find good parameters of the kriging process. In recent years, many authors are using bio-inspired techniques and achieving good results in estimating these parameters in comparison with traditional techniques. This paper presents a comparison between well known bio-inspired techniques such as Genetic Algorithms and Particle Swarm Optimization in the estimation of the essential kriging parameters: nugget, sill, range, angle, and factor. In order to perform the tests, we proposed a methodology based on the cluster-based kriging method. Considering the Friedman test, the results showed no statistical difference between the evaluated algorithms in optimizing kriging parameters. On the other hand, the Particle Swarm Optimization approach presented a faster convergence, which is important in this high-cost computational problem.

[2705]

Gürcan Yavuz, Doǧan Aydın, and Thomas Stützle. Self-adaptive Search Equation-based Artificial Bee Colony Algorithm on the CEC 2014 Benchmark Functions. In Proceedings of the 2016 Congress on Evolutionary Computation (CEC 2016), pp.  1173–1180, Piscataway, NJ, 2016. IEEE Press.
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Cliff Young, David S. Johnson, David R. Karger, and Michael D. Smith. Near-optimal Intraprocedural Branch Alignment. In M. C. Chen, R. K. Cytron, and A. M. Berman, editors, Proceedings of the ACM SIGPLAN'97 Conference on Programming Language Design and Implementation (PLDI), Las Vegas, Nevada, pp.  183–193. ACM Press, 1997.
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Philip L. H. Yu, Wai Ming Wan, and Paul H. Lee. Decision Tree Modeling for Ranking Data. In J. Fürnkranz and E. Hüllermeier, editors, Preference Learning, pp.  83–106. Springer, Heidelberg, Germany, 2011.
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[2708]

Zhi Yuan, Armin Fügenschuh, Henning Homfeld, Prasanna Balaprakash, Thomas Stützle, and Michael Schoch. Iterated Greedy Algorithms for a Real-World Cyclic Train Scheduling Problem. In M. J. Blesa, C. Blum, C. Cotta, A. J. Fernández, J. E. Gallardo, A. Roli, and M. Sampels, editors, Hybrid Metaheuristics, volume 5296 of Lecture Notes in Computer Science, pp.  102–116. Springer, Heidelberg, Germany, 2008.
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Bo Yuan and Marcus Gallagher. Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms. In X. Yao et al., editors, Parallel Problem Solving from Nature – PPSN VIII, volume 3242 of Lecture Notes in Computer Science, pp.  172–181. Springer, Heidelberg, Germany, 2004.
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[2710]

Bo Yuan and Marcus Gallagher. Combining Meta-EAs and racing for difficult EA parameter tuning tasks. In F. Lobo, C. F. Lima, and Z. Michalewicz, editors, Parameter Setting in Evolutionary Algorithms, pp.  121–142. Springer, Berlin, Germany, 2007.
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Zhi Yuan, Marco A. Montes de Oca, Thomas Stützle, Hoong Chuin Lau, and Mauro Birattari. An Analysis of Post-selection in Automatic Configuration. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013, pp.  1557–1564. ACM Press, New York, NY, 2013.
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Lin Yuefeng, Wenli Du, and Thomas Stützle. Three L-SHADE Based Algorithms on Mixed-variables Optimization Problems. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017), pp.  2274–2281, Piscataway, NJ, 2017. IEEE Press.
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Joseph Yuen, Sophia Gao, Markus Wagner, and Frank Neumann. An adaptive data structure for evolutionary multi-objective algorithms with unbounded archives. In Proceedings of the 2012 Congress on Evolutionary Computation (CEC 2012), pp.  1–8, Piscataway, NJ, 2012. IEEE Press.
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Xi Yun and Susan L. Epstein. Learning Algorithm Portfolios for Parallel Execution. In Y. Hamadi and M. Schoenauer, editors, Learning and Intelligent Optimization, 6th International Conference, LION 6, volume 7219 of Lecture Notes in Computer Science, pp.  323–338. Springer, Heidelberg, Germany, 2012.
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[2715]

Martin Zaefferer, J. Stork, and Thomas Bartz-Beielstein. Distance Measures for Permutations in Combinatorial Efficient Global Optimization. In T. Bartz-Beielstein, J. Branke, B. Filipič, and J. Smith, editors, Parallel Problem Solving from Nature – PPSN XIII, volume 8672 of Lecture Notes in Computer Science, pp.  373–383. Springer, Heidelberg, Germany, 2014.
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Keywords: CEGO, Bayesian optimization

[2716]

Martin Zaefferer, J. Stork, M. Friese, Andreas Fischbach, Boris Naujoks, and Thomas Bartz-Beielstein. Efficient Global Optimization for Combinatorial Problems. In C. Igel and D. V. Arnold, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014, pp.  871–878. ACM Press, New York, NY, 2014.
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Proposed CEGO algorithm

Keywords: CEGO, Bayesian optimization

[2717]

Emmanuel Zarpas. Benchmarking SAT solvers for bounded model checking. In F. Bacchus and T. Walsh, editors, International Conference on Theory and Applications of Satisfiability Testing, volume 3569, pp.  340–354, 2005.
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Tiantian Zhang, Michael Georgiopoulos, and Georgios C. Anagnostopoulos. S-Race: A Multi-Objective Racing Algorithm. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013, pp.  1565–1572. ACM Press, New York, NY, 2013.
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Tiantian Zhang, Michael Georgiopoulos, and Georgios C. Anagnostopoulos. SPRINT: Multi-Objective Model Racing. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  1383–1390. ACM Press, New York, NY, 2015.
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Extended version published as [1411]

Keywords: model selection, multi-objective optimization, racing algorithm, sequential probability ratio test

[2720]

Qingfu Zhang, Wudong Liu, and Hui Li. The Performance of a New Version of MOEA/D on CEC09 Unconstrained MOP Test Instances. In Proceedings of the 2009 Congress on Evolutionary Computation (CEC 2009), pp.  203–208, Piscataway, NJ, 2009. IEEE Press.
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Qingfu Zhang, A. Zhou, S. Zhao, Ponnuthurai N. Suganthan, W. Liu, and S. Tiwari. Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition. Working Report CES-487, School of Computer Science and Electronic Engieering, University of Essex, April 2009.
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Proposed UF benchmark

[2722]

Qingfu Zhang and Ponnuthurai N. Suganthan. Special Session on Performance Assessment of Multiobjective Optimization Algorithms/CEC'09 MOEA Competition. https://www3.ntu.edu.sg/home/epnsugan/index_files/CEC09-MOEA/CEC09-MOEA.htm, 2009.
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Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. Mutation operators based on variable grouping for multi-objective large-scale optimization. In X. Chen and A. Stafylopatis, editors, Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, pp.  1–8, 2016.
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[2724]

Eckart Zitzler, Dimo Brockhoff, and Lothar Thiele. The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration. In S. Obayashi et al., editors, Evolutionary Multi-criterion Optimization, EMO 2007, volume 4403 of Lecture Notes in Computer Science, pp.  862–876. Springer, Heidelberg, Germany, 2007.
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Eckart Zitzler, Joshua D. Knowles, and Lothar Thiele. Quality Assessment of Pareto Set Approximations. In J. Branke, K. Deb, K. Miettinen, and R. Slowiński, editors, Multiobjective Optimization: Interactive and Evolutionary Approaches, volume 5252 of Lecture Notes in Computer Science, pp.  373–404. Springer, Heidelberg, Germany, 2008.
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Eckart Zitzler and Simon Künzli. Indicator-based Selection in Multiobjective Search. In X. Yao et al., editors, Parallel Problem Solving from Nature – PPSN VIII, volume 3242 of Lecture Notes in Computer Science, pp.  832–842. Springer, Heidelberg, Germany, 2004.
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Keywords: IBEA

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Eckart Zitzler, Marco Laumanns, and Lothar Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland, 2001.
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Eckart Zitzler, Marco Laumanns, and Lothar Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In K. C. Giannakoglou, D. T. Tsahalis, J. Periaux, K. D. Papaliliou, and T. Fogarty, editors, Evolutionary Methods for Design, Optimisation and Control, pp.  95–100. CIMNE, Barcelona, Spain, 2002.
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Eckart Zitzler and Lothar Thiele. Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In A. E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature – PPSN V, volume 1498 of Lecture Notes in Computer Science, pp.  292–301. Springer, Heidelberg, Germany, 1998.
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[2730]

Eckart Zitzler, Lothar Thiele, and Johannes Bader. SPAM: Set Preference Algorithm for Multiobjective Optimization. In G. Rudolph et al., editors, Parallel Problem Solving from Nature – PPSN X, volume 5199 of Lecture Notes in Computer Science, pp.  847–858. Springer, Heidelberg, Germany, 2008.
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Santosh Tiwari, Patrick Koch, Georges Fadel, and Kalyanmoy Deb. AMGA: An archive-based micro genetic algorithm for multi-objective optimization. In C. Ryan, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008, pp.  729–736. ACM Press, New York, NY, 2008.
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Eckart Zitzler. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, ETH Zürich, Switzerland, 1999.
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Andrejs Zujevs and Janis Eiduks. New decision maker model for multiobjective optimization interactive methods. In 17th International Conference on Information and Software Technologies, Kaunas, Lithuania, pp.  51–58, 2011.
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F. E. B. Otero, A. A. Freitas, and C. G. Johnson. cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes. In M. Dorigo et al., editors, Ant Colony Optimization and Swarm Intelligence, 6th International Conference, ANTS 2008, volume 5217 of Lecture Notes in Computer Science, pp.  48–59. Springer, Heidelberg, Germany, 2008.
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Axel de Perthuis de Laillevault, Benjamin Doerr, and Carola Doerr. Money for Nothing: Speeding Up Evolutionary Algorithms Through Better Initialization. In S. Silva and A. I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp.  815–822. ACM Press, New York, NY, 2015.
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Jeremy Rapin and Olivier Teytaud. Nevergrad: A gradient-free optimization platform. https://github.com/FacebookResearch/Nevergrad, 2018.
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Scott Robert Ladd. ACOVEA (Analysis of Compiler Options via Evolutionary Algorithm). https://github.com/Acovea/libacovea, 2000.
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Carlos Ansótegui, Meinolf Sellmann, and Kevin Tierney. GGA: Gender-based Genetic Algorithm Configurator. https://bitbucket.org/gga_ac/, 2017. Version visited last on July 2017.
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Qingfu Zhang. MOEA/D homepage. https://sites.google.com/view/moead/, 2007.
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Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, and Thomas Stützle. ParamILS. http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/, 2017. Version visited last on July 2017.
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Jakobus E. van Zyl. A Methodology for Improved Operational Optimization of Water Distribution Systems. PhD thesis, School of Engineering and Computer Science, University of Exeter, UK, 2001.
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H. E. Shrobe, T. M. Mitchell, and R. G. Smith, editors. Proceedings of the 7th National Conference on Artificial Intelligence, St. Paul, MN, August 21-26, AAAI-88. AAAI Press/MIT Press, Menlo Park, CA, 1988.
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J. Hoffmann and B. Selman, editors. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2012, Toronto, Ontario, Canada, July 22-26, 2012. AAAI Press, 2012.
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S. P. Singh and S. Markovitch, editors. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA. AAAI Press, February 2017.
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