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You only need to fork (or link) the git repository in your papers and sync with the main copy to send/receive updates.
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@article{AbdGad2012dynamic, author = {Abdelkhalik, Ossama and Gad, Ahmed}, title = {Dynamic-Size Multiple Populations Genetic Algorithm for Multigravity-Assist Trajectory Optimization}, journal = {Journal of Guidance, Control, and Dynamics}, year = 2012, volume = 35, number = 2, pages = {520--529}, doi = {10.2514/1.54330} }
@article{AbrAmoDan1999, title = {Simulated annealing cooling schedules for the school timetabling problem}, author = { David Abramson and Amoorthy, Mohan Krishna and Dang, Henry}, journal = {Asia-Pacific Journal of Operational Research}, volume = 16, number = 1, pages = {1--22}, year = 1999 }
@article{Abramson1991, title = {Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms}, author = { David Abramson }, journal = {Management Science}, volume = 37, number = 1, pages = {98--113}, year = 1991, publisher = {{INFORMS}} }
@article{Ach2009mpc, author = {Tobias Achterberg}, title = {{SCIP}: {Solving} constraint integer programs}, journal = {Mathematical Programming Computation}, year = 2009, volume = 1, number = 1, month = jul, pages = {1--41}, epub = {http://mpc.zib.de/archive/2009/1/Achterberg2009_Article_SCIPSolvingConstraintIntegerPr.pdf} }
@article{AchBer2007, title = {Improving the feasibility pump}, author = {Achterberg, Tobias and Berthold, Timo}, journal = {Discrete Optimization}, volume = 4, number = 1, pages = {77--86}, year = 2007, publisher = {Elsevier} }
@article{AcoMes2014jbi, author = {H{\'e}ctor-Gabriel Acosta-Mesa and Fernando Rechy-Ram{\'i}rez and Efr{\'e}n Mezura-Montes and Nicandro Cruz-Ram{\'i}rez and Hern{\'a}ndez Jim{\'e}nez, Rodolfo}, title = {Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions}, journal = {Journal of Biomedical Informatics}, volume = 49, pages = {73--83}, year = 2014, doi = {10.1016/j.jbi.2014.03.004}, keywords = {irace} }
@article{AddLocSch2008, title = {Disk Packing in a Square: A New Global Optimization Approach}, author = {Addis, Bernardetta and Locatelli, Marco and Schoen, Fabio}, journal = {INFORMS Journal on Computing}, year = 2008, number = 4, pages = {516--524}, volume = 20, doi = {10.1287/ijoc.1080.0263} }
@article{Ade92, author = {B. Adenso-D{\'i}az}, title = {Restricted Neighborhood in the Tabu Search for the Flowshop Problem}, journal = {European Journal of Operational Research}, year = 1992, volume = 62, number = 1, pages = {27--37} }
@article{AdeLag06tuning, author = {B. Adenso-D{\'i}az and Manuel Laguna }, title = {Fine-Tuning of Algorithms Using Fractional Experimental Design and Local Search}, journal = {Operations Research}, year = 2006, volume = 54, number = 1, pages = {99--114}, keywords = {Calibra} }
@article{AdrBieSha2022jair, title = {Automated dynamic algorithm configuration}, author = { Steven Adriaensen and Biedenkapp, Andr{\'e} and Shala, Gresa and Awad, Noor and Eimer, Theresa and Marius Thomas Lindauer and Frank Hutter }, journal = {Journal of Artificial Intelligence Research}, volume = 75, pages = {1633--1699}, year = 2022, doi = {10.1613/jair.1.13922} }
@article{AfsMieRui2021survey, author = {Afsar, Bekir and Kaisa Miettinen and Francisco Ruiz }, title = {Assessing the Performance of Interactive Multiobjective Optimization Methods: A Survey}, year = 2021, volume = 54, number = 4, doi = {10.1145/3448301}, abstract = {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.}, journal = {{ACM} Computing Surveys}, numpages = 27, keywords = {decision-makers, Interactive methods, performance assessment, preference information, multiobjective optimization problems} }
@article{AfsSilMis2022design, author = {Afsar, Bekir and Silvennoinen, Johanna and Misitano, Giovanni and Francisco Ruiz and Ruiz, Ana B. and Kaisa Miettinen }, title = {Designing empirical experiments to compare interactive multiobjective optimization methods}, journal = {Journal of the Operational Research Society}, year = 2022, volume = 74, number = 11, pages = {2327--2338}, month = nov, doi = {10.1080/01605682.2022.2141145} }
@article{AgoPea1973normality, doi = {10.2307/2335012}, year = 1973, month = dec, publisher = {{JSTOR}}, volume = 60, number = 3, pages = {613--622}, author = {Ralph {D'Agostino} and E. S. Pearson}, title = {Tests for Departure from Normality. Empirical Results for the Distributions of $b_2$ and $\surd b_1$}, journal = {Biometrika} }
@article{Agrell1997ejor, title = {On redundancy in multi criteria decision making}, journal = {European Journal of Operational Research}, volume = 98, number = 3, pages = {571--586}, year = 1997, doi = {10.1016/0377-2217(95)00340-1}, author = {Per J. Agrell}, keywords = {Multi criteria decision making, Redundancy, objective reduction, Vector optimisation}, abstract = {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.} }
@article{AguTan2007ejor, title = {Working principles, behavior, and performance of {MOEAs} on {MNK}-landscapes}, author = { Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi }, journal = {European Journal of Operational Research}, volume = 181, number = 3, year = 2007, pages = {1670--1690}, doi = {10.1016/j.ejor.2006.08.004} }
@article{AhmOsm2004:aor, author = {Samad Ahmadi and Ibrahim H. Osman }, title = {Density Based Problem Space Search for the Capacitated Clustering $p$-Median Problem}, journal = {Annals of Operations Research}, year = 2004, volume = 131, pages = {21--43} }
@article{AhrElsSarEss2021weighted, title = {Weighted pointwise prediction method for dynamic multiobjective optimization}, journal = {Information Sciences}, volume = 546, pages = {349--367}, year = 2021, author = {Ali Ahrari and Saber Elsayed and Ruhul Sarker and Daryl Essam and Carlos A. {Coello Coello} } }
@article{AhuErgOrlPun2002:dam, author = { R. K. Ahuja and O. Ergun and A. P. Punnen}, title = {A Survey of Very Large-scale Neighborhood Search Techniques}, journal = {Discrete Applied Mathematics}, year = 2002, volume = 123, number = {1--3}, pages = {75--102} }
@article{AinKumCha2009asc, author = { Sandip Aine and Rajeev Kumar and P. P. Chakrabarti }, title = {Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off}, journal = {Applied Soft Computing}, volume = 9, number = 2, year = 2009, pages = {527--540}, doi = {10.1016/j.asoc.2008.07.001}, keywords = {anytime} }
@article{AlbLanSte2010, author = {Albrecht, A. A. and Lane, P. C. R. and Steinh{\"o}fel, K.}, title = {Analysis of Local Search Landscapes for k-{SAT} Instances}, journal = {Mathematics in Computer Science}, number = 4, pages = {465--488}, volume = 3, year = 2010, doi = {10.1007/s11786-010-0040-7} }
@article{Albers2003online, title = {Online Algorithms: A Survey}, author = {Albers, Susanne}, journal = {Mathematical Programming}, year = 2003, number = 1, pages = {3--26}, volume = 97 }
@article{AleMos2016slr, author = {Aldeida Aleti and Irene Moser}, year = 2016, title = {A systematic literature review of adaptive parameter control methods for evolutionary algorithms}, journal = {{ACM} Computing Surveys}, volume = 49, number = {3, Article 56}, month = oct, pages = 35, doi = {10.1145/2996355} }
@article{AlfRuiPagStu2020hybrid, title = {Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems}, author = { Pedro Alfaro-Fern{\'a}ndez and Rub{\'e}n Ruiz and Federico Pagnozzi and Thomas St{\"u}tzle }, journal = {European Journal of Operational Research}, volume = 282, number = 3, pages = {835--845}, year = 2020, doi = {10.1016/j.ejor.2019.10.004}, keywords = {Scheduling, Hybrid flowshop, Automatic algorithm configuration, Automatic Algorithm Design}, abstract = {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.} }
@article{AliMei2011kemeny, annote = {Computational Foundations of Social Choice}, year = 2012, month = jul, publisher = {Elsevier {BV}}, volume = 64, number = 1, pages = {28--40}, author = {Alnur Ali and Marina Meil{\u{a}}}, doi = {10.1016/j.mathsocsci.2011.08.008}, journal = { Mathematical Social Science }, title = {Experiments with {Kemeny} ranking: What Works When?}, keywords = {Borda ranking, Kemeny ranking} }
@article{AllAyd2013, title = {Algorithms for no-wait flowshops with total completion time subject to makespan}, author = {Allahverdi, Ali and Aydilek, Harun}, journal = {International Journal of Advanced Manufacturing Technology}, pages = {1--15}, year = 2013 }
@article{AllJasLieTam2022cor, title = {What if we increase the number of objectives? {Theoretical} and empirical implications for many-objective combinatorial optimization}, author = { Allmendinger, Richard and Andrzej Jaszkiewicz and Arnaud Liefooghe and Tammer, Christiane}, doi = {10.1016/j.cor.2022.105857}, journal = {Computers \& Operations Research}, volume = 145, pages = 105857, year = 2022, publisher = {Elsevier} }
@article{AllKno2013ephemeral, title = {On Handling Ephemeral Resource Constraints in Evolutionary Search}, author = { Allmendinger, Richard and Joshua D. Knowles }, year = 2013, month = sep, journal = {Evolutionary Computation}, volume = 21, number = 3, pages = {497--531}, issn = {1063-6560, 1530-9304}, doi = {10.1162/EVCO_a_00097}, abstract = {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.}, langid = {english} }
@article{Alm10, author = { Christian Almeder }, title = {A hybrid optimization approach for multi-level capacitated lot-sizing problems}, number = 2, journal = {European Journal of Operational Research}, year = 2010, keywords = {Ant colony optimization, Manufacturing, Material requirements planning, Mixed-integer programming}, pages = {599--606}, volume = 200, doi = {10.1016/j.ejor.2009.01.019}, abstract = {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} }
@article{AluKat2004:ieee, author = {S. Alupoaei and S. Katkoori}, title = {Ant Colony System Application to Marcocell Overlap Removal}, journal = {IEEE Transactions on Very Large Scale Integration (VLSI) Systems}, year = 2004, volume = 12, number = 10, pages = {1118--1122} }
@article{Amaral2012corridor, title = {The Corridor Allocation Problem}, journal = {Computers \& Operations Research}, volume = 39, number = 12, pages = {3325--3330}, year = 2012, author = {Amaral, Andr{\'e} R. S.}, doi = {10.1016/j.cor.2012.04.016}, keywords = {Facility layout, Double row layout, Integer programming}, abstract = {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.} }
@article{AmiBadFar2007:cis, author = {Amir, C. and Badr, A. and Farag, I}, title = {A Fuzzy Logic Controller for Ant Algorithms}, journal = {Computing and Information Systems}, year = 2007, volume = 11, number = 2, pages = {26--34} }
@article{AndDefDouJor2003, title = {An Introduction to {MCMC} for Machine Learning}, author = { Christophe Andrieu and Nando de Freitas and Arnaud Doucet and Michael I. Jordan }, journal = {Machine Learning}, volume = 50, number = {1-2}, pages = {5--43}, year = 2003, publisher = {Springer} }
@article{AndFagHob2015maritime, author = {Andersson, Henrik and Fagerholt, Kjetil and Hobbesland, Kirsti}, title = {Integrated maritime fleet deployment and speed optimization: Case study from {RoRo} shipping}, journal = {Computers \& Operations Research}, year = 2015, volume = 55, pages = {233--240}, month = mar, doi = {10.1016/j.cor.2014.03.017} }
@article{AndJorLin1996, author = {Andersen, K. A. and J{\"o}rnsten, K. and Lind, M.}, title = {On bicriterion minimal spanning trees: An approximation}, journal = {Computers \& Operations Research}, volume = 23, number = 12, pages = {1171--1182}, year = 1996 }
@article{AnejaNair79, author = {Aneja, Y. P. and Nair, K. P. K.}, title = {Bicriteria Transportation Problem}, journal = {Management Science}, volume = 25, number = 1, pages = {73--78}, year = 1979 }
@article{AngBamGou2004tcs, author = {Eric Angel and Evripidis Bampis and Laurent Gourv{\'e}s}, title = {Approximating the {Pareto} curve with local search for the bicriteria {TSP}(1,2) problem}, journal = {Theoretical Computer Science}, number = {1-3}, pages = {135--146}, volume = 310, year = 2004, doi = {10.1016/S0304-3975(03)00376-1}, keywords = {Archiving, Local search, Multicriteria TSP, Approximation algorithms} }
@article{AngWoo09, author = { Daniel Angus and Clinton Woodward}, title = {Multiple Objective Ant Colony Optimisation}, journal = {Swarm Intelligence}, year = 2009, volume = 3, number = 1, pages = {69--85}, doi = {10.1007/s11721-008-0022-4} }
@article{AnjVie2017flp, title = {Mathematical optimization approaches for facility layout problems: The state-of-the-art and future research directions}, author = {Anjos, Miguel F. and Vieira, Manuel V. C.}, journal = {European Journal of Operational Research}, volume = 261, number = 1, pages = {1--16}, year = 2017, publisher = {Elsevier} }
@article{AnsBriGou2002qap, author = {Kurt Anstreicher and Nathan Brixius and Jean-Pierre Goux and Jeff Linderoth}, title = {Solving large quadratic assignment problems on computational grids}, doi = {10.1007/s101070100255}, year = 2002, month = feb, volume = 91, number = 3, pages = {563--588}, journal = {Mathematical Programming Series B}, abstract = {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.} }
@article{AppBixChvCoo03:mp, author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook }, title = {Implementing the {Dantzig}-{Fulkerson}-{Johnson} Algorithm for Large Traveling Salesman Problems}, journal = {Mathematical Programming Series B}, year = 2003, volume = 97, number = {1--2}, pages = {91--153}, doi = {10.1007/s10107-003-0440-4} }
@article{AppBixChvCoo98, author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook }, title = {On the Solution of Traveling Salesman Problems}, journal = {Documenta Mathematica}, year = 1998, volume = {Extra Volume ICM III}, pages = {645--656} }
@article{AppBlaNew1961, author = {Appleby, J. S. and Blake, D. V. and Newman, E. A.}, title = {Techniques for producing school timetables on a computer and their application to other scheduling problems}, journal = {The Computer Journal}, year = 1961, volume = 3, number = 4, pages = {237--245}, doi = {10.1093/comjnl/3.4.237} }
@article{AppCoo91, author = { David Applegate and William J. Cook }, title = {A Computational Study of the Job-Shop Scheduling Problem}, journal = {ORSA Journal on Computing}, year = 1991, volume = 3, number = 2, pages = {149--156} }
@article{AppCooRoh2003, title = {Chained {Lin}-{Kernighan} for Large Traveling Salesman Problems}, author = { David Applegate and William J. Cook and Andr{\'e} Rohe}, journal = {INFORMS Journal on Computing}, volume = 15, number = 1, pages = {82--92}, year = 2003, doi = {10.1287/ijoc.15.1.82.15157} }
@article{AppEtAl09, author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook and D. Espinoza and M. Goycoolea and Keld Helsgaun }, title = {Certification of an Optimal {TSP} Tour Through 85,900 Cities}, journal = {Operations Research Letters}, volume = 37, number = 1, year = 2009, pages = {11--15} }
@article{AraCamCam2022openletter, author = {Claus Aranha and Camacho-Villal\'{o}n, Christian Leonardo and Felipe Campelo and Marco Dorigo and Rub{\'e}n Ruiz and Marc Sevaux and Kenneth S{\"o}rensen and Thomas St{\"u}tzle }, title = {Metaphor-based Metaheuristics, a Call for Action: the Elephant in the Room}, journal = {Swarm Intelligence}, pages = {1--6}, volume = 16, number = 1, doi = {10.1007/s11721-021-00202-9}, year = 2022 }
@article{ArcSavSpe2016vehicle, title = {The Vehicle Routing Problem with Occasional Drivers}, author = {Archetti, Claudia and Martin Savelsbergh and Speranza, M. Grazia }, journal = {European Journal of Operational Research}, volume = 254, number = 2, pages = {472--480}, year = 2016, doi = {10.1016/j.ejor.2016.03.049}, publisher = {Elsevier} }
@article{ArnSanSorVid2019, author = {Florian Arnold and Santana, \'{I}talo and Kenneth S{\"o}rensen and Thibaut Vidal }, title = {{PILS}: Exploring high-order neighborhoods by pattern mining and injection}, journal = {Arxiv preprint arXiv:1912.11462 [cs.AI]}, year = 2019, doi = {10.48550/arXiv.1912.11462} }
@article{ArnSor2019knowledge, author = {Florian Arnold and Kenneth S{\"o}rensen }, title = {Knowledge-guided local search for the vehicle routing problem}, journal = {Computers \& Operations Research}, year = 2019, volume = 105, pages = {32--46}, doi = {10.1016/j.cor.2019.01.002} }
@article{ArnSor2019vrp, author = {Florian Arnold and Kenneth S{\"o}rensen }, title = {What makes a {VRP} solution good? The generation of problem-specific knowledge for heuristics}, journal = {Computers \& Operations Research}, year = 2019, volume = 106, pages = {280--288}, doi = {10.1016/j.cor.2018.02.007} }
@article{AroKadKhu2006, title = {An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problems}, author = {Arostegui Jr, Marvin A. and Kadipasaoglu, Sukran N. and Khumawala, Basheer M.}, journal = {International Journal of Production Economics}, volume = 103, number = 2, pages = {742--754}, year = 2006, publisher = {Elsevier} }
@article{Arr04, title = {A partial enumeration heuristic for multi-objective flowshop scheduling problems}, author = { Jos{\'e} Elias C. Arroyo and V. A. Armentano}, journal = {Journal of the Operational Research Society}, volume = 55, number = 9, pages = {1000--1007}, year = 2004 }
@article{ArrArm05, author = { Jos{\'e} Elias C. Arroyo and V. A. Armentano}, title = {Genetic local search for multi-objective flowshop scheduling problems}, journal = {European Journal of Operational Research}, volume = 167, number = 3, pages = {717--738}, year = 2005, keywords = {Multicriteria Scheduling} }
@article{ArrLeu2017, author = { Jos{\'e} Elias C. Arroyo and Joseph Y.-T. Leung }, title = {An Effective Iterated Greedy Algorithm for Scheduling Unrelated Parallel Batch Machines with Non-identical Capacities and Unequal Ready Times}, journal = {Computers and Industrial Engineering}, year = 2017, volume = 105, pages = {84--100} }
@article{ArzCebIru2022jcgs, author = {Arza, Etor and Josu Ceberio and Irurozki, Ekhine and P{\'e}rez, Aritz}, title = {Comparing Two Samples Through Stochastic Dominance: A Graphical Approach}, journal = {Journal of Computational and Graphical Statistics}, year = 2022, pages = {1--38}, month = jun, doi = {10.1080/10618600.2022.2084405} }
@article{Asch01tsptw, author = { N. Ascheuer and Matteo Fischetti and M. Gr{\"o}tschel }, title = {Solving asymmetric travelling salesman problem with time windows by branch-and-cut}, journal = {Mathematical Programming}, year = 2001, volume = 90, pages = {475--506} }
@article{AssWanFre2014hetero, author = {John{-}Alexander M. Assael and Ziyu Wang and Nando de Freitas }, title = {Heteroscedastic Treed Bayesian Optimisation}, journal = {Arxiv preprint arXiv:1410.7172}, doi = {10.48550/arXiv.1410.7172}, year = 2014, eprinttype = {arXiv}, eprint = {1410.7172}, keywords = {Treed-GP} }
@article{Ata2003mik, author = { Alper Atamt{\"u}rk }, title = {On the facets of the mixed--integer knapsack polyhedron}, journal = {Mathematical Programming}, year = 2003, volume = 98, number = 1, pages = {145--175}, doi = {10.1007/s10107-003-0400-z} }
@article{AudDanOrb2014, author = { Charles Audet and Cong-Kien Dang and Dominique Orban }, title = {Optimization of Algorithms with {OPAL}}, journal = {Mathematical Programming Computation}, year = 2014, volume = 6, number = 3, pages = {233--254} }
@article{AudEgla1977, title = {New approach to the design of multifactor experiments}, author = {Audze, P. and Egl{\~a}js, Vilnis}, journal = {Problems of Dynamics and Strengths}, year = 1977, note = {(in Russian)}, pages = {104--107}, volume = 35, publisher = {Zinatne Publishing House, Riga} }
@article{AudOrb06:mads, author = { Charles Audet and Dominique Orban }, title = {Finding Optimal Algorithmic Parameters Using Derivative-Free Optimization}, journal = {SIAM Journal on Optimization}, year = 2006, volume = 17, number = 3, pages = {642--664}, keywords = {mesh adaptive direct search; pattern search}, doi = {10.1137/0406208} }
@article{Aue2002using, title = {Using Confidence Bounds for Exploitation-Exploration Trade-offs}, author = {Auer, Peter}, journal = {Journal of Machine Learning Research}, volume = 3, month = nov, pages = {397--422}, year = 2002, abstract = {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 $O(T^{1/2})$.} }
@article{AueCesFis2002finite, title = {Finite-time analysis of the multiarmed bandit problem}, author = {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul}, journal = {Machine Learning}, volume = 47, number = {2-3}, pages = {235--256}, year = 2002 }
@article{AugBadBroZit2012tcs, author = { Anne Auger and Johannes Bader and Dimo Brockhoff and Eckart Zitzler }, title = {Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications}, journal = {Theoretical Computer Science}, volume = 425, year = 2012, pages = {75--103}, doi = {10.1016/j.tcs.2011.03.012} }
@article{AvcTop2017:cor, author = {Mustafa Avci and Seyda Topaloglu}, title = {A Multi-start Iterated Local Search Algorithm for the Generalized Quadratic Multiple Knapsack Problem}, journal = {Computers \& Operations Research}, year = 2017, volume = 83, pages = {54--65} }
@article{AvrAllLop2021arxiv, author = { Andreea Avramescu and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models}, year = 2021, journal = {Arxiv preprint arXiv:2105.12699 [econ.GN]}, url = {https://arxiv.org/abs/2105.12699} }
@article{AydYavStu2017:si, author = { Do\v{g}an Ayd{\i}n and G{\"{u}}rcan Yavuz and Thomas St{\"u}tzle }, title = {{ABC-X:} A Generalized, Automatically Configurable Artificial Bee Colony Framework}, journal = {Swarm Intelligence}, year = 2017, volume = 11, number = 1, pages = {1--38} }
@article{AyoAllLopPar2022scalarisation, author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu }, title = {A Study of Scalarisation Techniques for Multi-Objective {QUBO} Solving}, journal = {Arxiv preprint arXiv:2210.11321}, year = 2022, doi = {10.48550/arXiv.2210.11321} }
@article{AziTay2014eaai, author = {Mahdi Aziz and {Tayarani-N}, Mohammad-H.}, title = {An adaptive memetic Particle Swarm Optimization algorithm for finding large-scale Latin hypercube designs}, journal = {Engineering Applications of Artificial Intelligence}, volume = 36, pages = {222--237}, year = 2014, doi = {10.1016/j.engappai.2014.07.021}, keywords = {F-race} }
@article{BacHelPic2020gaussian, title = {Gaussian process optimization with failures: Classification and convergence proof}, author = {Bachoc, Fran{\c c}ois and Helbert, C{\'e}line and Picheny, Victor}, journal = {Journal of Global Optimization}, year = 2020, epub = {https://hal.archives-ouvertes.fr/hal-02100819/file/optimwithfailurerevised_hal.pdf}, 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}, doi = {10.1007/s10898-020-00920-0}, abstract = {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.} }
@article{BadZit2011ec, author = { Johannes Bader and Eckart Zitzler }, title = {{HypE}: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization}, journal = {Evolutionary Computation}, volume = 19, number = 1, year = 2011, pages = {45--76}, doi = {10.1162/EVCO_a_00009} }
@article{BahComLau2019tre, title = {Bi-objective multi-layer location--\hspace{0pt}allocation model for the immediate aftermath of sudden-onset disasters}, author = {Baharmand, Hossein and Comes, Tina and Lauras, Matthieu}, journal = {Transportation Research Part E: Logistics and Transportation Review}, volume = 127, pages = {86--110}, year = 2019, doi = {10.1016/j.tre.2019.05.002}, abstract = {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.} }
@article{Baker2016reprod, title = {Is there a reproducibility crisis?}, author = {Monya Baker}, journal = {Nature}, volume = 533, pages = {452--454}, year = 2016 }
@article{Baker83:tsptw, author = {Edward K. Baker}, title = {An Exact Algorithm for the Time-Constrained Traveling Salesman Problem}, volume = 31, doi = {10.1287/opre.31.5.938}, number = 5, journal = {Operations Research}, year = 1983, pages = {938--945}, anote = {makespan optimization} }
@article{BalBea2008, title = {Facility location in humanitarian relief}, author = {Balcik, Burcu and Beamon, Benita M.}, journal = {International Journal of Logistics}, volume = 11, number = 2, pages = {101--121}, year = 2008, publisher = {Taylor \& Francis} }
@article{BalBirStuDor2009ejor, author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo }, title = {Adaptive Sampling Size and Importance Sampling in Estimation-based Local Search for the Probabilistic Traveling Salesman Problem}, journal = {European Journal of Operational Research}, year = 2009, volume = 199, number = 1, pages = {98--110} }
@article{BalBirStuDor2010cor, author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo }, title = {Estimation-based Metaheuristics for the Probabilistic Travelling Salesman Problem}, journal = {Computers \& Operations Research}, year = 2010, volume = 37, number = 11, pages = {1939--1951}, doi = {10.1016/j.cor.2009.12.005} }
@article{BalBirStuDor2015coa, author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Marco Dorigo }, title = {Estimation-based Metaheuristics for the Single Vehicle Routing Problem with Stochastic Demands and Customers}, journal = {Computational Optimization and Applications}, year = 2015, volume = 61, number = 2, pages = {463--487}, doi = {10.1007/s10589-014-9719-z} }
@article{BalBirStuYuaDor09, author = { Prasanna Balaprakash and Mauro Birattari and Thomas St{\"u}tzle and Zhi Yuan and Marco Dorigo }, title = {Estimation-based Ant Colony Optimization Algorithms for the Probabilistic Travelling Salesman Problem}, journal = {Swarm Intelligence}, volume = 3, number = 3, year = 2009, pages = {223--242} }
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@article{BalSim01tsptw, author = { Egon Balas and Neil Simonetti }, title = {Linear Time Dynamic-Programming Algorithms for New Classes of Restricted {TSP}s: {A} Computational Study}, volume = 13, doi = {10.1287/ijoc.13.1.56.9748}, abstract = {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.}, number = 1, journal = {INFORMS Journal on Computing}, year = 2001, keywords = {tsptw}, pages = {56--75} }
@article{BalVaz1998, author = { Egon Balas and A. Vazacopoulos}, title = {Guided Local Search with Shifting Bottleneck for Job Shop Scheduling}, journal = {Management Science}, year = 1998, volume = 44, number = 2, pages = {262--275} }
@article{Bankes2002, title = {Tools and techniques for developing policies for complex and uncertain systems}, author = { Bankes, Steven C. }, volume = 99, number = {suppl 3}, pages = {7263--7266}, year = 2002, abstract = {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}, journal = {Proceedings of the National Academy of Sciences}, doi = {10.1073/pnas.092081399} }
@article{BarBatSenSil2015ijem, title = {Improving the Performance of Metaheuristics: An Approach Combining Response Surface Methodology and Racing Algorithms}, author = {Eduardo Batista de Moraes Barbosa and Edson Luiz Franc{\c{c}}a Senne and Messias Borges Silva}, journal = {International Journal of Engineering Mathematics}, year = 2015, volume = 2015, pages = {Article ID 167031}, doi = {10.1155/2015/167031}, keywords = {F-race} }
@article{BarDiaSerBen2020xai, doi = {10.1016/j.inffus.2019.12.012}, year = 2020, month = jun, publisher = {Elsevier {BV}}, volume = 58, pages = {82--115}, author = {Alejandro Barredo Arrieta and Natalia D{\'{i}}az-Rodr{\'{i}}guez and Javier Del Ser and Adrien Bennetot and Siham Tabik and Alberto Barbado and Salvador Garcia and Sergio Gil-Lopez and Daniel Molina and Richard Benjamins and Raja Chatila and Francisco Herrera}, title = {Explainable Artificial Intelligence ({XAI}): Concepts, taxonomies, opportunities and challenges toward responsible {AI}}, journal = {Information Fusion} }
@article{BarDoeBer2020benchmarking, title = {Benchmarking in Optimization: Best Practice and Open Issues}, author = { Thomas Bartz-Beielstein and Carola Doerr and Daan van den Berg and Jakob Bossek and Sowmya Chandrasekaran and Tome Eftimov and Andreas Fischbach and Pascal Kerschke and William {La Cava} and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Katherine M. Malan and Jason H. Moore and Boris Naujoks and Patryk Orzechowski and Vanessa Volz and Markus Wagner and Thomas Weise}, year = 2020, journal = {Arxiv preprint arXiv:2007.03488 [cs.NE]}, url = {https://arxiv.org/abs/2007.03488} }
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@article{BatPas2010tec, author = { Roberto Battiti and Andrea Passerini }, title = {Brain-Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker}, journal = {IEEE Transactions on Evolutionary Computation}, volume = 14, number = 5, year = 2010, pages = {671--687}, doi = {10.1109/TEVC.2010.2058118}, keywords = {BC-EMOA}, annote = {Errata: DTLZ6 and DTLZ7 in the paper are actually DTLZ7 and DTLZ8 in \cite{DebThiLau2005dtlz}} }
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@article{BatSchUrl2017, author = {Michele Battistutta and Andrea Schaerf and Tommaso Urli }, title = {Feature-based Tuning of Single-stage Simulated Annealing for Examination Timetabling}, journal = {Annals of Operations Research}, year = 2017, volume = 252, number = 2, pages = {239--254} }
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@article{BeaShaSmiLop2018review, author = {Bealt, Jennifer and Shaw, Duncan and Smith, Chris M. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, year = 2019, title = {Peer Reviews for Making Cities Resilient: A Systematic Literature Review}, journal = {International Journal of Emergency Management}, volume = 15, number = 4, pages = {334--359}, doi = {10.1504/IJEM.2019.104201}, abstract = {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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@article{BelCesDigSchUrl2016, author = {Ruggero Bellio and Sara Ceschia and Luca {Di Gaspero} and Andrea Schaerf and Tommaso Urli }, title = {Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem}, journal = {Computers \& Operations Research}, volume = 65, pages = {83--92}, year = 2016, publisher = {Elsevier} }
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@article{BenKao2013, author = {Una Benlic and Jin-Kao Hao }, title = {Breakout Local Search for the Quadratic Assignment Problem}, journal = {Applied Mathematics and Computation}, year = 2013, volume = 219, number = 9, pages = {4800--4815} }
@article{BenLiuAuIst2014transient, title = {Transient protein-protein interface prediction: datasets, features, algorithms, and the {RAD-T} predictor}, author = {Bendell, Calem J. and Liu, Shalon and Aumentado-Armstrong, Tristan and Istrate, Bogdan and Cernek, Paul T. and Khan, Samuel and Picioreanu, Sergiu and Zhao, Michael and Murgita, Robert A.}, journal = {BMC Bioinformatics}, volume = 15, pages = 82, year = 2014 }
@article{BenLodPro2021ml, author = { Bengio, Yoshua and Andrea Lodi and Antoine Prouvost}, title = {Machine learning for combinatorial optimization: A methodological tour d'horizon}, journal = {European Journal of Operational Research}, year = 2021, volume = 290, number = 2, pages = {405--421}, doi = {10.1016/j.ejor.2020.07.063}, keywords = {Combinatorial optimization, Machine learning, Branch and bound, Mixed-integer programming solvers}, abstract = {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.} }
@article{BenRit2016:cor, author = {Alexander Javier Benavides and Marcus Ritt}, title = {Two Simple and Effective Heuristics for Minimizing the Makespan in Non-permutation Flow Shops}, journal = {Computers \& Operations Research}, year = 2016, volume = 66, pages = {160--169}, doi = {10.1016/j.cor.2015.08.001} }
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@article{Bentley1980, author = { Jon Louis Bentley }, title = {Multidimensional Divide-and-conquer}, journal = {Communications of the ACM}, year = 1980, volume = 23, number = 4, doi = {10.1145/358841.358850}, pages = {214--229}, abstract = {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.} }
@article{BerBen2012jmlr, author = { James S. Bergstra and Bengio, Yoshua }, title = {Random Search for Hyper-Parameter Optimization}, journal = {Journal of Machine Learning Research}, year = 2012, volume = 13, pages = {281--305}, abstract = {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.}, epub = {http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf} }
@article{BerEmmTav2017managing, title = {Managing catastrophic climate risks under model uncertainty aversion}, author = {Berger, Lo{\"i}c and Emmerling, Johannes and Tavoni, Massimo}, journal = {Management Science}, volume = 63, number = 3, pages = {749--765}, year = 2017, publisher = {{INFORMS}} }
@article{BerFisLod2007, title = {A feasibility pump heuristic for general mixed-integer problems}, author = {Bertacco, Livio and Matteo Fischetti and Andrea Lodi }, journal = {Discrete Optimization}, volume = 4, number = 1, pages = {63--76}, year = 2007, publisher = {Elsevier} }
@article{BerKal2020, title = {From predictive to prescriptive analytics}, author = {Bertsimas, Dimitris and Kallus, Nathan}, journal = {Management Science}, volume = 66, number = 3, pages = {1025--1044}, year = 2020, publisher = {{INFORMS}} }
@article{BerKraSch2016bayesian, author = {Felix Berkenkamp and Andreas Krause and Angela P. Schoellig}, title = {Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics}, journal = {Arxiv preprint arXiv:1602.04450}, year = 2016, url = {http://arxiv.org/abs/1602.04450}, keywords = {Safe Optimization, SafeOpt} }
@article{BerKraSch2021bayesian, author = {Berkenkamp, Felix and Krause, Andreas and Schoellig, Angela P.}, title = {Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics}, journal = {Machine Learning}, year = 2021, month = jun, annote = {Preprint: \url{http://arxiv.org/abs/1602.04450}}, doi = {10.1007/s10994-021-06019-1}, abstract = {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.} }
@article{BerTsiWu1997:joh, author = {Dimitri P. Bertsekas and John N. Tsitsiklis and Cynara Wu}, title = {Rollout Algorithms for Combinatorial Optimization}, journal = {Journal of Heuristics}, year = 1997, volume = 3, number = 3, pages = {245--262} }
@article{BerWan1987binpack, title = {Two-dimensional finite bin-packing algorithms}, author = {Berkey, Judith O. and Wang, Pearl Y.}, journal = {Journal of the Operational Research Society}, volume = 38, number = 5, pages = {423--429}, year = 1987, doi = {10.2307/2582731} }
@article{BeuFonLopPaqVah09:tec, author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold }, title = {On the complexity of computing the hypervolume indicator}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2009, volume = 13, number = 5, pages = {1075--1082}, doi = {10.1109/TEVC.2009.2015575}, abstract = {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 \emph{hypervolume indicator} (or \emph{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 $\mathcal{O}(n \log n + n^{d/2}\log n)$ comparisons for an input instance of size $n$ in $d$ dimensions; as of this writing, it is unknown whether a lower bound higher than $\Omega(n \log n)$ can be proven. In this article, we derive a lower bound of $\Omega(n\log n)$ for the complexity of computing the hypervolume indicator in any number of dimensions $d>1$ by reducing the so-called \textsc{UniformGap} problem to it. For the three dimensional case, we also present a matching upper bound of $\mathcal{O}(n\log n)$ comparisons that is obtained by extending an algorithm for finding the maxima of a point set.} }
@article{BeuNauEmm2007ejor, author = { Nicola Beume and Boris Naujoks and Emmerich, Michael T. M. }, title = {{SMS-EMOA}: Multiobjective selection based on dominated hypervolume}, journal = {European Journal of Operational Research}, year = 2007, volume = 181, number = 3, pages = {1653--1669}, doi = {10.1016/j.ejor.2006.08.008} }
@article{BeySch2002:es, author = { Hans-Georg Beyer and Hans-Paul Schwefel }, title = {Evolution Strategies: A Comprehensive Introduction}, journal = {Natural Computing}, volume = 1, pages = {3--52}, year = 2002 }
@article{BeySchWeg2002, title = {How to analyse evolutionary algorithms}, author = { Hans-Georg Beyer and Hans-Paul Schwefel and Ingo Wegener }, journal = {Theoretical Computer Science}, volume = 287, number = 1, pages = {101--130}, year = 2002, publisher = {Elsevier} }
@article{BezLopStu2015tec, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatic Component-Wise Design of Multi-Objective Evolutionary Algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2016, volume = 20, number = 3, pages = {403--417}, doi = {10.1109/TEVC.2015.2474158}, supplement = {https://github.com/iridia-ulb/automoea-tevc-2016} }
@article{BezLopStu2017assessment, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms}, year = 2018, journal = {Evolutionary Computation}, doi = {10.1162/evco_a_00217}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-007/}, volume = 26, number = 4, pages = {621--656}, abstract = {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.} }
@article{BezLopStu2019ec, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms}, journal = {Evolutionary Computation}, year = 2020, volume = 28, number = 2, pages = {195--226}, doi = {10.1162/evco_a_00263}, supplement = {https://github.com/iridia-ulb/automoea-ecj-2020}, abstract = {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.} }
@article{BiaBirMan2006jmma, author = { Leonora Bianchi and Mauro Birattari and M. Manfrin and M. Mastrolilli and Lu{\'i}s Paquete and O. Rossi-Doria and Tommaso Schiavinotto }, title = {Hybrid Metaheuristics for the Vehicle Routing Problem with Stochastic Demands}, journal = {Journal of Mathematical Modelling and Algorithms}, year = 2006, volume = 5, number = 1, pages = {91--110} }
@article{BiaDorGam2009survey, title = {A survey on metaheuristics for stochastic combinatorial optimization}, author = { Leonora Bianchi and Marco Dorigo and L. M. Gambardella and Gutjahr, Walter J. }, journal = {Natural Computing}, volume = 8, number = 2, pages = {239--287}, year = 2009 }
@article{BinGinRou2015gaupar, title = {Quantifying uncertainty on {Pareto} fronts with {Gaussian} process conditional simulations}, volume = 243, doi = {10.1016/j.ejor.2014.07.032}, abstract = {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.}, number = 2, journal = {European Journal of Operational Research}, author = {Binois, M. and Ginsbourger, D. and Roustant, O.}, year = 2015, keywords = {Attainment function, Expected Hypervolume Improvement, Kriging, Multi-objective optimization, Vorob'ev expectation}, pages = {386--394} }
@article{BirBalStuDor07:informs, author = { Mauro Birattari and Prasanna Balaprakash and Thomas St{\"u}tzle and Marco Dorigo }, title = {Estimation Based Local Search for Stochastic Combinatorial Optimization}, journal = {INFORMS Journal on Computing}, year = 2008, volume = 20, number = 4, pages = {644--658} }
@article{BirPelDor2007:tec, author = { Mauro Birattari and Paola Pellegrini and Marco Dorigo }, title = {On the invariance of ant colony optimization}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2007, volume = 11, number = 6, pages = {732--742}, doi = {10.1109/TEVC.2007.892762} }
@article{BirZloDor06meta_design, author = { Mauro Birattari and Zlochin, M. and Marco Dorigo }, title = {Towards a theory of practice in metaheuristics design: A machine learning perspective}, journal = {Theoretical Informatics and Applications}, year = 2006, volume = 40, number = 2, pages = {353--369} }
@article{BisIzzYam2010:pagmo-arxiv, title = {A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation}, author = {Biscani, Francesco and Dario Izzo and Yam, Chit Hong}, journal = {Arxiv preprint arXiv:1004.3824}, year = 2010, url = {http://arxiv.org/abs/1004.3824}, keywords = {PaGMO}, abstract = {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.} }
@article{BisBinLan2023wirdmkd, title = {Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges}, author = { Bernd Bischl and Binder, Martin and Lang, Michel and Pielok, Tobias and Richter, Jakob and Coors, Stefan and Thomas, Janek and Ullmann, Theresa and Becker, Marc and Boulesteix, Anne-Laure and Deng, Difan and Marius Thomas Lindauer }, journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery}, volume = 13, number = 2, pages = {e1484}, year = 2023, publisher = {Wiley Online Library} }
@article{BisKerKot++16:ASlib, author = { Bernd Bischl and Pascal Kerschke and Kotthoff, Lars and Marius Thomas Lindauer and Yuri Malitsky and Alexandre Fr{\'{e}}chette and Holger H. Hoos and Frank Hutter and Kevin Leyton-Brown and Kevin Tierney and Joaquin Vanschoren }, title = {{ASlib}: A Benchmark Library for Algorithm Selection}, journal = {Artificial Intelligence}, year = 2016, volume = 237, pages = {41--58} }
@article{BisLanKot2016mlr, title = {{\rpackage{mlr}}: Machine Learning in \proglang{R}}, author = { Bernd Bischl and Michel Lang and Kotthoff, Lars and Julia Schiffner and Jakob Richter and Erich Studerus and Giuseppe Casalicchio and Zachary M. Jones}, journal = {Journal of Machine Learning Research}, year = 2016, volume = 17, number = 170, pages = {1--5}, epub = {http://jmlr.org/papers/v17/15-066.html} }
@article{BlaHerSanMar2008vis, title = {A new graphical visualization of n-dimensional {Pareto} front for decision-making in multiobjective optimization}, author = {Blasco, Xavier and Herrero, Juan M. and Sanchis, Javier and Mart{\'i}nez, Manuel}, journal = {Information Sciences}, volume = 178, number = 20, pages = {3908--3924}, year = 2008, publisher = {Elsevier} }
@article{BlaRayEde2017:corr, author = {Craig Blackmore and Oliver Ray and Kerstin Eder}, title = {Automatically Tuning the {GCC} Compiler to Optimize the Performance of Applications Running on Embedded Systems}, journal = {Arxiv preprint arXiv:1703.08228}, url = {https://arxiv.org/abs/1703.08228}, year = 2017 }
@article{BleBlu2007:jmma, author = { Mar{\'i}a J. Blesa and Christian Blum }, title = {Finding edge-disjoint paths in networks by means of artificial ant colonies}, journal = {Journal of Mathematical Modelling and Algorithms}, year = 2007, volume = 6, number = 3, pages = {361--391} }
@article{BliCosRefZha2023aitsp, title = {The First {AI4TSP} Competition: Learning to Solve Stochastic Routing Problems}, journal = {Artificial Intelligence}, pages = 103918, volume = 319, year = 2023, issn = {0004-3702}, doi = {10.1016/j.artint.2023.103918}, author = {Laurens Bliek and Paulo {da Costa} and Reza {Refaei Afshar} and Robbert Reijnen and Yingqian Zhang and Tom Catshoek and Dani{\"e}l Vos and Sicco Verwer and Fynn Schmitt-Ulms and Andr{\'e} Hottung and Tapan Shah and Meinolf Sellmann and Kevin Tierney and Carl Perreault-Lafleur and Caroline Leboeuf and Federico Bobbio and Justine Pepin and Warley Almeida Silva and Ricardo Gama and Hugo L. Fernandes and Martin Zaefferer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Irurozki, Ekhine }, keywords = {AI for TSP competition, Travelling salesman problem, Routing problem, Stochastic combinatorial optimization, Surrogate-based optimization, Deep reinforcement learning}, abstract = {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 \url{https://github.com/paulorocosta/ai-for-tsp-competition}.} }
@article{Blu05:cor, author = { Christian Blum }, title = {{Beam-ACO}---{Hybridizing} Ant Colony Optimization with Beam Search: {An} Application to Open Shop Scheduling}, journal = {Computers \& Operations Research}, year = 2005, volume = 32, number = 6, pages = {1565--1591} }
@article{Blu08:informs, author = { Christian Blum }, title = {Beam-{ACO} for simple assembly line balancing}, journal = {INFORMS Journal on Computing}, year = 2008, volume = 20, number = 4, pages = {618--627}, doi = {10.1287/ijoc.1080.0271} }
@article{BluBleLop09-BeamSearch-LCS, author = { Christian Blum and Mar{\'i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Beam search for the longest common subsequence problem}, number = 12, journal = {Computers \& Operations Research}, year = 2009, pages = {3178--3186}, volume = 36, doi = {10.1016/j.cor.2009.02.005}, abstract = {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.} }
@article{BluCaBle2015swarm, author = { Christian Blum and Borja Calvo and Mar{\'i}a J. Blesa }, title = {{FrogCOL} and {FrogMIS}: New Decentralized Algorithms for Finding Large Independent Sets in Graphs}, journal = {Swarm Intelligence}, year = 2015, volume = 9, number = {2-3}, pages = {205--227}, doi = {10.1007/s11721-015-0110-1}, keywords = {irace} }
@article{BluDor03:ieee_tsmcb, author = { Christian Blum and Marco Dorigo }, title = {The hyper-cube framework for ant colony optimization}, journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part B}, year = 2004, volume = 34, number = 2, pages = {1161--1172} }
@article{BluDor2005:tec, author = { Christian Blum and Marco Dorigo }, journal = {IEEE Transactions on Evolutionary Computation}, number = 2, pages = {159--174}, title = {Search Bias in Ant Colony Optimization: On the Role of Competition-Balanced Systems}, volume = 9, year = 2005 }
@article{BluOch2021, author = { Christian Blum and Gabriela Ochoa }, title = {A comparative analysis of two matheuristics by means of merged local optima networks}, journal = {European Journal of Operational Research}, volume = 290, number = 1, pages = {36--56}, year = 2021 }
@article{BluPinLopLoz2015cor, author = { Christian Blum and Pedro Pinacho and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} A. Lozano }, title = {Construct, Merge, Solve \& Adapt: A New General Algorithm for Combinatorial Optimization}, journal = {Computers \& Operations Research}, year = 2016, volume = 68, pages = {75--88}, doi = {10.1016/j.cor.2015.10.014}, keywords = {irace, CMSA} }
@article{BluPucRaiRol11:asc, author = { Christian Blum and Jakob Puchinger and G{\"u}nther R. Raidl and Andrea Roli }, title = {Hybrid Metaheuristics in Combinatorial Optimization: A Survey}, journal = {Applied Soft Computing}, year = 2011, volume = 11, number = 6, pages = {4135--4151} }
@article{BluRol03:acm-cs, author = { Christian Blum and Andrea Roli }, title = {Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison}, journal = {{ACM} Computing Surveys}, year = 2003, volume = 35, number = 3, pages = {268--308} }
@article{BluSam2004:jmma, author = { Christian Blum and M. Sampels }, title = {An Ant Colony Optimization Algorithm for Shop Scheduling Problems}, journal = {Journal of Mathematical Modelling and Algorithms}, year = 2004, volume = 3, number = 3, pages = {285--308}, doi = {10.1023/B:JMMA.0000038614.39977.6f} }
@article{BluYabBle08:cor, author = { Christian Blum and M. {Y{\'a}bar Vall{\`e}s} and Mar{\'i}a J. Blesa }, title = {An ant colony optimization algorithm for {DNA} sequencing by hybridization}, journal = {Computers \& Operations Research}, year = 2008, volume = 35, number = 11, pages = {3620--3635} }
@article{BocFawVal2018performance, author = {Bocchese, Andrea F. and Chris Fawcett and Vallati, Mauro and Gerevini, Alfonso E. and Holger H. Hoos }, title = {Performance robustness of {AI} planners in the 2014 International Planning Competition}, volume = 31, doi = {10.3233/AIC-170537}, abstract = {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.}, number = 6, journal = {AI Communications}, publisher = {IOS Press}, year = 2018, month = dec, pages = {445--463} }
@article{BoeKahMud1994, author = {Kenneth D. Boese and Andrew B. Kahng and Sudhakar Muddu}, title = {A New Adaptive Multi-Start Technique for Combinatorial Global Optimization}, journal = {Operations Research Letters}, year = 1994, volume = 16, number = 2, pages = {101--113}, keywords = {big-valley hypothesis, TSP, landscape analysis} }
@article{Boh2009idcs, author = {Marko Bohanec}, title = {Decision making: a computer-science and information-technology viewpoint}, journal = {Interdisciplinary Description of Complex Systems}, year = 2009, volume = 7, number = 2, pages = {22--37} }
@article{BohJohSte1986, title = {Generalized Simulated Annealing for Function Optimization}, author = { Ihor O. Bohachevsky and Mark E. Johnson and Myron L. Stein }, journal = {Technometrics}, volume = 28, number = 3, pages = {209--217}, year = 1986, publisher = {Taylor \& Francis} }
@article{Bor2000, title = {{CHESS} - Changing Horizon Efficient Set Search: A simple principle for multiobjective optimization}, author = {Borges, P. C.}, journal = {Journal of Heuristics}, volume = 6, number = 3, pages = {405--418}, year = 2000 }
@article{BorHamTav2007joh, author = {Boros, Endre and Hammer, Peter L. and Tavares, Gabriel}, title = {Local search heuristics for Quadratic Unconstrained Binary Optimization ({QUBO})}, journal = {Journal of Heuristics}, year = 2007, volume = 13, number = 2, pages = {99--132} }
@article{Borda1781, author = {Jean-Charles de Borda}, journal = {Histoire de l'Acad{\'e}mie Royal des Sciences}, title = {M{\'e}moire sur les {\'E}lections au Scrutin}, year = 1781, keywords = {ranking} }
@article{BotBon98, author = {Hozefa M. Botee and Eric Bonabeau}, title = {Evolving Ant Colony Optimization}, year = 1998, journal = {Advances in Complex Systems}, volume = 1, pages = {149--159} }
@article{BotSch2019dominance, title = {Dominance for multi-objective robust optimization concepts}, author = {Botte, Marco and Sch{\"o}bel, Anita }, journal = {European Journal of Operational Research}, volume = 273, number = 2, pages = {430--440}, year = 2019, publisher = {Elsevier} }
@article{BouBluBou2012, author = {Salim Bouamama and Christian Blum and Abdellah Boukerram}, title = {A Population-based Iterated Greedy Algorithm for the Minimum Weight Vertex Cover Problem}, journal = {Applied Soft Computing}, year = 2012, volume = 12, number = 6, pages = {1632--1639} }
@article{BouForGliPir2010:ejor, author = { G{\'e}raldine Bous and Philippe Fortemps and Fran\c{c}ois Glineur and Marc Pirlot }, title = {{ACUTA}: {A} novel method for eliciting additive value functions on the basis of holistic preference statements}, journal = {European Journal of Operational Research}, year = 2010, volume = 206, number = 2, pages = {435--444} }
@article{BouLec2003ejor, author = {Bouleimen, K. and Lecocq, H.}, title = {A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version}, volume = 149, doi = {10.1016/S0377-2217(02)00761-0}, abstract = {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.}, number = 2, journal = {European Journal of Operational Research}, year = 2003, keywords = {multi-mode resource-constrained project scheduling, project scheduling, simulated annealing}, pages = {268--281} }
@article{BozFowGelKim2010or, title = {Quantitative comparison of approximate solution sets for multicriteria optimization problems with weighted {Tchebycheff} preference function}, author = {Bozkurt, B. and Fowler, J. W. and Gel, E. S. and Kim, B. and Murat K{\"o}ksalan and Wallenius, Jyrki }, journal = {Operations Research}, year = 2010, number = 3, pages = {650--659}, volume = 58, publisher = {INFORMS}, annote = {Proposed IPF indicator} }
@article{BraGreSlo2010bpas, title = {Interactive evolutionary multiobjective optimization driven by robust ordinal regression}, author = { J{\"u}rgen Branke and Salvatore Greco and Roman S{\l}owi{\'n}ski and Zielniewicz, P}, journal = {Bulletin of the Polish Academy of Sciences: Technical Sciences}, volume = 58, number = 3, pages = {347--358}, year = 2010, doi = {10.2478/v10175-010-0033-3} }
@article{BraGutRAu2006cms, author = {S. C. Brailsford and Gutjahr, Walter J. and M. S. Rauner and W. Zeppelzauer}, title = {Combined Discrete-event Simulation and Ant Colony Optimisation Approach for Selecting Optimal Screening Policies for Diabetic Retinopathy}, journal = {Computational Management Science}, year = 2006, volume = 4, number = 1, pages = {59--83} }
@article{BraKauSch2001aes, author = { J{\"u}rgen Branke and Kaussler, T. and Schmeck, H.}, title = {Guidance in evolutionary multi-objective optimization}, journal = {Advances in Engineering Software}, year = 2001, volume = 32, pages = {499--507} }
@article{BraNguPic2016tec, author = { J{\"u}rgen Branke and S. Nguyen and C. W. Pickardt and M. Zhang}, journal = {IEEE Transactions on Evolutionary Computation}, title = {Automated Design of Production Scheduling Heuristics: A Review}, year = 2016, volume = 20, number = 1, pages = {110--124} }
@article{BraSch2005faster, title = {Faster Convergence by Means of Fitness Estimation}, author = { J{\"u}rgen Branke and Schmidt, C.}, year = 2005, month = jan, journal = {Soft Computing}, volume = 9, number = 1, pages = {13--20}, issn = {1432-7643, 1433-7479}, doi = {10.1007/s00500-003-0329-4}, langid = {english} }
@article{BraZap2016:cor, author = {Roland Braune and G. Z{\"a}pfel}, title = {Shifting Bottleneck Scheduling for Total Weighted Tardiness Minimization---A Computational Evaluation of Subproblem and Re-optimization Heuristics}, journal = {Computers \& Operations Research}, year = 2016, volume = 66, pages = {130--140} }
@article{BranCorrGreSlow2016ejor, author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Roman S{\l}owi{\'n}ski and Zielniewicz, P.}, title = {Using {Choquet} integral as preference model in interactive evolutionary multiobjective optimization}, journal = {European Journal of Operational Research}, volume = 250, number = 3, pages = {884--901}, year = 2016, doi = {10.1016/j.ejor.2015.10.027} }
@article{BranFarSha2016cgti, author = { J{\"u}rgen Branke and Farid, S. S. and Shah, N.}, title = {Industry 4.0: a vision for personalized medicine supply chains?}, journal = {Cell and Gene Therapy Insights}, year = 2016, volume = 2, number = 2, pages = {263--270}, doi = {10.18609/cgti.2016.027} }
@article{BranGreSlow2015, author = { J{\"u}rgen Branke and Salvatore Greco and Roman S{\l}owi{\'n}ski and Piotr Zielniewicz}, title = {Learning Value Functions in Interactive Evolutionary Multiobjective Optimization}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2015, volume = 19, pages = {88--102}, number = 1 }
@article{BranJin2005tec, author = { Yaochu Jin and J{\"u}rgen Branke }, title = {Evolutionary Optimization in Uncertain Environments---A Survey}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2005, volume = 9, number = 5, pages = {303--317} }
@article{Breiman2001, author = {Leo Breiman}, title = {Random Forests}, journal = {Machine Learning}, year = 2001, volume = 45, number = 1, pages = {5--32}, doi = {10.1023/A:1010933404324} }
@article{BriCabEmm2018maximum, title = {Maximum volume subset selection for anchored boxes}, author = { Karl Bringmann and Cabello, Sergio and Emmerich, Michael T. M. }, journal = {Arxiv preprint arXiv:1803.00849}, year = 2018, doi = {10.48550/arXiv.1803.00849}, abstract = {Let $B$ be a set of $n$ axis-parallel boxes in $\mathbb{R}^d$ such that each box has a corner at the origin and the other corner in the positive quadrant of $\mathbb{R}^d$, 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 \emph{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 $d \ge 3$ is $\Omega\big(\binom{n}{k}\big)$. We show that: The problem is NP-hard already in 3 dimensions. In 3 dimensions, we break the bound $\Omega\big(\binom{n}{k}\big)$, by providing an $n^{O(\sqrt{k})}$ algorithm. For any constant dimension $d$, we present an efficient polynomial-time approximation scheme.}, keywords = {hypervolume subset selection} }
@article{BriFri2012tcs, author = { Karl Bringmann and Tobias Friedrich }, title = {Approximating the Least Hypervolume Contributor: {NP}-Hard in General, But Fast in Practice}, pages = {104--116}, year = 2012, volume = 425, journal = {Theoretical Computer Science}, doi = {10.1016/j.tcs.2010.09.026} }
@article{BriFri2010eff, author = { Karl Bringmann and Tobias Friedrich }, title = {An efficient algorithm for computing hypervolume contributions}, journal = {Evolutionary Computation}, volume = 18, number = 3, pages = {383--402}, year = 2010 }
@article{BriFri2014convergence, title = {Convergence of hypervolume-based archiving algorithms}, author = { Karl Bringmann and Tobias Friedrich }, journal = {IEEE Transactions on Evolutionary Computation}, year = 2014, number = 5, pages = {643--657}, volume = 18, publisher = {IEEE}, keywords = {competitive ratio}, doi = {10.1109/TEVC.2014.2341711}, annote = {Proof that all nondecreasing $(\mu + \lambda)$ archiving algorithms with $\lambda < \mu$ are ineffective.} }
@article{Bro1970bfgs, author = {Broyden, Charles G.}, title = {The Convergence of a Class of Double-rank Minimization Algorithms: 2. The New Algorithm}, journal = {IMA Journal of Applied Mathematics}, year = 1970, volume = 6, number = 3, pages = {222--231}, month = sep, annote = {One of the four papers that proposed BFGS.}, doi = {10.1093/imamat/6.3.222}, eprint = {https://academic.oup.com/imamat/article-pdf/6/3/222/1848059/6-3-222.pdf}, keywords = {BFGS} }
@article{BroBadThiZit2013directed, title = {Directed Multiobjective Optimization Based on the Weighted Hypervolume Indicator}, volume = 20, doi = {10.1002/mcda.1502}, abstract = {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.}, number = {5-6}, journal = {Journal of Multi-Criteria Decision Analysis}, author = { Dimo Brockhoff and Johannes Bader and Lothar Thiele and Eckart Zitzler }, year = 2013, keywords = {hypervolume, preference-based search, multi objective optimization, evolutionary algorithm}, pages = {291--317} }
@article{BroCorFre2010tutorial, author = {Brochu, Eric and Cora, Vlad and Nando de Freitas }, year = 2010, month = dec, title = {A Tutorial on {Bayesian} Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning}, journal = {Arxiv preprint arXiv:1012.2599}, url = {https://arxiv.org/abs/1012.2599} }
@article{BroTusTusWag2016biobj, author = { Dimo Brockhoff and Tea Tu{\v s}ar and Dejan Tu{\v s}ar and Tobias Wagner and Nikolaus Hansen and Anne Auger }, title = {Biobjective performance assessment with the {COCO} platform}, journal = {Arxiv preprint arXiv:1605.01746}, year = 2016, doi = {10.48550/arXiv.1605.01746} }
@article{BroWagTrau2015r2, title = {{R2} indicator-based multiobjective search}, author = { Dimo Brockhoff and Tobias Wagner and Heike Trautmann }, journal = {Evolutionary Computation}, year = 2015, number = 3, pages = {369--395}, volume = 23 }
@article{BroZit2009ec, author = { Dimo Brockhoff and Eckart Zitzler }, title = {Objective Reduction in Evolutionary Multiobjective Optimization: Theory and Applications}, journal = {Evolutionary Computation}, volume = 17, number = 2, pages = {135--166}, year = 2009, abstract = {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.}, doi = {10.1162/evco.2009.17.2.135} }
@article{Broyden1970bfgs, author = {Broyden, C. G.}, title = {The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations}, journal = {IMA Journal of Applied Mathematics}, year = 1970, volume = 6, number = 1, pages = {76--90}, month = mar, keywords = {Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm}, abstract = {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.}, doi = {10.1093/imamat/6.1.76} }
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@article{BruHurWer1997, author = {Peter Brucker and Johann Hurink and Frank Werner}, title = {Improving Local Search Heuristics for some Scheduling Problems --- {Part} {II}}, journal = {Discrete Applied Mathematics}, year = 1997, volume = 72, number = {1--2}, pages = {47--69} }
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@article{BurByk2017, title = {The Late Acceptance Hill-Climbing Heuristic}, author = { Edmund K. Burke and Yuri Bykov }, journal = {European Journal of Operational Research}, volume = 258, number = 1, pages = {70--78}, year = 2017 }
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@article{BurFraMos2004:joh, author = {Luciana Buriol and Paulo M. Fran{\c c}a and Pablo Moscato }, title = {A New Memetic Algorithm for the Asymmetric Traveling Salesman Problem}, journal = {Journal of Heuristics}, year = 2004, volume = 10, number = 5, pages = {483--506} }
@article{BurGenHyd2013, author = { Edmund K. Burke and Michel Gendreau and Matthew R. Hyde and Graham Kendall and Gabriela Ochoa and Ender {\"O}zcan and Rong Qu }, title = {Hyper-heuristics: A Survey of the State of the Art}, journal = {Journal of the Operational Research Society}, year = 2013, volume = 64, number = 12, pages = {1695--1724}, doi = {10.1057/jors.2013.71} }
@article{BurHydKen2010tec, author = { Edmund K. Burke and Matthew R. Hyde and Graham Kendall and John R. Woodward}, journal = {IEEE Transactions on Evolutionary Computation}, title = {A Genetic Programming Hyper-Heuristic Approach for Evolving {2-D} Strip Packing Heuristics}, year = 2010, volume = 14, number = 6, pages = {942--958}, doi = {10.1109/TEVC.2010.2041061} }
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@article{CaiLiFan2015archive, title = {An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization}, author = {Cai, Xinye and Li, Yexing and Fan, Zhun and Zhang, Qingfu }, journal = {IEEE Transactions on Evolutionary Computation}, year = 2015, number = 4, pages = {508--523}, volume = 19 }
@article{CaiXiaLiHu2021grid, title = {A grid-based inverted generational distance for multi/many-objective optimization}, author = {Cai, Xinye and Xiao, Yushun and Li, Miqing and Hu, Han and Ishibuchi, Hisao and Li, Xiaoping}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2021, number = 1, pages = {21--34}, volume = 25, publisher = {IEEE}, annote = {weakly Pareto-compliant indicator} }
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@article{CamDorStu2022exposing, author = {Camacho-Villal\'{o}n, Christian Leonardo and Marco Dorigo and Thomas St{\"u}tzle }, title = {Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors}, journal = {International Transactions in Operational Research}, doi = {10.1111/itor.13176}, year = 2022 }
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@article{CamStuDor2021psox, author = {Camacho-Villal\'{o}n, Christian Leonardo and Thomas St{\"u}tzle and Marco Dorigo }, title = {{PSO-X}: A Component-Based Framework for the Automatic Design of Particle Swarm Optimization Algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2021, volume = 26, number = 3, pages = {402--416}, doi = {10.1109/TEVC.2021.3102863} }
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@article{CebIruMen2014eda, author = { Josu Ceberio and Irurozki, Ekhine and Alexander Mendiburu and Jos{\'e} A. Lozano }, title = {A distance-based ranking model estimation of distribution algorithm for the flowshop scheduling problem}, abstract = {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.}, doi = {10.1109/TEVC.2013.2260548}, journal = {IEEE Transactions on Evolutionary Computation}, keywords = {Estimation of distribution algorithms,Generalized Mallows model,Permutation flowshop scheduling problem,Permutations-based optimization problems}, number = 2, pages = {286--300}, volume = 18, year = 2014 }
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@article{ChaMeaBea2000cor, author = {T.-J. Chang and N. Meade and John E. Beasley and Y. M. Sharaiha}, title = {Heuristics for cardinality constrained portfolio optimisation}, journal = {Computers \& Operations Research}, year = 2000, volume = 27, number = 13, pages = {1271--1302}, keywords = {Portfolio optimisation, CCMVPOP, Efficient frontier}, abstract = {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.} }
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@article{ChuSinHak2019surv, author = { Tinkle Chugh and Sindhya, Karthik and Hakanen, Jussi and Kaisa Miettinen }, title = {A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms}, journal = {Soft Computing}, pages = {3137--3166}, volume = 23, number = 9, year = 2019, abstract = {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.}, doi = {10.1007/s00500-017-2965-0} }
@article{CinFerLopAlb2022irace, author = { Christian Cintrano and Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique }, title = {Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs}, journal = {Evolutionary Computation}, year = 2023, volume = 31, number = 1, pages = {31--51}, doi = {10.1162/evco_a_00314}, abstract = {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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@article{DebDeb2014, author = { Kalyanmoy Deb and Debayan Deb}, title = {Analysing mutation schemes for real-parameter genetic algorithms}, journal = {International Journal of Artificial Intelligence and Soft Computing}, year = 2014, volume = 4, number = 1, pages = {1--28}, annote = {Proposed Gaussian mutation} }
@article{DebGupDauBran2009reliab, author = { Kalyanmoy Deb and S. Gupta and D. Daum and J{\"u}rgen Branke and A. Mall and D. Padmanabhan}, title = {Reliability-based optimization using evolutionary algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, volume = 13, number = 5, pages = {1054--1074}, month = oct, year = 2009, doi = {10.1109/TEVC.2009.2014361} }
@article{DebJain2014:nsga3-part1, author = { Kalyanmoy Deb and Himanshu Jain }, journal = {IEEE Transactions on Evolutionary Computation}, title = {An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part {I}: Solving Problems With Box Constraints}, year = 2014, volume = 18, number = 4, pages = {577--601}, annote = {Proposed NSGA-III} }
@article{DebKok2010tec-ged, author = { Kalyanmoy Deb and Murat K{\"o}ksalan }, title = {Guest Editorial: Special Issue on Preference-based Multiobjective Evolutionary Algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, volume = 14, number = 5, month = oct, year = 2010, pages = {669--670}, doi = {10.1109/TEVC.2010.2070371} }
@article{DebMohMis2005epsilon, title = {Evaluating the {$\epsilon$}-domination based multi-objective evolutionary algorithm for a quick computation of {Pareto}-optimal solutions}, author = { Kalyanmoy Deb and Mohan, Manikanth and Mishra, Shikhar}, journal = {Evolutionary Computation}, year = 2005, month = dec, number = 4, pages = {501--525}, volume = 13, doi = {10.1162/106365605774666895}, keywords = {$\epsilon$-dominance, $\epsilon$-MOEA} }
@article{DebTiw2008omni, author = { Kalyanmoy Deb and Santosh Tiwari }, title = {Omni-optimizer: {A} generic evolutionary algorithm for single and multi-objective optimization}, journal = {European Journal of Operational Research}, year = 2008, volume = 185, number = 3, pages = {1062--1087}, annote = {Archiving method with epsilon dominance and density in the decision and objective spaces}, keywords = {epsilon-dominance, archiving}, doi = {10.1016/j.ejor.2006.06.042} }
@article{DebZhuKul2018tec, author = { Kalyanmoy Deb and Zhu, Ling and Kulkarni, Sandeep}, title = {Handling Multiple Scenarios in Evolutionary Multi-Objective Numerical Optimization}, doi = {10.1109/TEVC.2017.2776921}, abstract = {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.}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2018, volume = 22, number = 6, pages = {920--933}, keywords = {scenario-based} }
@article{DecSor2012ejor, author = {Annelies De Corte and Kenneth S{\"o}rensen }, title = {Optimisation of gravity-fed water distribution network design: A critical review}, journal = {European Journal of Operational Research}, volume = 228, number = 1, pages = {1--10}, doi = {10.1016/j.ejor.2012.11.046}, year = 2013 }
@article{DecSor2016, author = {Annelies De Corte and Kenneth S{\"o}rensen }, title = {An Iterated Local Search Algorithm for Water Distribution Network Design Optimization}, journal = {Networks}, year = 2016, volume = 67, number = 3, pages = {187--198} }
@article{DecSor2016water, author = {Annelies De Corte and Kenneth S{\"o}rensen }, title = {An Iterated Local Search Algorithm for multi-period water distribution network design optimization}, journal = {Water}, volume = 8, number = 8, pages = 359, doi = {10.3390/w8080359}, year = 2016 }
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@article{DelGarGro2012:cor, author = { Federico {Della Croce} and Thierry Garaix and Andrea Grosso }, title = {Iterated Local Search and Very Large Neighborhoods for the Parallel-machines Total Tardiness Problem}, journal = {Computers \& Operations Research}, year = 2012, volume = 39, number = 6, pages = {1213--1217} }
@article{DelIorMar2016binpack, title = {Bin packing and cutting stock problems: Mathematical models and exact algorithms}, author = {Delorme, Maxence and Manuel Iori and Silvano Martello }, journal = {European Journal of Operational Research}, volume = 255, number = 1, pages = {1--20}, year = 2016, publisher = {Elsevier}, doi = {10.1016/j.ejor.2016.04.030} }
@article{DelIorMarMon2016, author = {Mauro Dell'Amico and Manuel Iori and Silvano Martello and Monaci, Michele }, title = {Heuristic and Exact Algorithms for the Identical Parallel Machine Scheduling Problem}, journal = {INFORMS Journal on Computing}, year = 2016, volume = 20, number = 3, pages = {333--344} }
@article{DelIorMarc2018bpplib, title = {{BPPLIB}: a library for bin packing and cutting stock problems}, author = {Delorme, Maxence and Manuel Iori and Silvano Martello }, journal = {Optimization Letters}, volume = 12, number = 2, pages = {235--250}, year = 2018, doi = {10.1007/s11590-017-1192-z} }
@article{DelIorNovStu2016, author = {Mauro Dell'Amico and Manuel Iori and Stefano Novellani and Thomas St{\"u}tzle }, title = {A destroy and repair algorithm for the Bike sharing Rebalancing Problem}, journal = {Computers \& Operations Research}, volume = 71, pages = {146--162}, year = 2016, doi = {10.1016/j.cor.2016.01.011}, keywords = {irace} }
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@article{DenZha2019approxhv, author = {Deng, Jingda and Zhang, Qingfu }, title = {Approximating Hypervolume and Hypervolume Contributions Using Polar Coordinate}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2019, volume = 23, number = 5, pages = {913--918}, month = oct, annote = {Proposed approximating the hypervolume using scalarizations}, doi = {10.1109/tevc.2019.2895108} }
@article{DerGarMolHer2011stats, title = {A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms}, author = {Derrac, Joaqu{\'i}n and Garc{\'i}a, Salvador and Daniel Molina and Francisco Herrera }, journal = {Swarm and Evolutionary Computation}, volume = 1, number = 1, pages = {3--18}, year = 2011 }
@article{DerVog2014:joh, author = {Ulrich Derigs and Ulrich Vogel}, title = {Experience with a Framework for Developing Heuristics for Solving Rich Vehicle Routing Problems}, journal = {Journal of Heuristics}, year = 2014, volume = 20, number = 1, pages = {75--106} }
@article{DesBelDop2021bops, author = {Aryan Deshwal and Syrine Belakaria and Janardhan Rao Doppa and Dae Hyun Kim}, title = {Bayesian Optimization over Permutation Spaces}, journal = {Arxiv preprint arXiv:2112.01049}, year = 2021, doi = {10.48550/arXiv.2112.01049}, keywords = {BOPS, CEGO} }
@article{DesRitLopPer2021acviz, author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie }, title = {{\softwarepackage{ACVIZ}}: A Tool for the Visual Analysis of the Configuration of Algorithms with {\rpackage{irace}}}, journal = {Operations Research Perspectives}, year = 2021, doi = {10.1016/j.orp.2021.100186}, supplement = {https://zenodo.org/record/4714582}, abstract = {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.}, volume = 8, pages = 100186 }
@article{DetPapZab2017omega, title = {A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare}, author = {Paolo Detti and Francesco Papalini and Garazi Zabalo {Manrique de Lara}}, journal = {Omega}, volume = 70, pages = {1--14}, year = 2017, doi = {10.1016/j.omega.2016.08.008} }
@article{DevVoh2003informs, title = {Combinatorial Auctions: A Survey}, author = { Sven {De Vries} and Rakesh V. Vohra }, journal = {INFORMS Journal on Computing}, volume = 15, number = 3, pages = {284--309}, year = 2003, publisher = {{INFORMS}} }
@article{DiaHanXu2017, title = {Evolutionary robust optimization in production planning: interactions between number of objectives, sample size and choice of robustness measure}, journal = {Computers \& Operations Research}, volume = 79, pages = {266--278}, year = 2017, doi = {10.1016/j.cor.2016.06.020}, author = { Juan Esteban Diaz and Julia Handl and Dong-Ling Xu }, keywords = {Evolutionary multi-objective optimization, Production planning, Robust optimization, Simulation-based optimization, Uncertainty modelling} }
@article{DiaHanXu2018, title = {Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system}, journal = {European Journal of Operational Research}, volume = 266, number = 3, pages = {976--989}, year = 2018, issn = {0377-2217}, doi = {10.1016/j.ejor.2017.10.062}, author = { Juan Esteban Diaz and Julia Handl and Dong-Ling Xu }, keywords = {Genetic algorithms, Combinatorial optimization, Production planning, Simulation-based optimization, Uncertainty modelling} }
@article{DiaLop2020ejor, author = { Juan Esteban Diaz and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Incorporating Decision-Maker's Preferences into the Automatic Configuration of Bi-Objective Optimisation Algorithms}, journal = {European Journal of Operational Research}, year = 2021, volume = 289, number = 3, pages = {1209--1222}, doi = {10.1016/j.ejor.2020.07.059}, abstract = {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.}, supplement = {https://doi.org/10.5281/zenodo.3749288} }
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@article{DoeDoeEbe2015, author = { Benjamin Doerr and Carola Doerr and Franziska Ebel}, title = {From black-box complexity to designing new genetic algorithms}, journal = {Theoretical Computer Science}, volume = 567, pages = {87--104}, year = 2015, doi = {10.1016/j.tcs.2014.11.028} }
@article{DoeDoeYan2020, author = { Benjamin Doerr and Carola Doerr and Yang, Jing}, title = {Optimal parameter choices via precise black-box analysis}, journal = {Theoretical Computer Science}, volume = 801, pages = {1--34}, year = 2020, doi = {10.1016/j.tcs.2019.06.014} }
@article{DoeFueGro06, author = { Karl F. Doerner and Guenther Fuellerer and Manfred Gronalt and Richard F. Hartl and Manuel Iori }, title = {Metaheuristics for the Vehicle Routing Problem with Loading Constraints}, journal = {Networks}, year = 2006, volume = 49, number = 4, pages = {294--307} }
@article{DoeGieWitYan2019, title = {The ({1+\(\lambda\)}) evolutionary algorithm with self-adjusting mutation rate}, author = { Benjamin Doerr and Gie{\ss}en, Christian and Carsten Witt and Yang, Jing}, journal = {Algorithmica}, volume = 81, number = 2, pages = {593--631}, year = 2019, publisher = {Springer} }
@article{DoeGutHar08, author = { Karl F. Doerner and Gutjahr, Walter J. and Richard F. Hartl and Christine Strauss and Christian Stummer }, title = {Nature-Inspired Metaheuristics in Multiobjective Activity Crashing}, journal = {Omega}, year = 2008, volume = 36, number = 6, pages = {1019--1037} }
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@article{DoeGutHarStrStu06:ejor, author = { Karl F. Doerner and Gutjahr, Walter J. and Richard F. Hartl and Christine Strauss and Christian Stummer }, title = {{Pareto} ant colony optimization with ILP preprocessing in multiobjective project portfolio selection}, journal = {European Journal of Operational Research}, year = 2006, volume = 171, pages = {830--841} }
@article{DoeHarRei03, author = { Karl F. Doerner and Richard F. Hartl and Marc Reimann }, title = {Are {COMPETants} more competent for problem solving? {The} case of a multiple objective transportation problem}, journal = {Central European Journal for Operations Research and Economics}, pages = {115--141}, volume = 11, number = 2, year = 2003 }
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@article{DoeMerStu2009:swarm, author = { Karl F. Doerner and D. Merkle and Thomas St{\"u}tzle }, title = {Special issue on Ant Colony Optimization}, journal = {Swarm Intelligence}, year = 2009, volume = 3, number = 1 }
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@article{Dog2015asoco, author = { Do\v{g}an Ayd{\i}n }, title = {Composite artificial bee colony algorithms: From component-based analysis to high-performing algorithms}, journal = {Applied Soft Computing}, volume = 32, pages = {266--285}, year = 2015, doi = {10.1016/j.asoc.2015.03.051}, keywords = {irace} }
@article{DoiPekReg2004rank, author = {Jean-Paul Doignon and Aleksandar Peke{\v{c}} and Michel Regenwetter}, title = {The repeated insertion model for rankings: Missing link between two subset choice models}, doi = {10.1007/bf02295838}, year = 2004, month = mar, volume = 69, number = 1, pages = {33--54}, journal = {Psychometrika}, abstract = {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 $\varphi$-model as a special case. We explore its basic properties as well as its relationship to Fligner and Verducci's multistage ranking model.} }
@article{DolMor2002benchmarking, author = {Dolan, Elizabeth D. and Mor{\'e}, Jorge J.}, journal = {Mathematical Programming}, number = 2, pages = {201--213}, title = {Benchmarking optimization software with performance profiles}, volume = 91, year = 2002, keywords = {performance profiles; convergence}, annote = {This methodology has been criticized in \url{https://doi.org/10.1145/2950048}} }
@article{DonCheHua2013, author = {Xingye Dong and Ping and Houkuan Huang and Maciek Nowak}, title = {A Multi-restart Iterated Local Search Algorithm for the Permutation Flow Shop Problem Minimizing Total Flow Time}, journal = {Computers \& Operations Research}, year = 2013, volume = 40, number = 2, pages = {627--632} }
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@article{Dor2007:scholarpedia, author = { Marco Dorigo }, title = {Ant {Colony} {Optimization}}, year = 2007, journal = {Scholarpedia}, volume = 2, number = 3, pages = 1461, doi = {10.4249/scholarpedia.1461} }
@article{Dor2016sipolicy, author = { Marco Dorigo }, title = {Swarm intelligence: A few things you need to know if you want to publish in this journal}, journal = {Swarm Intelligence}, year = 2016, month = nov, url = {https://static.springer.com/sgw/documents/1593723/application/pdf/Additional_submission_instructions.pdf} }
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@article{DorBlu2005:tcs, author = { Marco Dorigo and Christian Blum }, title = {Ant colony optimization theory: A survey}, journal = {Theoretical Computer Science}, volume = 344, number = {2-3}, year = 2005, pages = {243--278} }
@article{DorDicGam99:al, author = { Marco Dorigo and Gianni A. {Di Caro} and L. M. Gambardella }, title = {Ant Algorithms for Discrete Optimization}, journal = {Artificial Life}, volume = 5, number = 2, pages = {137--172}, year = 1999 }
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@article{DorGamMidStu2002:tec, doi = {10.1109/TEVC.2002.802446}, author = { Marco Dorigo and L. M. Gambardella and Martin Middendorf and Thomas St{\"u}tzle }, title = {Guest Editorial: Special Section on Ant Colony Optimization}, year = 2002, journal = {IEEE Transactions on Evolutionary Computation}, volume = 6, number = 4, pages = {317--320}, keywords = {ant colony optimization, swarm intelligence} }
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@article{DorStuDic2000:fgcs, author = { Marco Dorigo and Thomas St{\"u}tzle and Gianni A. {Di Caro} }, title = {Special Issue on ``{Ant} {Algorithms}''}, year = 2000, journal = {Future Generation Computer Systems}, volume = 16, number = 8, keywords = {swarm intelligence, ant colony optimization} }
@article{DouZop2010:ejor, author = { Michael Doumpos and Constantin Zopounidis }, title = {Preference disaggregation and statistical learning for multicriteria decision support: A review}, journal = {European Journal of Operational Research}, volume = 209, number = 3, pages = {203--214}, year = 2011 }
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@article{DubLopStu2011amai, author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Improving the Anytime Behavior of Two-Phase Local Search}, journal = {Annals of Mathematics and Artificial Intelligence}, year = 2011, volume = 61, number = 2, pages = {125--154}, doi = {10.1007/s10472-011-9235-0} }
@article{DubLopStu2011cor, author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {A Hybrid {TP$+$PLS} Algorithm for Bi-objective Flow-Shop Scheduling Problems}, journal = {Computers \& Operations Research}, year = 2011, volume = 38, number = 8, pages = {1219--1236}, doi = {10.1016/j.cor.2010.10.008}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2010-001/} }
@article{DubLopStu2015ejor, author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Anytime {Pareto} Local Search}, journal = {European Journal of Operational Research}, year = 2015, volume = 243, number = 2, pages = {369--385}, doi = {10.1016/j.ejor.2014.10.062}, keywords = {Pareto local search} }
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@article{FarMarYan2015pltoolbox, author = {Farrugia, Vincent E. and Mart{\'i}nez, H{\'e}ctor P. and Yannakakis, Georgios N.}, title = {The Preference Learning Toolbox}, journal = {Arxiv preprint arXiv:1506.01709}, year = 2015, doi = {10.48550/arXiv.1506.01709} }
@article{FarWalSav2006hydroinf, author = {R. Farmani and Godfrey A. Walters and Dragan A. Savic }, title = {Evolutionary multi-objective optimization of the design and operation of water distribution network: total cost vs. reliability vs. water quality}, journal = { Journal of Hydroinformatics }, year = 2006, volume = 8, number = 3, pages = {165--179} }
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@article{FerFra2014, title = {On Insertion Tie-breaking Rules in Heuristics for the Permutation Flowshop Scheduling Problem}, author = { Victor Fernandez-Viagas and Jose M. Frami{\~n}{\'a}n }, journal = {Computers \& Operations Research}, year = 2014, pages = {60--67}, volume = 45 }
@article{FerFra2017, author = { Victor Fernandez-Viagas and Jose M. Frami{\~n}{\'a}n }, title = {A Beam-search-based Constructive Heuristic for the {PFSP} to Minimise Total Flowtime}, journal = {Computers \& Operations Research}, year = 2017, volume = 81, pages = {167--177} }
@article{FerFra2018, author = { Victor Fernandez-Viagas and Jose M. Frami{\~n}{\'a}n }, title = {Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimise total tardiness}, journal = {Expert Systems with Applications}, year = 2018, volume = 94, pages = {58--69} }
@article{FerGarAl2016mpe, author = { Javier Ferrer and Jos{\'e} Garc{\'i}a-Nieto and Alba, Enrique and Chicano, Francisco }, doi = {10.1155/2016/3871046}, journal = {Mathematical Problems in Engineering}, pages = {1--19}, title = {Intelligent Testing of Traffic Light Programs: Validation in Smart Mobility Scenarios}, volume = 2016, year = 2016 }
@article{FerGuiRamJua2016, author = {Alberto Ferrer and Daniel Guimarans and Helena {Ramalhinho Louren{\c c}o} and Angel A. Juan}, title = {A {BRILS} Metaheuristic for Non-smooth Flow-shop Problems with Failure-risk Costs}, journal = {Expert Systems with Applications}, year = 2016, volume = 44, pages = {177--186} }
@article{FerLopAlb2019asoc, author = { Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique }, title = {Reliable Simulation-Optimization of Traffic Lights in a Real-World City}, journal = {Applied Soft Computing}, year = 2019, volume = 78, pages = {697--711}, doi = {10.1016/j.asoc.2019.03.016}, supplement = {https://github.com/MLopez-Ibanez/irace-sumo} }
@article{FerNavBer2009:ejor, author = { Eduardo Fernandez and Jorge Navarro and Sergio Bernal }, title = {Multicriteria Sorting Using a Valued Indifference Relation Under a Preference Disaggregation Paradigm}, journal = {European Journal of Operational Research}, volume = 198, number = 2, pages = {602--609}, year = 2009 }
@article{FerRuiFra2016, author = { Victor Fernandez-Viagas and Rub{\'e}n Ruiz and Jose M. Frami{\~n}{\'a}n }, title = {A New Vision of Approximate Methods for the Permutation Flowshop to Minimise Makespan: State-of-the-art and Computational Evaluation}, journal = {European Journal of Operational Research}, volume = 257, number = 3, pages = {707--721}, year = 2017 }
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@article{FerValFra2018, author = { Victor Fernandez-Viagas and Jorge M. S. Valente and Jose M. Frami{\~n}{\'a}n }, title = {Iterated-greedy-based algorithms with Beam Search Initialization for the Permutation Flowshop to Minimise Total Tardiness}, journal = {Expert Systems with Applications}, volume = 94, pages = {58--69}, year = 2018 }
@article{FiaDaCSchSeb2010:amai, author = { {\'A}lvaro Fialho and Luis {Da Costa} and Marc Schoenauer and Mich{\`e}le Sebag }, title = {Analyzing Bandit-based Adaptive Operator Selection Mechanisms}, journal = {Annals of Mathematics and Artificial Intelligence}, year = 2010, volume = 60, number = {1--2}, pages = {25--64} }
@article{Fie2000:siamo, author = { Mark J. Fielding }, title = {Simulated Annealing with an Optimal Fixed Temperature}, journal = {SIAM Journal on Optimization}, year = 2000, volume = 11, number = 2, pages = {289--307} }
@article{FieEveSing2003tec, title = {Using unconstrained elite archives for multiobjective optimization}, author = { Jonathan E. Fieldsend and Everson, Richard M. and Singh, Sameer}, journal = {IEEE Transactions on Evolutionary Computation}, volume = 7, number = 3, pages = {305--323}, year = 2003, doi = {10.1109/TEVC.2003.810733} }
@article{FigFonHalKla2017easy, title = {Easy to say they are Hard, but Hard to see they are Easy-Towards a Categorization of Tractable Multiobjective Combinatorial Optimization Problems}, author = { Jos{\'e} Rui Figueira and Carlos M. Fonseca and Halffmann, Pascal and Kathrin Klamroth and Lu{\'i}s Paquete and Ruzika, Stefan and Schulze, Britta and Stiglmayr, Michael and Willems, David}, journal = {Journal of Multi-Criteria Decision Analysis}, volume = 24, number = {1-2}, pages = {82--98}, year = 2017, publisher = {Wiley Online Library}, doi = {10.1002/mcda.1574} }
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@article{FisGloLod2005, title = {The feasibility pump}, author = { Matteo Fischetti and Fred Glover and Andrea Lodi }, journal = {Mathematical Programming}, volume = 104, number = 1, pages = {91--104}, year = 2005, publisher = {Springer} }
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@article{FisMon2014joh, title = {Proximity search for 0-1 mixed-integer convex programming}, author = { Matteo Fischetti and Monaci, Michele }, journal = {Journal of Heuristics}, volume = 20, number = 6, pages = {709--731}, year = 2014, publisher = {Springer} }
@article{FisMon2014or, author = { Matteo Fischetti and Monaci, Michele }, title = {Exploiting Erraticism in Search}, journal = {Operations Research}, volume = 62, number = 1, pages = {114--122}, year = 2014, doi = {10.1287/opre.2013.1231}, annote = {\url{http://mat.tepper.cmu.edu/blog/?p=1695}}, abstract = { 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. } }
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@article{FocLodMil02tsptw, author = { Filippo Focacci and Andrea Lodi and Michela Milano }, title = {A Hybrid Exact Algorithm for the {TSPTW}}, journal = {INFORMS Journal on Computing}, year = 2002, volume = 14, pages = {403--417} }
@article{FonFle1995ec, title = {An overview of evolutionary algorithms in multiobjective optimization}, author = { Carlos M. Fonseca and Peter J. Fleming }, journal = {Evolutionary Computation}, year = 1995, number = 1, pages = {1--16}, volume = 3, annote = {Proposed FON benchmark problem} }
@article{FonFle1998:tsmca, author = { Carlos M. Fonseca and Peter J. Fleming }, title = {Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms ({II}): {Application} Example}, journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part A}, year = 1998, volume = 28, number = 1, pages = {38--44}, month = jan, doi = {10.1109/3468.650320} }
@article{FonFle1998:tsmca1, author = { Carlos M. Fonseca and Peter J. Fleming }, title = {Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms ({I}): {A} Unified Formulation}, journal = {IEEE Transactions on Systems, Man, and Cybernetics -- Part A}, year = 1998, volume = 28, number = 1, pages = {26--37}, month = jan, doi = {10.1109/3468.650319} }
@article{ForKea2009surrogate, author = {Forrester, Alexander I. J. and Keane, Andy J.}, title = {Recent advances in surrogate-based optimization}, journal = {Progress in Aerospace Sciences}, volume = 45, number = {1-3}, pages = {50--79}, doi = {10.1016/j.paerosci.2008.11.001}, year = 2009, keywords = {Kriging; Gaussian Process; EGO; Design of Experiments} }
@article{FowGelKok2010ejor, title = {Interactive evolutionary multi-objective optimization for quasi-concave preference functions}, journal = {European Journal of Operational Research}, volume = 206, number = 2, pages = {417--425}, year = 2010, doi = {10.1016/j.ejor.2010.02.027}, author = {John W. Fowler and Esma S. Gel and Murat K{\"o}ksalan and Pekka Korhonen and Jon L. Marquis and Wallenius, Jyrki }, keywords = {Interactive optimization, Multi-objective optimization, Evolutionary optimization, Knapsack problem}, abstract = {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.} }
@article{Fox1993integrating, author = { Bennett L. Fox }, title = {Integrating and accelerating tabu search, simulated annealing, and genetic algorithms}, journal = {Annals of Operations Research}, volume = 41, number = 2, pages = {47--67}, year = 1993, publisher = {Springer} }
@article{Fra2018tutorial, author = {Peter I. Frazier}, title = {A Tutorial on {Bayesian} Optimization}, journal = {Arxiv preprint arXiv:1807.02811}, year = 2018, doi = {10.48550/arXiv.1807.02811} }
@article{Fra2022:4or, title = {Empirical Analysis of Stochastic Local Search Behaviour: Connecting Structure, Components and Landscape}, author = { Alberto Franzin }, journal = {{4OR}: A Quarterly Journal of Operations Research}, year = 2022, doi = {10.1007/s10288-022-00511-7} }
@article{FraBraBru2014automode, author = {G. Francesca and M. Brambilla and A. Brutschy and Vito Trianni and Mauro Birattari }, title = {{AutoMoDe}: A Novel Approach to the Automatic Design of Control Software for Robot Swarms}, journal = {Swarm Intelligence}, year = 2014, volume = 8, number = 2, pages = {89--112}, doi = {10.1007/s11721-014-0092-4} }
@article{FraBraBru2015automode, author = {Francesca, Gianpiero and Brambilla, Manuele and Brutschy, Arne and Garattoni, Lorenzo and Miletitch, Roman and Podevijn, Gaetan and Reina, Andreagiovanni and Soleymani, Touraj and Salvaro, Mattia and Pinciroli, Carlo and Mascia, Franco and Vito Trianni and Mauro Birattari }, title = {{AutoMoDe-Chocolate}: Automatic Design of Control Software for Robot Swarms}, year = 2015, journal = {Swarm Intelligence}, doi = {10.1007/s11721-015-0107-9}, keywords = {Swarm robotics; Automatic design; AutoMoDe}, language = {English} }
@article{FraGupLei2004, title = {A Review and Classification of Heuristics for Permutation Flow-shop Scheduling with Makespan Objective}, author = { Jose M. Frami{\~n}{\'a}n and Jatinder N. D. Gupta and Rainer Leisten }, journal = {Journal of the Operational Research Society}, year = 2004, number = 12, pages = {1243--1255}, volume = 55 }
@article{FraPerStu2018ol, author = { Alberto Franzin and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle }, title = {Effect of Transformations of Numerical Parameters in Automatic Algorithm Configuration}, journal = {Optimization Letters}, year = 2018, volume = 12, number = 8, pages = {1741--1753}, doi = {10.1007/s11590-018-1240-3} }
@article{FraSamDiC2016, author = { Alberto Franzin and Sambo, Francesco and Di Camillo, Barbara}, title = {\rpackage{bnstruct}: an {R} package for {Bayesian} Network structure learning in the presence of missing data}, journal = {Bioinformatics}, year = 2016, volume = 33, number = 8, pages = {1250--1252} }
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@article{Fursin2011milepost, author = {Grigori Fursin and Yuriy Kashnikov and Abdul Wahid Memon and Zbigniew Chamski and Olivier Temam and Mircea Namolaru and Elad Yom-Tov and Bilha Mendelson and Ayal Zaks and Eric Courtois and Francois Bodin and Phil Barnard and Elton Ashton and Edwin Bonilla and John Thomson and Christopher K. I. Williams and Michael O'Boyle}, title = {Milepost {GCC}: Machine Learning Enabled Self-tuning Compiler}, journal = {International Journal of Parallel Programming}, year = 2011, volume = 39, number = 3, pages = {296--327}, publisher = {Springer, US}, doi = {10.1007/s10766-010-0161-2} }
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@article{GamDor00:informs, author = { L. M. Gambardella and Marco Dorigo }, title = {Ant {Colony} {System} Hybridized with a New Local Search for the Sequential Ordering Problem}, volume = 12, number = 3, pages = {237--255}, journal = {INFORMS Journal on Computing}, year = 2000, anote = {IJ.26} }
@article{GamMonWey12:ejor, author = { L. M. Gambardella and Roberto Montemanni and Dennis Weyland }, title = {Coupling Ant Colony Systems with Strong Local Searches}, journal = {European Journal of Operational Research}, volume = 220, number = 3, year = 2012, pages = {831--843}, doi = {10.1016/j.ejor.2012.02.038} }
@article{GanJasFre2000joh, author = { Xavier Gandibleux and Andrzej Jaszkiewicz and A. Fr{\'e}ville and Roman S{\l}owi{\'n}ski }, title = {Special Issue on {Multiple} {Objective} {Metaheuristics}}, journal = {Journal of Heuristics}, year = 2000, volume = 6, number = 3 }
@article{Gao2016, author = {Gao, Kaizhou and Zhang, Yicheng and Sadollah, Ali and Su, Rong}, doi = {10.1016/j.asoc.2016.07.029}, journal = {Applied Soft Computing}, keywords = {harmony search algorithm,traffic light scheduling}, month = nov, pages = {359--372}, title = {Optimizing urban traffic light scheduling problem using harmony search with ensemble of local search}, volume = 48, year = 2016 }
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@article{GonZhaChi2018kbs, title = {The optimization ordering model for intuitionistic fuzzy preference relations with utility functions}, journal = {Knowledge-Based Systems}, volume = 162, pages = {174--184}, year = 2018, annote = {Special Issue on intelligent decision-making and consensus under uncertainty in inconsistent and dynamic environments}, issn = {0950-7051}, doi = {10.1016/j.knosys.2018.07.012}, author = {Zaiwu Gong and Ning Zhang and Francisco Chiclana}, keywords = {Intuitionistic fuzzy preference relation, Utility function, Ranking, Multiplicative consistency, Non-archimedean infinitesimal}, abstract = {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.} }
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@article{GraJuaLou2016, author = {Alex Grasas and Angel A. Juan and Helena {Ramalhinho Louren{\c c}o} }, title = {{SimILS}: A Simulation-based Extension of the Iterated Local Search Metaheuristic for Stochastic Combinatorial Optimization}, journal = {Journal of Simulation}, year = 2016, volume = 10, number = 1, pages = {69--77} }
@article{GraPriGag02, author = {M. Gravel and W. L. Price and Caroline Gagn{\'e} }, title = {Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic}, journal = {European Journal of Operational Research}, year = 2002, volume = 143, number = 1, pages = {218--229}, doi = {10.1016/S0377-2217(01)00329-0} }
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@article{GreKadMouSlo2011:ejor, author = { Salvatore Greco and Kadzi{\'n}ski, Mi{\l}osz and Vincent Mousseau and Roman S{\l}owi{\'n}ski }, title = {{ELECTRE}$^\mathrm{{GKMS}}$: Robust ordinal regression for outranking methods}, journal = {European Journal of Operational Research}, volume = 214, number = 1, pages = {118--135}, year = 2011 }
@article{GreMouSlo2014ejor, author = { Salvatore Greco and Vincent Mousseau and Roman S{\l}owi{\'n}ski }, title = {Robust ordinal regression for value functions handling interacting criteria}, journal = {European Journal of Operational Research}, volume = 239, number = 3, pages = {711--730}, year = 2014, doi = {10.1016/j.ejor.2014.05.022} }
@article{GriBauIoa2018modeling, title = {Modelling science trustworthiness under publish or perish pressure}, author = {David R. Grimes and Chris T. Bauch and John P. A. Ioannidis }, journal = {Royal Society Open Science}, volume = 5, pages = {171511}, year = 2018 }
@article{GroDelTad2004, author = { Andrea Grosso and Federico {Della Croce} and R. Tadei}, title = {An Enhanced Dynasearch Neighborhood for the Single-Machine Total Weighted Tardiness Scheduling Problem}, journal = {Operations Research Letters}, year = 2004, volume = 32, number = 1, pages = {68--72} }
@article{GroJamLoc2009, author = { Andrea Grosso and A. R. M. J. U. Jamali and Marco Locatelli}, title = {Finding Maximin Latin Hypercube Designs by Iterated Local Search Heuristics}, journal = {European Journal of Operational Research}, year = 2009, volume = 197, number = 2, pages = {541--547} }
@article{GroKayKnoVan2013, title = {The "big data" revolution in healthcare}, author = {Groves, Peter and Kayyali, Basel and Knott, David and Van Kuiken, Steve}, journal = {McKinsey Quarterly}, volume = 2, year = 2013 }
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@article{Gui2011objred, author = {Gonzalo Guill{\'e}n-Gos{\'a}lbez}, title = {A novel {MILP}-based objective reduction method for multi-objective optimization: Application to environmental problems}, journal = {Computers \& Chemical Engineering}, volume = 35, number = 8, pages = {1469--1477}, year = 2011, issn = {0098-1354}, doi = {10.1016/j.compchemeng.2011.02.001}, keywords = {Environmental engineering, Life cycle assessment, Multi-objective optimization, Objective reduction}, abstract = {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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@article{JooLeyDec2022knapsack, author = {Jorik Jooken and Pieter Leyman and Patrick {De Causmaecker}}, title = {A new class of hard problem instances for the 0--1 knapsack problem}, journal = {European Journal of Operational Research}, year = 2022, volume = 301, number = 3, pages = {841--854} }
@article{JooLeyWau2023exploring, author = {Jorik Jooken and Pieter Leyman and Tony Wauters and Patrick {De Causmaecker}}, title = {Exploring search space trees using an adapted version of {Monte} {Carlo} tree search for combinatorial optimization problems}, journal = {Computers \& Operations Research}, year = 2023, volume = 150, pages = 106070, doi = {10.1016/j.cor.2022.106070} }
@article{JosCle1999:jair, author = {D. E. Joslin and D. P. Clements}, title = {Squeaky Wheel Optimization}, journal = {Journal of Artificial Intelligence Research}, year = 1999, volume = 10, pages = {353--373} }
@article{Jowitt92, author = { P. W. Jowitt and G. Germanopoulos }, title = {Optimal pump scheduling in water supply networks}, journal = {Journal of Water Resources Planning and Management, {ASCE}}, year = 1992, volume = 118, number = 4, pages = {406--422}, note = {}, abstract = {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.} }
@article{JuaFauGras2015orp, author = {Angel A. Juan and Javier Faulin and Scott E. Grasman and Markus Rabe and Gon{\c c}alo Figueira}, title = {A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems}, journal = {Operations Research Perspectives}, volume = 2, pages = {62--72}, year = 2015, doi = {10.1016/j.orp.2015.03.001}, keywords = {Metaheuristics; Simulation; Combinatorial optimization; Stochastic problems} }
@article{JuaLouMatLuoCas2014, author = {Angel A. Juan and Helena R. {Louren{\c c}o} and Manuel Mateo and Rachel Luo and Quim Castell{\`{a}}}, title = {Using Iterated Local Search for Solving the Flow-shop Problem: Parallelization, Parametrization, and Randomization Issues}, journal = {International Transactions in Operational Research}, year = 2014, volume = 21, number = 1, pages = {103--126} }
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@article{KabColKorLop2017jacryst, author = { Kabova, Elena A. and Cole, Jason C. and Oliver Korb and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Williams, Adrian C. and Shankland, Kenneth }, title = {Improved performance of crystal structure solution from powder diffraction data through parameter tuning of a simulated annealing algorithm}, journal = {Journal of Applied Crystallography}, year = 2017, volume = 50, number = 5, pages = {1411--1420}, month = oct, doi = {10.1107/S1600576717012602}, abstract = {Significant gains in the performance of the simulated annealing algorithm in the {\it DASH} software package have been realized by using the {\it 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 $\chi^2$ 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} }
@article{KahTve1979prospect, title = {Prospect theory: {An} analysis of decision under risk}, author = { Kahneman, Daniel and Tversky, Amos }, journal = {Econometrica}, pages = {263--291}, volume = 47, number = 2, year = 1979, doi = {10.2307/1914185} }
@article{Kahneman2003maps, title = {Maps of bounded rationality: Psychology for behavioral economics}, author = { Kahneman, Daniel }, journal = {The American Economic Review}, volume = 93, number = 5, pages = {1449--1475}, year = 2003 }
@article{KalHasHemSor2023deeprl, author = {Kallestad, Jakob and Hasibi, Ramin and Hemmati, Ahmad and Kenneth S{\"o}rensen }, title = {A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems}, journal = {European Journal of Operational Research}, year = 2023, volume = 309, number = 1, pages = {446--468}, month = aug, doi = {10.1016/j.ejor.2023.01.017}, keywords = {Deep RL, hyper-heuristic, ALNS} }
@article{KanHeWei2013, author = {Qinma Kang and Hong He and Jun Wei}, title = {An Effective Iterated Greedy Algorithm for Reliability-oriented Task Allocation in Distributed Computing Systems}, journal = {Journal of Parallel and Distributed Computing}, year = 2013, volume = 73, number = 8, pages = {1106--1115} }
@article{Kar2016, author = {Korhan Karabulut}, title = {A hybrid iterated greedy algorithm for total tardiness minimization in permutation flowshops}, journal = {Computers and Industrial Engineering}, year = 2016, volume = 98, number = {Supplement C}, pages = {300 -- 307} }
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@article{KarHooEib2015:tec, author = {Giorgos Karafotias and Mark Hoogendoorn and Agoston E. Eiben }, title = {Parameter Control in Evolutionary Algorithms: Trends and Challenges}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2015, volume = 19, number = 2, pages = {167--187} }
@article{KarKok2010tdea, title = {A territory defining multiobjective evolutionary algorithms and preference incorporation}, author = { Karahan, {\.I}brahim and Murat K{\"o}ksalan }, journal = {IEEE Transactions on Evolutionary Computation}, volume = 14, number = 4, pages = {636--664}, year = 2010, keywords = {TDEA}, doi = {10.1109/TEVC.2009.2033586} }
@article{KarMohMey2022ml, author = {Maryam Karimi-Mamaghan and Mehrdad Mohammadi and Patrick Meyer and Amir Mohammad Karimi-Mamaghan and Talbi, El-Ghazali }, title = {Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art}, journal = {European Journal of Operational Research}, year = 2022, volume = 296, number = 2, pages = {393--422}, doi = {10.1016/j.ejor.2021.04.032}, keywords = {Meta-heuristics, Machine learning, Combinatorial optimization problems, State-of-the-art}, abstract = {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.} }
@article{KarShiDai05:medicine, title = {Prediction of {MHC} class {II} binders using the ant colony search strategy}, author = {Karpenko, Oleksiy and Shi, Jianming and Dai, Yang}, journal = {Artificial Intelligence in Medicine}, volume = 35, number = 1, pages = {147--156}, year = 2005 }
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@article{KasReeCha2012ems, title = {Many-objective de {Novo} water supply portfolio planning under deep uncertainty}, author = { Kasprzyk, Joseph R. and Patrick M. Reed and Characklis, Gregory W. and Kirsch, Brian R.}, journal = {Environmental Modelling \& Software}, volume = 34, pages = {87--104}, year = 2012, keywords = {scenario-based} }
@article{KazCohJea2020, author = {Artem Kaznatcheev and David A. Cohen and Peter Jeavons}, title = {Representing Fitness Landscapes by Valued Constraints to Understand the Complexity of Local Search}, journal = {Journal of Artificial Intelligence Research}, volume = 69, pages = {1077--1102}, year = 2020, doi = {10.1613/jair.1.12156} }
@article{KeArcFen08, author = {Liangjun Ke and Claudia Archetti and Zuren Feng}, title = {Ants can solve the team orienteering problem}, volume = 54, number = 3, journal = {Computers and Industrial Engineering}, year = 2008, pages = {648--665}, doi = {10.1016/j.cie.2007.10.001}, abstract = {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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@article{KerTra2019, author = { Pascal Kerschke and Heike Trautmann }, title = {Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning}, journal = {Evolutionary Computation}, volume = 27, number = 1, pages = {99--127}, year = 2019, doi = {10.1162/evco_a_00236}, abstract = {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. } }
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@article{KimAllLop2020arxiv, author = { Kim, Youngmin and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art}, journal = {Arxiv preprint arXiv:2101.09505 [cs.LG]}, year = 2020, url = {https://arxiv.org/abs/2101.09505}, abstract = {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. } }
@article{KimCouYou2021set, title = {Bayesian Optimization with Approximate Set Kernels}, author = {Jungtaek Kim and Michael McCourt and Tackgeun You and Saehoon Kim and Seungjin Choi}, abstract = {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 \emph{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.}, journal = {Machine Learning}, year = 2021, doi = {10.1007/s10994-021-05949-0} }
@article{KimParLee2017, author = {Kim, J.-S. and Park, J.-H. and Lee, D.-H.}, title = {Iterated Greedy Algorithms to Minimize the Total Family Flow Time for Job-shop Scheduling with Job Families and Sequence-dependent Set-ups}, journal = {Engineering Optimization}, year = 2017, volume = 49, number = 10, pages = {1719--1732} }
@article{KinBa2014adam, title = {Adam: A method for stochastic optimization}, author = {Kingma, Diederik P. and Ba, Jimmy}, journal = {Arxiv preprint arXiv:1412.6980 [cs.LG]}, year = 2014, url = {https://arxiv.org/abs/1412.6980}, annote = {Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015~\cite{KinBa2015adam}} }
@article{KirTou1985, author = { Scott Kirkpatrick and G. Toulouse}, title = {Configuration Space Analysis of Travelling Salesman Problems}, journal = {Journal de Physique}, year = 1985, volume = 46, number = 8, pages = {1277--1292} }
@article{Kirkpatrick1984, author = { Scott Kirkpatrick }, title = {Optimization by Simulated Annealing: Quantitative Studies}, journal = {Journal of Statistical Physics}, year = 1984, volume = 34, number = {5-6}, pages = {975--986} }
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@article{KlaMosNau2017iwoven, author = { Kathrin Klamroth and Mostaghim, Sanaz and Boris Naujoks and Silvia Poles and Robin C. Purshouse and G{\"u}nther Rudolph and Ruzika, Stefan and Serpil Say{\i}n and Margaret M. Wiecek and Xin Yao }, title = {Multiobjective optimization for interwoven systems}, journal = {Journal of Multi-Criteria Decision Analysis}, year = 2017, volume = 24, number = {1-2}, pages = {71--81}, doi = {10.1002/mcda.1598} }
@article{KleShaHom2002, author = {Anton J. Kleywegt and Alexander Shapiro and Tito Homem{-}de{-}Mello}, title = {The Sample Average Approximation Method for Stochastic Discrete Optimization}, journal = {SIAM Journal on Optimization}, year = 2002, volume = 12, number = 2, pages = {479--502} }
@article{Kno2005tec, author = { Joshua D. Knowles }, title = {{ParEGO}: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2006, volume = 10, number = 1, pages = {50--66}, doi = {10.1109/TEVC.2005.851274}, keywords = {ParEGO, online, metamodel} }
@article{Kno2009closed, author = { Joshua D. Knowles }, title = {Closed-loop evolutionary multiobjective optimization}, journal = {IEEE Computational Intelligence Magazine}, volume = 4, issue = 3, pages = {77--91}, doi = {10.1109/MCI.2009.933095}, year = 2009, abstract = {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\textemdash design engineering problems in fluid dynamics, and chemical plant process optimization\textemdash 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.}, langid = {english} }
@article{KnoCor00paes, author = { Joshua D. Knowles and David Corne }, title = {Approximating the Nondominated Front Using the {Pareto} Archived Evolution Strategy}, journal = {Evolutionary Computation}, volume = 8, number = 2, pages = {149--172}, year = 2000, doi = {10.1162/106365600568167}, annote = {Proposed PAES} }
@article{KnoCor2003tec, author = { Joshua D. Knowles and David Corne }, title = {Properties of an Adaptive Archiving Algorithm for Storing Nondominated Vectors}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2003, volume = 7, number = 2, pages = {100--116}, month = apr, keywords = {S-metric, hypervolume}, annote = {Proposed to use S-metric (hypervolume metric) for environmental selection} }
@article{KnoVanGro2011, author = {Knol, Mirjam J. and VanderWeele, Tyler J. and Groenwold, Rolf H. H. and Klungel, Olaf H. and Rovers, Maroeska M. and Grobbee, Diederick E.}, title = {Estimating measures of interaction on an additive scale for preventive exposures}, journal = {European Journal of Epidemiology}, year = 2011, volume = 26, number = 6, pages = {433--438} }
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@article{KocHaoGlo2014bqap, title = {The unconstrained binary quadratic programming problem: a survey}, author = { Gary A. Kochenberger and Jin-Kao Hao and Fred Glover and Lewis, Mark and L{\"u}, Zhipeng and Wang, Haibo and Wang, Yang}, journal = {Journal of Combinatorial Optimization}, volume = 28, number = 1, pages = {58--81}, year = 2014, doi = {10.1007/s10878-014-9734-0} }
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@article{KokKar2010itdea, title = {An Interactive Territory Defining Evolutionary Algorithm: {iTDEA}}, volume = 14, doi = {10.1109/TEVC.2010.2070070}, number = 5, journal = {IEEE Transactions on Evolutionary Computation}, author = { Murat K{\"o}ksalan and Karahan, {\.I}brahim }, month = oct, year = 2010, pages = {702--722} }
@article{KolHar2006ejor, author = {Kolisch, Rainer and Hartmann, S{\"o}nke}, title = {Experimental investigation of heuristics for resource-constrained project scheduling: An update}, volume = 174, doi = {10.1016/j.ejor.2005.01.065}, abstract = {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}.}, number = 1, journal = {European Journal of Operational Research}, month = oct, year = 2006, keywords = {Computational evaluation, Heuristics, Project scheduling, Resource constraints}, pages = {23--37} }
@article{KolPap2007approx, title = {Approximately dominating representatives}, author = {Koltun, Vladlen and Christos H. Papadimitriou }, journal = {Theoretical Computer Science}, year = 2007, number = 3, pages = {148--154}, volume = 371, publisher = {Elsevier} }
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@article{KolRee2007video, title = {A framework for visually interactive decision-making and design using evolutionary multi-objective optimization ({VIDEO})}, author = { Kollat, Joshua B. and Patrick M. Reed }, journal = {Environmental Modelling \& Software}, volume = 22, number = 12, pages = {1691--1704}, year = 2007, keywords = {glyph plot} }
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@article{Kor1985omega, author = {Kornbluth, Jsh}, title = {Sequential multi-criterion decision making}, doi = {10.1016/0305-0483(85)90045-3}, abstract = {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.}, number = 6, volume = 13, journal = {Omega}, year = 1985, keywords = {machine decision making}, pages = {569--574} }
@article{KorMosWal1990choice, author = { Pekka Korhonen and Moskowitz, Herbert and Wallenius, Jyrki }, title = {Choice Behavior in Interactive Multiple-Criteria Decision Making}, journal = {Annals of Operations Research}, year = 1990, volume = 23, number = 1, pages = {161--179}, month = dec, doi = {10.1007/BF02204844}, abstract = {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.} }
@article{KorPagFal2001, title = {On the ``dimensionality curse'' and the ``self-similarity blessing''}, author = {Korn, Flip and Pagel, B.-U. and Faloutsos, Christos}, journal = {IEEE Transactions on Knowledge and Data Engineering}, volume = 13, number = 1, pages = {96--111}, year = 2001, doi = {10.1109/69.908983}, abstract = {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.} }
@article{KorSilRob04:ml-aco, author = { P. Koro{\v s}ec and Jurij {\v S}ilc and B. Robi{\v c}}, title = {Solving the mesh-partitioning problem with an ant-colony algorithm}, journal = {Parallel Computing}, year = 2004, volume = 30, pages = {785--801} }
@article{KorSilWalOor2012linear, author = { Pekka Korhonen and Silvennoinen, Kari and Wallenius, Jyrki and {\"O}{\"o}rni, Anssi}, title = {Can a linear value function explain choices? {An} experimental study}, journal = {European Journal of Operational Research}, year = 2012, volume = 219, number = 2, pages = {360--367}, month = jun, shorttitle = {Can a linear value function explain choices?}, doi = {10.1016/j.ejor.2011.12.040}, abstract = {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.}, language = {en}, keywords = {Binary choices, Inconsistency, Linear value function, Multiple criteria, Weights} }
@article{KorStuExn07:si, author = { Oliver Korb and Thomas St{\"u}tzle and Thomas E. Exner }, title = {An Ant Colony Optimization Approach to Flexible Protein--Ligand Docking}, journal = {Swarm Intelligence}, year = 2007, volume = 1, number = 2, pages = {115--134} }
@article{KorStuExn2009jcim, author = { Oliver Korb and Thomas St{\"u}tzle and Thomas E. Exner }, title = {Empirical Scoring Functions for Advanced Protein-Ligand Docking with {PLANTS}}, journal = {Journal of Chemical Information and Modeling}, year = 2009, volume = 49, number = 2, pages = {84--96} }
@article{KorStuExn2010jcim, author = { Oliver Korb and Peter Monecke and Gerhard Hessler and Thomas St{\"u}tzle and Thomas E. Exner }, title = {pharm{ACO}phore: Multiple Flexible Ligand Alignment Based on Ant Colony Optimization}, journal = {Journal of Chemical Information and Modeling}, year = 2010, volume = 50, number = 9, pages = {1669--1681} }
@article{KorWal1998paretorace, author = { Pekka Korhonen and Wallenius, Jyrki }, title = {A pareto race}, journal = {Naval Research Logistics}, year = 1988, volume = 35, number = 6, pages = {615--623}, doi = {10.1002/1520-6750(198812)35:6<615::AID-NAV3220350608>3.0.CO;2-K}, abstract = {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.} }
@article{Kot2014:aim, author = {Kotthoff, Lars}, title = {Algorithm Selection for Combinatorial Search Problems: {A} Survey}, journal = {{AI} Magazine}, year = 2014, volume = 35, number = 3, pages = {48--60} }
@article{KotNeuRogWit2012swarm, author = {K{\"o}tzing, Timo and Frank Neumann and R{\"o}glin, Heiko and Carsten Witt }, title = {Theoretical Analysis of Two {ACO} Approaches for the Traveling Salesman Problem}, journal = {Swarm Intelligence}, year = 2012, volume = 6, number = 1, pages = {1--21}, abstract = {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.}, doi = {10.1007/s11721-011-0059-7} }
@article{KotThoHooHutLey2016autoweka, title = {{Auto-WEKA} 2.0: Automatic model selection and hyperparameter optimization in {WEKA}}, author = {Kotthoff, Lars and Thornton, Chris and Holger H. Hoos and Frank Hutter and Kevin Leyton-Brown }, journal = {Journal of Machine Learning Research}, volume = 17, pages = {1--5}, year = 2016 }
@article{KowStaMad2009sustainable, title = {Sustainable energy futures: Methodological challenges in combining scenarios and participatory multi-criteria analysis}, author = {Kowalski, Katharina and Stagl, Sigrid and Madlener, Reinhard and Omann, Ines}, journal = {European Journal of Operational Research}, volume = 197, number = 3, pages = {1063--1074}, year = 2009, publisher = {Elsevier} }
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@article{LaTMuePen11:soco, author = {LaTorre, Antonio and Muelas, Santiago and Pe{\~n}a, Jos{\'e}-Mar{\'i}a}, title = {A {MOS}-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test}, journal = {Soft Computing}, year = 2011, volume = 15, number = 11, pages = {2187--2199} }
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@article{Li2021telo, title = {Is Our Archiving Reliable? Multiobjective Archiving Methods on ``Simple'' Artificial Input Sequences}, author = { Li, Miqing }, journal = {ACM Transactions on Evolutionary Learning and Optimization}, year = 2021, number = 3, pages = {1--19}, volume = 1, doi = {10.1145/3465335} }
@article{LiCheFuYao2018twoarch, title = {Two-archive evolutionary algorithm for constrained multiobjective optimization}, author = {Li, Ke and Chen, Renzhi and Fu, Guangtao and Xin Yao }, journal = {IEEE Transactions on Evolutionary Computation}, year = 2018, number = 2, pages = {303--315}, volume = 23, publisher = {IEEE} }
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@article{LiGroYanLiu2018multi, title = {Multi-line distance minimization: A visualized many-objective test problem suite}, author = { Li, Miqing and Grosan, Crina and Yang, Shengxiang and Liu, Xiaohui and Xin Yao }, journal = {IEEE Transactions on Evolutionary Computation}, year = 2018, number = 1, pages = {61--78}, volume = 22, annote = {highly degenerate Pareto fronts} }
@article{LiLopYao2023archiving, author = { Li, Miqing and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Xin Yao }, title = {Multi-Objective Archiving}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2023, volume = 28, number = 3, pages = {696--717}, doi = {10.1109/TEVC.2023.3314152}, abstract = {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., $\epsilon$-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.} }
@article{LiShaBah2016traffic, author = {Li, Zhiyi and Shahidehpour, Mohammad and Bahramirad, Shay and Khodaei, Amin}, doi = {10.1109/TSG.2016.2526032}, journal = {IEEE Transactions on Smart Grid}, number = 4, pages = {1--1}, title = {Optimizing Traffic Signal Settings in Smart Cities}, volume = 3053, year = 2016, abstract = {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.} }
@article{LiChenXuGupta2015, author = {Xiaoping Li and Long Chen and Haiyan Xu and Jatinder N. D. Gupta}, title = {Trajectory Scheduling Methods for Minimizing Total Tardiness in a Flowshop}, journal = {Operations Research Perspectives}, volume = 2, pages = {13--23}, year = 2015, issn = {2214--7160}, doi = {10.1016/j.orp.2014.12.001} }
@article{LiJamSal2018hyperband, author = {Lisha Li and Kevin Jamieson and Giulia DeSalvo and Afshin Rostamizadeh and Ameet Talwalkar}, title = {Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization}, journal = {Journal of Machine Learning Research}, year = 2018, volume = 18, number = 185, pages = {1--52}, epub = {http://jmlr.org/papers/v18/16-558.html}, abstract = {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} }
@article{LiLi07, author = {Y. Li and W. Li}, title = {Adaptive Ant Colony Optimization Algorithm Based on Information Entropy: Foundation and Application}, journal = {Fundamenta Informaticae}, volume = 77, number = 3, year = 2007, pages = {229--242}, publisher = {IOS Press}, address = {Amsterdam, The Netherlands} }
@article{LiLiTanYao2015many, author = {Li, Bingdong and Li, Jinlong and Tang, Ke and Xin Yao }, title = {Many-Objective Evolutionary Algorithms: A Survey}, journal = {{ACM} Computing Surveys}, volume = 48, number = 1, year = 2015, pages = {1--35}, doi = {10.1145/2792984}, numpages = 35 }
@article{LiTanLiYao2016stochastic, title = {Stochastic ranking algorithm for many-objective optimization based on multiple indicators}, author = {Li, Bingdong and Tang, Ke and Li, Jinlong and Xin Yao }, journal = {IEEE Transactions on Evolutionary Computation}, year = 2016, number = 6, pages = {924--938}, volume = 20, publisher = {IEEE} }
@article{LiYanLiu2014shift, title = {Shift-based density estimation for {Pareto}-based algorithms in many-objective optimization}, author = { Li, Miqing and Yang, Shengxiang and Liu, Xiaohui}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2014, number = 3, pages = {348--365}, volume = 18, publisher = {IEEE}, annote = {Proposed SDE indicator algorithm} }
@article{LiYanLiu2016tec, title = {{Pareto} or non-{Pareto}: {Bi}-criterion evolution in multiobjective optimization}, author = { Li, Miqing and Yang, Shengxiang and Liu, Xiaohui}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2016, number = 5, pages = {645--665}, volume = 20 }
@article{LiYao2019qual, title = {Quality Evaluation of Solution Sets in Multiobjective Optimisation: A Survey}, author = { Li, Miqing and Xin Yao }, journal = {{ACM} Computing Surveys}, year = 2019, number = 2, volume = 52, pages = {1--38}, doi = {10.1145/3300148}, publisher = {ACM} }
@article{LiYao2017arxiv, title = {Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization}, author = { Li, Miqing and Xin Yao }, journal = {arXiv preprint arXiv:1702.00477}, year = 2017 }
@article{LiYao2020ec, title = {What weights work for you? Adapting weights for any {Pareto} front shape in decomposition-based evolutionary multiobjective optimisation}, author = { Li, Miqing and Xin Yao }, journal = {Evolutionary Computation}, year = 2020, number = 2, pages = {227--253}, volume = 28 }
@article{LiZha2009:moead-de, title = {Multiobjective Optimization Problems with Complicated {Pareto} sets, {MOEA/D} and {NSGA-II}}, author = {Li, Hui and Zhang, Qingfu }, journal = {IEEE Transactions on Evolutionary Computation}, volume = 13, number = 2, pages = {284--302}, year = 2009 }
@article{LiZouYan2021twoarch, title = {A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization}, author = {Li, Zhipan and Zou, Juan and Yang, Shengxiang and Zheng, Jinhua}, journal = {Information Sciences}, year = 2021, pages = {413--430}, volume = 574, publisher = {Elsevier} }
@article{LiaAydStu13, author = {Liao, Tianjun and Do\v{g}an Ayd{\i}n and Thomas St{\"u}tzle }, title = {Artificial Bee Colonies for Continuous Optimization: Experimental Analysis and Improvements}, journal = {Swarm Intelligence}, year = 2013, volume = 7, number = 4, pages = {327--356} }
@article{LiaMolMonStu2014, author = {Liao, Tianjun and Daniel Molina and Marco A. {Montes de Oca} and Thomas St{\"u}tzle }, title = {A Note on the Effects of Enforcing Bound Constraints on Algorithm Comparisons using the {IEEE} {CEC'05} Benchmark Function Suite}, journal = {Evolutionary Computation}, year = 2014, volume = 22, number = 2, pages = {351--359} }
@article{LiaMolStu2015, author = {Liao, Tianjun and Daniel Molina and Thomas St{\"u}tzle }, title = {Performance Evaluation of Automatically Tuned Continuous Optimizers on Different Benchmark Sets}, journal = {Applied Soft Computing}, year = 2015, volume = 27, pages = {490--503} }
@article{LiaMonStu13:soco, author = {Liao, Tianjun and Marco A. {Montes de Oca} and Thomas St{\"u}tzle }, title = {Computational results for an automatically tuned {CMA-ES} with increasing population size on the {CEC'05} benchmark set}, journal = {Soft Computing}, pages = {1031--1046}, volume = 17, number = 6, year = 2013, doi = {0.1007/s00500-012-0946-x} }
@article{LiaSocMonStuDor2014, author = {Liao, Tianjun and Krzysztof Socha and Marco A. {Montes de Oca} and Thomas St{\"u}tzle and Marco Dorigo }, title = {Ant Colony Optimization for Mixed-Variable Optimization Problems}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2014, volume = 18, number = 4, pages = {503--518}, keywords = {ACOR} }
@article{LiaStuMonDor2014, author = {Liao, Tianjun and Thomas St{\"u}tzle and Marco A. {Montes de Oca} and Marco Dorigo }, title = {A Unified Ant Colony Optimization Algorithm for Continuous Optimization}, journal = {European Journal of Operational Research}, year = 2014, volume = 234, number = 3, pages = {597--609} }
@article{LiaTseLua07, author = { C.-J. Liao and C.-T. Tseng and P. Luarn }, title = {A Discrete Version of Particle Swarm Optimization for Flowshop Scheduling Problems}, journal = {Computers \& Operations Research}, volume = 34, number = 10, pages = {3099--3111}, year = 2007 }
@article{LieDaoVerDer2019tec, author = { Arnaud Liefooghe and Fabio Daolio and Bilel Derbel and Verel, S{\'e}bastien and Aguirre, Hern\'{a}n E. and Tanaka, Kiyoshi }, journal = {IEEE Transactions on Evolutionary Computation}, number = 6, pages = {1063--1077}, title = {Landscape-Aware Performance Prediction for Evolutionary Multi-objective Optimization}, volume = 24, year = 2020 }
@article{LieHumMes2011, author = { Arnaud Liefooghe and J{\'e}r{\'e}mie Humeau and Salma Mesmoudi and Laetitia Jourdan and Talbi, El-Ghazali }, title = {On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems}, journal = {Journal of Heuristics}, volume = 18, number = 2, pages = {317--352}, year = 2012, abstract = {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.}, doi = {10.1007/s10732-011-9181-3} }
@article{LieJouTal2011paradiseo, title = {A Software Framework Based on a Conceptual Unified Model for Evolutionary Multiobjective Optimization: {ParadisEO}-{MOEO}}, author = { Arnaud Liefooghe and Laetitia Jourdan and Talbi, El-Ghazali }, journal = {European Journal of Operational Research}, volume = 209, number = 2, pages = {104--112}, year = 2011 }
@article{LieVerHao2014hybrid, author = { Arnaud Liefooghe and Verel, S{\'e}bastien and Jin-Kao Hao }, title = {A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming}, journal = {Applied Soft Computing}, year = 2014, volume = 16, pages = {10--19}, publisher = {Elsevier} }
@article{LikKoc2007predictive, title = {Predictive control of a gas--liquid separation plant based on a {Gaussian} process model}, author = {Likar, Bojan and Kocijan, Ju{\v{s}}}, journal = {Computers \& Chemical Engineering}, volume = 31, number = 3, pages = {142--152}, year = 2007, publisher = {Elsevier}, doi = {10.1016/j.compchemeng.2006.05.011} }
@article{LinEggFeu2022smac3, author = { Marius Thomas Lindauer and Katharina Eggensperger and Matthias Feurer and Biedenkapp, Andr{\'e} and Difan Deng and Carolin Benjamins and Tim Ruhkopf and René Sass and Frank Hutter }, title = {{SMAC3}: A Versatile Bayesian Optimization Package for Hyperparameter Optimization}, journal = {Journal of Machine Learning Research}, year = 2022, volume = 23, pages = {1--9}, epub = {http://jmlr.org/papers/v23/21-0888.html} }
@article{LinHooHutSch2015autofolio, title = {{AutoFolio}: An Automatically Configured Algorithm Selector}, author = { Marius Thomas Lindauer and Holger H. Hoos and Frank Hutter and Schaub, Torsten}, journal = {Journal of Artificial Intelligence Research}, volume = 53, pages = {745--778}, year = 2015 }
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@article{LinVanKot2019, title = {The algorithm selection competitions 2015 and 2017}, author = { Marius Thomas Lindauer and van Rijn, Jan N. and Kotthoff, Lars}, journal = {Artificial Intelligence}, volume = 272, pages = {86--100}, year = 2019 }
@article{LisWit2015tcs, title = {Runtime Analysis of Ant Colony Optimization on Dynamic Shortest Path Problems}, journal = {Theoretical Computer Science}, volume = 561, number = {Part A}, pages = {73--85}, year = 2015, doi = {10.1016/j.tcs.2014.06.035}, author = { Andrei Lissovoi and Carsten Witt }, abstract = {A simple ACO algorithm called $\lambda$-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 $\lambda$ 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. } }
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@article{LiuYenGon2018twoarch, title = {A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies}, author = {Liu, Yiping and Yen, Gary G. and Gong, Dunwei}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2018, number = 4, pages = {660--674}, volume = 23 }
@article{LocSch1999mlsl, author = {Locatelli, Marco and Schoen, Fabio}, title = {Random Linkage: a family of acceptance/rejection algorithms for global optimisation.}, journal = {Mathematical Programming}, year = 1999, volume = 85, number = 2, keywords = {Multi-Level Single-Linkage (MLSL)} }
@article{LodMarMon2002, title = {Two-dimensional packing problems: A survey}, author = { Andrea Lodi and Silvano Martello and Monaci, Michele }, journal = {European Journal of Operational Research}, volume = 141, number = 2, pages = {241--252}, year = 2002, doi = {10.1016/S0377-2217(02)00123-6} }
@article{LodMarVig1999binpack, title = {Heuristic and metaheuristic approaches for a class of two-dimensional bin packing problems}, author = { Andrea Lodi and Silvano Martello and Vigo, Daniele }, journal = {INFORMS Journal on Computing}, volume = 11, number = 4, pages = {345--357}, year = 1999, publisher = {{INFORMS}}, doi = {10.1287/ijoc.11.4.345} }
@article{LodMarVig2004tspack, title = {{TSpack}: a unified tabu search code for multi-dimensional bin packing problems}, author = { Andrea Lodi and Silvano Martello and Vigo, Daniele }, journal = {Annals of Operations Research}, volume = 131, number = {1-4}, pages = {203--213}, year = 2004, publisher = {Springer}, doi = {10.1023/B:ANOR.0000039519.03572.08} }
@article{LodZar2017learning, title = {On Learning and Branching: A Survey}, author = { Andrea Lodi and Zarpellon, Giulia}, journal = {TOP}, volume = 25, pages = {207--236}, year = 2017, publisher = {Springer} }
@article{LohHorLin2008antennas, author = {Lohn, Jason D. and Hornby, Gregory S. and Linden, Derek S.}, title = {Human-competitive Evolved Antennas}, journal = {Artificial Intelligence for Engineering Design, Analysis and Manufacturing}, volume = 22, number = 3, year = 2008, pages = {235--247}, doi = {10.1017/s0890060408000164}, publisher = {Cambridge University Press}, annote = {Evolutionary optimization of antennas for NASA} }
@article{LopBlu2010cor, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum }, title = {Beam-{ACO} for the travelling salesman problem with time windows}, journal = {Computers \& Operations Research}, year = 2010, doi = {10.1016/j.cor.2009.11.015}, volume = 37, number = 9, pages = {1570--1583}, keywords = {Ant colony optimization, Travelling salesman problem with time windows, Hybridization}, abstract = {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.} }
@article{LopBlu2013asoc, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum and Jeffrey W. Ohlmann and Barrett W. Thomas }, title = {The Travelling Salesman Problem with Time Windows: Adapting Algorithms from Travel-time to Makespan Optimization}, journal = {Applied Soft Computing}, year = 2013, volume = 13, number = 9, pages = {3806--3815}, doi = {10.1016/j.asoc.2013.05.009}, epub = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2013-011.pdf} }
@article{LopBraPaq2021arxiv, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\"u}rgen Branke and Lu{\'i}s Paquete }, title = {Reproducibility in Evolutionary Computation}, journal = {Arxiv preprint arXiv:20102.03380 [cs.AI]}, year = 2021, url = {https://arxiv.org/abs/2102.03380}, abstract = {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} }
@article{LopBraPaq2021telo, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\"u}rgen Branke and Lu{\'i}s Paquete }, title = {Reproducibility in Evolutionary Computation}, journal = {ACM Transactions on Evolutionary Learning and Optimization}, year = 2021, volume = 1, number = 4, pages = {1--21}, doi = {10.1145/3466624}, epub = {https://arxiv.org/abs/2102.03380}, abstract = {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} }
@article{LopDubPerStuBir2016irace, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle and Mauro Birattari }, title = {The {\rpackage{irace}} Package: Iterated Racing for Automatic Algorithm Configuration}, journal = {Operations Research Perspectives}, year = 2016, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2016-003/}, doi = {10.1016/j.orp.2016.09.002}, volume = 3, pages = {43--58} }
@article{LopKesStu2017:cim, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci and Thomas St{\"u}tzle }, title = {Automatic Design of Hybrid Metaheuristics from Algorithmic Components}, journal = {Submitted}, year = 2017, optvolume = {}, optnumber = {}, optpages = {} }
@article{LopPaqStu05:jmma, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle }, title = {Hybrid Population-based Algorithms for the Bi-objective Quadratic Assignment Problem}, journal = {Journal of Mathematical Modelling and Algorithms}, year = 2006, volume = 5, number = 1, pages = {111--137}, doi = {10.1007/s10852-005-9034-x}, abstract = {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.} }
@article{LopPerStu2020ifors, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and P{\'e}rez C{\'a}ceres, Leslie and Thomas St{\"u}tzle }, title = {{irace}: A Tool for the Automatic Configuration of Algorithms}, journal = {International Federation of Operational Research Societies (IFORS) News}, year = 2020, volume = 14, number = 2, pages = {30--32}, month = jun, url = {https://www.ifors.org/newsletter/ifors-news-june2020.pdf} }
@article{LopPraPae08aco, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter }, title = {Ant Colony Optimisation for the Optimal Control of Pumps in Water Distribution Networks}, journal = {Journal of Water Resources Planning and Management, {ASCE}}, year = 2008, volume = 134, number = 4, pages = {337--346}, publisher = {{ASCE}}, epub = {http://link.aip.org/link/?QWR/134/337/1}, doi = {10.1061/(ASCE)0733-9496(2008)134:4(337)}, abstract = {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.} }
@article{LopPraPae2011ec, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter }, title = {Representations and Evolutionary Operators for the Scheduling of Pump Operations in Water Distribution Networks}, journal = {Evolutionary Computation}, year = 2011, doi = {10.1162/EVCO_a_00035}, volume = 19, number = 3, pages = {429--467}, abstract = {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.} }
@article{LopStu2012swarm, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {An experimental analysis of design choices of multi-objective ant colony optimization algorithms}, journal = {Swarm Intelligence}, year = 2012, number = 3, volume = 6, pages = {207--232}, doi = {10.1007/s11721-012-0070-7}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/} }
@article{LopStu2012tec, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {The Automatic Design of Multi-Objective Ant Colony Optimization Algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2012, volume = 16, number = 6, pages = {861--875}, doi = {10.1109/TEVC.2011.2182651}, abstract = { 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.} }
@article{LopStu2013ejor, author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatically Improving the Anytime Behaviour of Optimisation Algorithms}, journal = {European Journal of Operational Research}, year = 2014, volume = 235, number = 3, pages = {569--582}, doi = {10.1016/j.ejor.2013.10.043}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/}, abstract = {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.} }
@article{LopTerRos2014esa, author = {Eunice López-Camacho and Hugo Terashima-Marin and Peter Ross and Gabriela Ochoa }, title = {A unified hyper-heuristic framework for solving bin packing problems}, journal = {Expert Systems with Applications}, volume = 41, number = 15, pages = {6876--6889}, year = 2014, doi = {10.1016/j.eswa.2014.04.043} }
@article{LouBoi2008vns_anytime, author = { Samir Loudni and Patrice Boizumault }, title = {Combining {VNS} with constraint programming for solving anytime optimization problems}, journal = {European Journal of Operational Research}, year = 2008, volume = 191, pages = {705--735}, doi = {10.1016/j.ejor.2006.12.062} }
@article{Lourenco1995, author = { Helena R. {Louren{\c c}o} }, title = {Job-Shop Scheduling: Computational Study of Local Search and Large-Step Optimization Methods}, journal = {European Journal of Operational Research}, year = 1995, volume = 83, number = 2, pages = {347--364} }
@article{LovTor2001aor, author = {Lova, Antonio and Tormos, Pilar}, title = {Analysis of Scheduling Schemes and Heuristic Rules Performance in Resource-Constrained Multiproject Scheduling}, volume = 102, doi = {10.1023/A:1010966401888}, abstract = {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.}, number = {1-4}, journal = {Annals of Operations Research}, month = feb, year = 2001, keywords = {Combinatorics, heuristic based on priority rules, Multiproject scheduling, Operation {Research/Decision} Theory, Project management, project management software, Resource allocation, Theory of Computation}, pages = {263--286} }
@article{LovTorCer2009ijpe, author = {Lova, Antonio and Tormos, Pilar and Cervantes, Mariamar and Barber, Federico}, title = {An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes}, volume = 117, doi = {10.1016/j.ijpe.2008.11.002}, abstract = {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.}, number = 2, journal = {International Journal of Production Economics}, year = 2009, keywords = {genetic algorithm, multi-mode resource-constrained project scheduling}, pages = {302--316} }
@article{LozGloGarRodMar2014, author = { Manuel Lozano and Fred Glover and Carlos Garc{\'i}a-Mart{\'i}nez and Francisco J. Rodr{\'i}guez and Rafael Mart{\'i} }, title = {Tabu Search with Strategic Oscillation for the Quadratic Minimum Spanning Tree}, journal = {IIE Transactions}, year = 2014, volume = 46, number = 4, pages = {414--428} }
@article{LozMolGar2011, author = { Manuel Lozano and Daniel Molina and Carlos Garc{\'i}a-Mart{\'i}nez }, title = {Iterated Greedy for the Maximum Diversity Problem}, journal = {European Journal of Operational Research}, year = 2011, volume = 214, number = 1, pages = {31--38} }
@article{LuGloHao2010ejor, author = { L{\"u}, Zhipeng and Fred Glover and Jin-Kao Hao }, title = {A hybrid metaheuristic approach to solving the {UBQP} problem}, journal = {European Journal of Operational Research}, volume = 207, number = 3, pages = {1254--1262}, year = 2010, doi = {10.1016/j.ejor.2010.06.039} }
@article{Lucas2014ising, title = {Ising formulations of many {NP} problems}, author = {Lucas, Andrew}, journal = { Frontiers in Physics }, volume = 2, pages = 5, year = 2014, publisher = {Frontiers}, doi = {10.3389/fphy.2014.00005} }
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@article{LusTeg2009tpls, author = { Thibaut Lust and Jacques Teghem }, title = {Two-phase {Pareto} local search for the biobjective traveling salesman problem}, doi = {10.1007/s10732-009-9103-9}, abstract = {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. }, journal = {Journal of Heuristics}, volume = 16, number = 3, pages = {475--510}, year = 2010 }
@article{LusTeg2010arxiv, author = { Thibaut Lust and Jacques Teghem }, title = {The multiobjective multidimensional knapsack problem: a survey and a new approach}, journal = {Arxiv preprint arXiv:1007.4063}, year = 2010, note = {Published as~\cite{LusTeg2012itor}} }
@article{LusTeg2012itor, title = {The multiobjective multidimensional knapsack problem: a survey and a new approach}, author = { Thibaut Lust and Jacques Teghem }, journal = {International Transactions in Operational Research}, volume = 19, number = 4, pages = {495--520}, year = 2012, doi = {10.1111/j.1475-3995.2011.00840.x} }
@article{LustJasz09btsp, author = { Thibaut Lust and Andrzej Jaszkiewicz }, title = {Speed-up techniques for solving large-scale biobjective {TSP}}, journal = {Computers \& Operations Research}, year = 2010, doi = {10.1016/j.cor.2009.01.005}, pages = {521--533}, volume = 37, number = 3, keywords = {Multiobjective combinatorial optimization, Hybrid metaheuristics, TSP, Local search, Speed-up techniques} }
@article{LuvBarBri2014, title = {A survey on multi-objective evolutionary algorithms for many-objective problems}, author = { C. von L{\"u}cken and Benjam{\'i}n Bar{\'a}n and Brizuela, Carlos}, pages = {707--756}, year = 2014, journal = {Computational Optimization and Applications}, volume = 58, number = 3 }
@article{MaaHin2008tsne, author = {Laurens van der Maaten and Geoffrey Hinton}, title = {Visualizing Data using t-{SNE}}, journal = {Journal of Machine Learning Research}, year = 2008, volume = 9, number = 86, pages = {2579--2605}, epub = {http://jmlr.org/papers/v9/vandermaaten08a.html} }
@article{MachBelTal2018ale, author = {Machado, Marlos C. and Bellemare, Marc G. and Talvitie, Erik and Veness, Joel and Hausknecht, Matthew and Bowling, Michael}, title = {Revisiting the {Arcade} {Learning} {Environment}: Evaluation Protocols and Open Problems for General Agents}, year = 2018, publisher = {AI Access Foundation}, address = {El Segundo, CA, USA}, volume = 61, number = 1, issn = {1076-9757}, abstract = {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.}, journal = {Journal of Artificial Intelligence Research}, month = jan, pages = {523--562}, numpages = 40 }
@article{Madden2012, title = {From Databases to Big Data}, author = {Madden, Sam}, journal = {IEEE Internet Computing}, volume = 16, number = 3, year = 2012 }
@article{MahFesDam2007harmony, author = {M. Mahdavi and M. Fesanghary and E. Damangir}, title = {An improved harmony search algorithm for solving optimization problems}, journal = {Applied Mathematics and Computation}, volume = 188, number = 2, pages = {1567--1579}, year = 2007, doi = {10.1016/j.amc.2006.11.033}, keywords = {Global optimization, Heuristics, Harmony search algorithm, Mathematical programming}, abstract = {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.} }
@article{MaiRon2012, title = {New heuristics for total tardiness minimization in a flexible flowshop}, author = {Mainieri, Guilherme B. and Ronconi, D{\'e}bora P.}, journal = {Optimization Letters}, pages = {1--20}, year = 2012 }
@article{MaieSimp03:ACODesignWDN, author = { Holger R. Maier and Angus R. Simpson and Aaron C. Zecchin and Wai Kuan Foong and Kuang Yeow Phang and Hsin Yeow Seah and Tan, Chan Lim }, title = {Ant Colony Optimization for Design of Water Distribution Systems}, journal = {Journal of Water Resources Planning and Management, {ASCE}}, volume = 129, number = 3, pages = {200--209}, date = {2003-05/2003-06}, year = 2003, month = may # { / } # jun }
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@article{Man1999:joc, author = { Vittorio Maniezzo }, title = {Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem}, journal = {INFORMS Journal on Computing}, year = 1999, volume = 11, number = 4, pages = {358--369} }
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@article{Mar84, title = {On a multicritera shortest path problem}, journal = {European Journal of Operational Research}, volume = 16, pages = {236--245}, year = 1984, author = {E. Q. V. Martins} }
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@article{MarCavHer2023repr, author = { Raul Mart{\'i}n-Santamar{\'i}a and Cavero, Sergio and Herrán, Alberto and Duarte, Abraham and Colmenar, J. Manuel }, title = {A Practical Methodology for Reproducible Experimentation: An Application to the Double-Row Facility Layout Problem}, journal = {Evolutionary Computation}, year = 2023, pages = {1--35}, month = nov, issn = {1063-6560}, doi = {10.1162/evco_a_00317}, publisher = {MIT Press}, keywords = {irace} }
@article{MarDeBHaeVanSnoBae07, author = {D. Martens and M. De Backer and R. Haesen and J. Vanthienen and M. Snoeck and B. Baesens}, title = {Classification With Ant Colony Optimization}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2007, volume = 11, number = 5, pages = {651--665} }
@article{MarLopStuCol2024auto, author = { Raul Mart{\'i}n-Santamar{\'i}a and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Colmenar, J. Manuel }, title = {On the automatic generation of metaheuristic algorithms for combinatorial optimization problems}, journal = {European Journal of Operational Research}, year = 2024, doi = {10.1016/j.ejor.2024.06.001}, abstract = {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} }
@article{MarMarWeiWol2002, title = {Cutting planes in integer and mixed integer programming}, author = {Marchand, Hugues and Martin, Alexander and Weismantel, Robert and Wolsey, Laurence}, journal = {Discrete Applied Mathematics}, volume = 123, number = {1--3}, pages = {397--446}, year = 2002, publisher = {Elsevier} }
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@article{MarOttFel92:orl, author = { Olivier Martin and S. W. Otto and E. W. Felten}, title = {Large-step {Markov} Chains for the {TSP} Incorporating Local Search Heuristics}, journal = {Operations Research Letters}, year = 1992, volume = 11, number = 4, pages = {219--224} }
@article{MarReiDua2012, author = { Rafael Mart{\'i} and Gerhard Reinelt and Duarte, Abraham }, title = {A Benchmark Library and a Comparison of Heuristic Methods for the Linear Ordering Problem}, journal = {Computational Optimization and Applications}, year = 2012, volume = 51, number = 3, pages = {1297--1317} }
@article{MarSanPer2022strategic, author = { Raul Mart{\'i}n-Santamar{\'i}a and Jes{\'u}s S{\'a}nchez-Oro and S. P\'{e}rez-Pel\'{o} and Duarte, Abraham }, title = {Strategic oscillation for the balanced minimum sum-of-squares clustering problem}, journal = {Information Sciences}, year = 2022, volume = 585, pages = {529--542}, doi = {10.1016/j.ins.2021.11.048} }
@article{MarTot1990dam, author = { Silvano Martello and Paolo Toth }, title = {Lower bounds and reduction procedures for the bin packing problem}, journal = {Discrete Applied Mathematics}, volume = 28, number = 1, year = 1990, pages = {59--70}, doi = {10.1016/0166-218X(90)90094-S} }
@article{MarVig1998exact, title = {Exact solution of the two-dimensional finite bin packing problem}, author = { Silvano Martello and Vigo, Daniele }, journal = {Management Science}, volume = 44, number = 3, pages = {388--399}, year = 1998, publisher = {{INFORMS}}, doi = {10.1287/mnsc.44.3.388} }
@article{MasHwa1981isgp, author = {Masud, Abu S. and Hwang, C. L.}, title = {Interactive Sequential Goal Programming}, journal = {Journal of the Operational Research Society}, year = 1981, volume = 32, number = 5, pages = {391--400}, month = may, issn = {1476-9360}, doi = {10.1057/jors.1981.76}, abstract = {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.} }
@article{MasLopDubStu2014cor, author = { Franco Mascia and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle }, title = {Grammar-Based Generation of Stochastic Local Search Heuristics through Automatic Algorithm Configuration Tools}, journal = {Computers \& Operations Research}, year = 2014, doi = {10.1016/j.cor.2014.05.020}, volume = 51, pages = {190--199} }
@article{MasPelStuBir2014itor, author = { Franco Mascia and Paola Pellegrini and Thomas St{\"u}tzle and Mauro Birattari }, title = {An Analysis of Parameter Adaptation in Reactive Tabu Search}, journal = {International Transactions in Operational Research}, year = 2014, volume = 21, number = 1, pages = {127--152} }
@article{MasVidMic++2013, author = {Renaud Masson and Thibaut Vidal and Julien Michallet and Puca Huachi {Vaz Penna} and Vinicius Petrucci and Anand Subramanian and Hugues Dubedout}, title = {An Iterated Local Search Heuristic for Multi-capacity Bin Packing and Machine Reassignment Problems}, journal = {Expert Systems with Applications}, year = 2013, volume = 40, number = 13, pages = {5266--5275} }
@article{MatDauLah2011:ejor, author = {Yazid Mati and St{\'e}phane Dauz{\`e}re-P{\`e}r{\'e}s and Chams Lahlou}, title = {A General Approach for Optimizing Regular Criteria in the Job-shop Scheduling Problem}, journal = {European Journal of Operational Research}, year = 2011, volume = 212, number = 1, pages = {33--42} }
@article{MazLopChuMie2023tgp, author = { Atanu Mazumdar and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Tinkle Chugh and Kaisa Miettinen }, title = {Treed {Gaussian} Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems}, journal = {Evolutionary Computation}, year = 2023, volume = 31, number = 4, pages = {375--399}, doi = {10.1162/evco_a_00329}, abstract = {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} }
@article{McConMehNah2011certifying, author = {Ross M. McConnell and Kurt Mehlhorn and Stefan N{\"a}her and Pascal Schweitzer}, title = {Certifying algorithms}, journal = {Computer Science Review}, year = 2011, volume = 5, number = 2, pages = {119--161}, issn = {1574-0137}, doi = {10.1016/j.cosrev.2010.09.009}, keywords = {Algorithms, Software reliability, Certification}, abstract = {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.} }
@article{McCorPow03demand, title = {Optimal Pump Scheduling in Water Supply Systems with Maximum Demand Charges}, author = { G. McCormick and R. S. Powell }, publisher = {ASCE}, year = 2003, journal = {Journal of Water Resources Planning and Management, {ASCE}}, volume = 129, number = 5, pages = {372--379}, keywords = {water supply; pumps; Markov processes; cost optimal control}, epub = {http://link.aip.org/link/?QWR/129/372/1}, doi = {10.1061/(ASCE)0733-9496(2003)129:5(372)} }
@article{McCormick04, author = { G. McCormick and R. S. Powell }, title = {Derivation of near-optimal pump schedules for water distribution by simulated annealing}, journal = {Journal of the Operational Research Society}, year = 2004, volume = 55, number = 7, pages = {728--736}, month = jul, doi = {10.1057/palgrave.jors.2601718}, abstract = {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.} }
@article{McDermott2020nfl, author = {James McDermott}, title = {When and Why Metaheuristics Researchers can Ignore "No Free Lunch" Theorems}, journal = {{SN} Computer Science}, volume = 1, number = 60, pages = {1--18}, year = 2020, doi = {10.1007/s42979-020-0063-3} }
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@article{MieEskRui2010nautilus, author = { Kaisa Miettinen and Eskelinen, Petri and Francisco Ruiz and Mariano Luque }, title = {{NAUTILUS} method: {An} interactive technique in multiobjective optimization based on the nadir point}, journal = {European Journal of Operational Research}, year = 2010, volume = 206, number = 2, pages = {426--434}, month = oct, issn = {0377-2217}, shorttitle = {{NAUTILUS} method}, doi = {10.1016/j.ejor.2010.02.041}, abstract = {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.}, language = {en}, keywords = {Reference point methods, Interactive methods, Multiple objective programming, Pareto optimality, Preference information} }
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@article{MunSunKirHal2015sel, title = {Algorithm selection for black-box continuous optimization problems: a survey on methods and challenges}, author = { Mario A. Mu{\~{n}}oz and Sun, Yuan and Kirley, Michael and Halgamuge, Saman K.}, journal = {Information Sciences}, volume = 317, pages = {224--245}, year = 2015 }
@article{MunVilBaaSmi2018ismlc, author = { Mario A. Mu{\~{n}}oz and Villanova, Laura and Baatar, Davaatseren and Kate Smith{-}Miles }, title = {Instance Spaces for Machine Learning Classification}, journal = {Machine Learning}, year = 2018, volume = 107, number = 1, pages = {109--147}, doi = {10.1007/s10994-017-5629-5} }
@article{NagKob2013, author = {Yuichi Nagata and Shigenobu Kobayashi}, title = {A Powerful Genetic Algorithm Using Edge Assembly Crossover for the Traveling Salesman Problem}, journal = {INFORMS Journal on Computing}, year = 2013, volume = 25, number = 2, pages = {346--363}, doi = {10.1287/ijoc.1120.0506}, keywords = {TSP, EAX, evolutionary algorithms}, abstract = {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.} }
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@article{NebLopGarCoe2023automopso, author = { Nebro, Antonio J. and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Jos{\'e} Garc{\'i}a-Nieto and Carlos A. {Coello Coello} }, title = {On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis}, journal = {Swarm Intelligence}, year = 2023, doi = {10.1007/s11721-023-00227-2}, abstract = {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.} }
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@article{PagStu2019:ejor, author = { Federico Pagnozzi and Thomas St{\"u}tzle }, title = {Automatic Design of Hybrid Stochastic Local Search Algorithms for Permutation Flowshop Problems}, journal = {European Journal of Operational Research}, year = 2019, volume = 276, pages = {409--421}, issue = 2, doi = {10.1016/j.ejor.2019.01.018}, keywords = {EMILI}, abstract = {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.} }
@article{PagStu2020:itor, author = { Federico Pagnozzi and Thomas St{\"u}tzle }, title = {Evaluating the impact of grammar complexity in automatic algorithm design}, journal = {International Transactions in Operational Research}, pages = {1--26}, doi = {10.1111/itor.12902}, year = 2020 }
@article{PagStu2021:orp, author = { Federico Pagnozzi and Thomas St{\"u}tzle }, title = {Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints}, journal = {Operations Research Perspectives}, year = 2021, volume = 8, pages = 100180, doi = {10.1016/j.orp.2021.100180}, abstract = {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.} }
@article{PajBlaHerMar2021archiving, title = {A Comparison of Archiving Strategies for Characterization of Nearly Optimal Solutions under Multi-Objective Optimization}, author = {Pajares, Alberto and Blasco, Xavier and Herrero, Juan Manuel and Mart{\'i}nez, Miguel A.}, journal = {Mathematics}, year = 2021, doi = {10.3390/math9090999}, volume = 9, number = 9, pages = {999}, abstract = {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 (ArchiveUpdate$P_{Q,\epsilon}D_{xy}$, 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} }
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@article{Scipy2020natmet, fullauthor = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy 1.0 Contributors}}, author = {Virtanen, Pauli and others}, title = {{SciPy} 1.0: Fundamental Algorithms for Scientific Computing in {Python}}, journal = {Nature Methods}, year = 2020, volume = 17, pages = {261--272}, epub = {https://rdcu.be/b08Wh}, doi = {10.1038/s41592-019-0686-2} }
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@article{ShaLopAlm2023hidden, author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Allmendinger, Richard }, title = {Detecting Hidden and Irrelevant Objectives in Interactive Multi-Objective Optimization}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2023, volume = 28, number = 2, pages = {544--557}, doi = {10.1109/TEVC.2023.3269348}, abstract = {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.} }
@article{ShaLopKno2023bench, author = { Shavarani, Seyed Mahdi and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles }, title = {On Benchmarking Interactive Evolutionary Multi-Objective Algorithms}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2023, volume = 28, number = 4, pages = {1084--1098}, doi = {10.1109/TEVC.2023.3289872}, abstract = {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} }
@article{ShaLopMie2021visual, title = {Visualizations for Decision Support in Scenario-based Multiobjective Optimization}, author = { Shavazipour, Babooshka and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen }, journal = {Information Sciences}, volume = 578, pages = {1--21}, year = 2021, abstract = {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.}, doi = {10.1016/j.ins.2021.07.025}, supplement = {https://doi.org/10.5281/zenodo.5040421} }
@article{ShaPiSha2017:asoco, author = { Weishi Shao and Dechang Pi and Zhongshi Shao }, title = {Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion}, journal = {Applied Soft Computing}, volume = 54, pages = {164--182}, year = 2017 }
@article{ShaPiSha2018:cor, author = { Weishi Shao and Dechang Pi and Zhongshi Shao }, title = {A hybrid discrete teaching-learning based meta-heuristic for solving no-idle flow shop scheduling problem with total tardiness criterion}, journal = {Computers \& Operations Research}, volume = 94, pages = {89--105}, year = 2018 }
@article{ShaShuIsh2023is, doi = {10.1016/j.ins.2022.11.155}, year = 2023, volume = 622, pages = {755--770}, author = {Ke Shang and Tianye Shu and Ishibuchi, Hisao and Yang Nan and Lie Meng Pang}, title = {Benchmarking large-scale subset selection in evolutionary multi-objective optimization}, journal = {Information Sciences} }
@article{ShaSte2019deep, author = { Shavazipour, Babooshka and T. J. Stewart }, title = {Multi-objective optimisation under deep uncertainty}, journal = {Operational Research}, year = 2019, month = sep, abstract = {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.}, doi = {10.1007/s12351-019-00512-1} }
@article{ShaStrSte2020cce, author = { Shavazipour, Babooshka and Jonas Stray and T. J. Stewart }, title = {Sustainable planning in sugar-bioethanol supply chain under deep uncertainty: A case study of {South} {African} sugarcane industry}, journal = {Computers \& Chemical Engineering}, volume = 143, pages = 107091, year = 2020, doi = {10.1016/j.compchemeng.2020.107091}, keywords = {Supply chain management, Multi-objective optimisation, Deep uncertainty, Scenario planning, Renewable energy,}, abstract = {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.} }
@article{ShaSweWan2016taking, author = {Shahriari, B. and Swersky, K. and Wang, Z. and Adams, R. P. and Nando de Freitas }, journal = {Proceedings of the IEEE}, title = {Taking the human out of the loop: A review of {Bayesian} optimization}, year = 2016, number = 1, pages = {148--175}, volume = 104, publisher = {IEEE} }
@article{ShaSweWanAdaFre2016, author = {Bobak Shahriari and Kevin Swersky and Ziyu Wang and Ryan P. Adams and Nando de Freitas }, title = {Taking the Human Out of the Loop: {A} Review of {Bayesian} Optimization}, journal = {Proceedings of the IEEE}, year = 2016, volume = 104, number = 1, pages = {148--175} }
@article{ShiBac2009niching, title = {Niching with derandomized evolution strategies in artificial and real-world landscapes}, author = { Shir, Ofer M. and Thomas B{\"a}ck }, journal = {Natural Computing}, volume = 8, number = 1, pages = {171--196}, year = 2009, doi = {10.1007/s11047-007-9065-5}, publisher = {Springer} }
@article{ShiCebLoz2018space, author = {Shirazi, Abolfazl and Josu Ceberio and Jos{\'e} A. Lozano }, title = {Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions}, journal = { Progress in Aerospace Sciences }, year = 2018, volume = 102, pages = {76--98}, month = oct, doi = {10.1016/j.paerosci.2018.07.007} }
@article{ShiMarDud2008stat, author = {David Shilane and Jarno Martikainen and Sandrine Dudoit and Seppo J. Ovaska}, title = {A general framework for statistical performance comparison of evolutionary computation algorithms}, journal = {Information Sciences}, volume = 178, number = 14, pages = {2870--2879}, year = 2008, doi = {10.1016/j.ins.2008.03.007} }
@article{ShiZha2016, title = {The generalization of {Latin} hypercube sampling}, author = {Shields, Michael D. and Zhang, Jiaxin}, journal = {Reliability Engineering \& System Safety}, year = 2016, pages = {96--108}, volume = 148 }
@article{ShmHoo05:bmc, author = { A. Shmygelska and Holger H. Hoos }, title = {An Ant Colony Optimisation Algorithm for the {2D} and {3D} Hydrophobic Polar Protein Folding Problem}, journal = {BMC Bioinformatics}, year = 2005, volume = 6, pages = 30, doi = {10.1186/1471-2105-6-30} }
@article{SilConRey2023automatic, author = { Silva-Mu\~noz, Mois\'es and Contreras-Bolton, Carlos and Rey, Carlos and Parada, Victor}, title = {Automatic generation of a hybrid algorithm for the maximum independent set problem using genetic programming}, journal = {Applied Soft Computing}, year = 2023, pages = 110474, publisher = {Elsevier}, doi = {10.1016/j.asoc.2023.110474} }
@article{SilFraBer2021, author = { Silva-Mu\~noz, Mois\'es and Alberto Franzin and Hughes Bersini }, title = {Automatic configuration of the {Cassandra} database using irace}, year = 2021, journal = {{PeerJ} Computer Science}, volume = 7, pages = {e634}, doi = {10.7717/peerj-cs.634} }
@article{SilRit2017:cor, author = {Paulo Vitor Silvestrin and Marcus Ritt}, title = {An Iterated Tabu Search for the Multi-compartment Vehicle Routing Problem}, journal = {Computers \& Operations Research}, year = 2017, volume = 81, pages = {192--202} }
@article{SilSubOch2015, author = {Marcos {Melo Silva} and Anand Subramanian and Luiz Satoru Ochi }, title = {An Iterated Local Search Heuristic for the Split Delivery Vehicle Routing Problem}, journal = {Computers \& Operations Research}, year = 2015, volume = 53, pages = {234--249} }
@article{SimChaThi2014:swarm, author = {Olivier Simonin and Fran{\c{c}}ois Charpillet and Eric Thierry}, title = {Revisiting wavefront construction with collective agents: an approach to foraging}, journal = {Swarm Intelligence}, year = 2014, volume = 9, number = 2, pages = {113--138}, doi = {10.1007/s11721-014-0093-3}, keywords = {irace} }
@article{SimHarPae2015ecj, author = {Kevin Sim and Emma Hart and Ben Paechter }, title = {A Lifelong Learning Hyper-heuristic Method for Bin Packing}, volume = 23, number = 1, pages = {37--67}, year = 2015, doi = {10.1162/EVCO_a_00121}, journal = {Evolutionary Computation} }
@article{SimNelSim2011phack, author = {Simmons, Joseph P. and Nelson, Leif D. and Simonsohn, Uri}, title = {False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant}, journal = {Psychological Science}, year = 2011, url = {https://ssrn.com/abstract=1850704}, annote = {Proposed the term p-hacking} }
@article{SimNew1958heur, title = {Heuristic Problem Solving: The Next Advance in Operations Research}, volume = 6, doi = {10.1287/opre.6.1.1}, number = 1, journal = {Operations Research}, author = { Simon, Herbert A. and Newell, Allen}, year = 1958, pages = {1--10} }
@article{SimLebNel2010anchoring, author = {Simmons, Joseph P. and LeBoeuf, Robyn A. and Nelson, Leif D.}, title = {The effect of accuracy motivation on anchoring and adjustment: {Do} people adjust from provided anchors?}, journal = {Journal of Personality and Social Psychology}, year = 2010, volume = 99, number = 6, pages = {917--932}, issn = {1939-1315, 0022-3514}, shorttitle = {The effect of accuracy motivation on anchoring and adjustment}, doi = {10.1037/a0021540}, abstract = {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.}, language = {en} }
@article{Simon1955, author = { Simon, Herbert A. }, title = {A Behavioral Model of Rational Choice}, journal = {The Quarterly Journal of Economics}, volume = 69, number = 1, pages = {99--118}, year = 1955, epub = {http://www.jstor.org/stable/1884852} }
@article{SinIsaTap2011pareto, author = {Singh, Hemant Kumar and Isaacs, Amitay and Ray, Tapabrata }, title = {A {Pareto} Corner Search Evolutionary Algorithm and Dimensionality Reduction in Many-Objective Optimization Problems}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2011, volume = 15, number = 4, pages = {539--556}, abstract = {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}, doi = {10.1109/TEVC.2010.2093579} }
@article{SinPin1998:IIE, author = {Marcos Singer and Michael L. Pinedo}, title = {A Computational Study of Branch and Bound Techniques for Minimizing the Total Weighted Tardiness in Job Shops}, journal = {IIE Transactions}, year = 1998, volume = 30, number = 2, pages = {109--118} }
@article{SinSaxDeb2013asc, author = {Ankur Sinha and Saxena, Dhish Kumar and Kalyanmoy Deb and Ashutosh Tiwari }, title = {Using objective reduction and interactive procedure to handle many-objective optimization problems}, journal = {Applied Soft Computing}, volume = 13, number = 1, pages = {415--427}, year = 2013, doi = {10.1016/j.asoc.2012.08.030}, keywords = {Evolutionary algorithms, Evolutionary multi- and many-objective optimization, Multi-criteria decision making, Machine learning, Interactive optimization}, abstract = {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.} }
@article{SinBahRay2019distance, title = {Distance-based subset selection for benchmarking in evolutionary multi/many-objective optimization}, author = {Singh, Hemant Kumar and Bhattacharjee, Kalyan Shankar and Ray, Tapabrata }, journal = {IEEE Transactions on Evolutionary Computation}, year = 2019, number = 5, pages = {904--912}, volume = 23, publisher = {IEEE} }
@article{SioGag2018:ejor, author = {Sioud, Aymen and Caroline Gagn{\'e} }, title = {Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times}, journal = {European Journal of Operational Research}, volume = 264, number = 1, pages = {66--73}, year = 2018 }
@article{SmaMcCAll2011efficient, title = {Efficient discovery of anti-inflammatory small-molecule combinations using evolutionary computing}, author = {Small, Ben G. and McColl, Barry W. and Allmendinger, Richard and Pahle, J{\"u}rgen and L{\'o}pez-Castej{\'o}n, Gloria and Rothwell, Nancy J. and Joshua D. Knowles and Mendes, Pedro and Brough, David and Kell, Douglas B.}, journal = {Nature Chemical Biology}, volume = 7, number = 12, pages = {902--908}, year = 2011, publisher = {Nature Publishing Group} }
@article{SmiBaaWreLew2014isa, author = { Kate Smith{-}Miles and Baatar, Davaatseren and Wreford, Brendan and Lewis, Rhyd M. R. }, title = {Towards Objective Measures of Algorithm Performance across Instance Space}, journal = {Computers \& Operations Research}, year = 2014, volume = 45, pages = {12--24}, doi = {10.1016/j.cor.2013.11.015}, abstract = {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} }
@article{SmiBow2015:cor, author = { Kate Smith{-}Miles and Simon Bowly}, title = {Generating New Test Instances by Evolving in Instance Space}, journal = {Computers \& Operations Research}, year = 2015, volume = 63, pages = {102--113}, doi = {10.1016/j.cor.2015.04.022}, abstract = {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} }
@article{SmiChrMun2021where, author = { Kate Smith{-}Miles and Jeffrey Christiansen and Mario A. Mu{\~{n}}oz }, title = {Revisiting Where Are the Hard Knapsack Problems? Via {Instance} {Space} {Analysis}}, journal = {Computers \& Operations Research}, year = 2021, volume = 128, pages = 105184, doi = {10.1016/j.cor.2020.105184}, keywords = {0-1 Knapsack problem; Algorithm portfolios; Algorithm selection; Instance difficulty; Instance generation; Instance Space Analysis; Performance evaluation} }
@article{SmiLop2012:cor, author = { Kate Smith{-}Miles and Lopes, Leo}, title = {Measuring instance difficulty for combinatorial optimization problems}, journal = {Computers \& Operations Research}, year = 2012, volume = 39, pages = {875--889} }
@article{SmiMun2023isa, author = { Kate Smith{-}Miles and Mario A. Mu{\~{n}}oz }, title = {Instance Space Analysis for Algorithm Testing: Methodology and Software Tools}, journal = {{ACM} Computing Surveys}, year = 2023, volume = 55, number = 12, month = mar, issue_date = {December 2023}, doi = {10.1145/3572895}, abstract = {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.}, articleno = 255, numpages = 31, keywords = {test instance diversity, benchmarking, timetabling, Algorithm footprints, MATLAB, software as a service, meta-heuristics, algorithm selection, meta-learning} }
@article{Smith-Miles2008, author = { Kate Smith{-}Miles }, title = {Cross-disciplinary Perspectives on Meta-learning for Algorithm Selection}, journal = {{ACM} Computing Surveys}, year = 2008, volume = 41, number = 1, pages = {1--25} }
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@article{Sol2002:tec, author = { Christine Solnon }, title = {Ants Can Solve Constraint Satisfaction Problems}, journal = {IEEE Transactions on Evolutionary Computation}, year = 2002, volume = 6, number = 4, pages = {347--357} }
@article{SolMarMic2008, author = {D. Soler and E. Mart{\'i}nez and J. C. Mic\'o}, title = {A Transformation for the Mixed General Routing Problem with Turn Penalties}, journal = {Journal of the Operational Research Society}, year = 2008, volume = 59, pages = {540--547} }
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@article{SonWanHeJin2021kriging, title = {A {Kriging}-assisted two-archive evolutionary algorithm for expensive many-objective optimization}, author = {Song, Zhenshou and Wang, Handing and He, Cheng and Yaochu Jin }, journal = {IEEE Transactions on Evolutionary Computation}, volume = 25, number = 6, pages = {1013--1027}, year = 2021, publisher = {IEEE} }
@article{Sor2013, title = {Metaheuristics---the metaphor exposed}, author = { Kenneth S{\"o}rensen }, journal = {International Transactions in Operational Research}, year = 2015, volume = 22, number = 1, pages = {3--18}, doi = {10.1111/itor.12001} }
@article{SorArnPal2017, author = { Kenneth S{\"o}rensen and Florian Arnold and Daniel {Palhazi Cuervo}}, title = {A critical analysis of the ``improved {Clarke} and {Wright} savings algorithm''}, journal = {International Transactions in Operational Research}, volume = 26, number = 1, pages = {54--63}, year = 2017, doi = {10.1111/itor.12443}, keywords = {reproducibility, vehicle routing} }
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@article{SouRitLop2021cap, author = { Marcelo {De Souza} and Marcus Ritt and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Capping Methods for the Automatic Configuration of Optimization Algorithms}, journal = {Computers \& Operations Research}, doi = {10.1016/j.cor.2021.105615}, year = 2022, volume = 139, pages = 105615, supplement = {https://github.com/souzamarcelo/supp-cor-capopt}, abstract = {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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@article{TerSumTam2021auto, title = {Black-Box Optimization for Automated Discovery}, author = {Terayama, Kei and Sumita, Masato and Tamura, Ryo and Tsuda, Koji}, year = 2021, month = mar, journal = {Accounts of Chemical Research}, volume = 54, number = 6, pages = {1334--1346}, publisher = {American Chemical Society}, doi = {10.1021/acs.accounts.0c00713}, abstract = {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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@article{TomKad2019decomposition, title = {Decomposition-based interactive evolutionary algorithm for multiple objective optimization}, author = { Tomczyk, Micha{\l} K and Kadzi{\'n}ski, Mi{\l}osz }, journal = {IEEE Transactions on Evolutionary Computation}, volume = 24, number = 2, pages = {320--334}, year = 2019, publisher = {IEEE}, doi = {10.1109/TEVC.2019.2915767}, abstract = {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} }
@article{TomKad2019emosor, author = { Tomczyk, Micha{\l} K and Kadzi{\'n}ski, Mi{\l}osz }, title = {{EMOSOR}: Evolutionary multiple objective optimization guided by interactive stochastic ordinal regression}, journal = {Computers \& Operations Research}, volume = 108, pages = {134--154}, year = 2019, doi = {10.1016/j.cor.2019.04.008}, keywords = {Multiple objective optimization, Interactive evolutionary hybrids, Stochastic ordinal regression, Preference disaggregation, Pairwise comparisons, Active learning}, abstract = {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.} }
@article{TomKad2021ciemod, author = { Tomczyk, Micha{\l} K and Kadzi{\'n}ski, Mi{\l}osz }, title = {Decomposition-based co-evolutionary algorithm for interactive multiple objective optimization}, journal = {Information Sciences}, volume = 549, pages = {178--199}, year = 2021, doi = {10.1016/j.ins.2020.11.030}, keywords = {Evolutionary multiple objective optimization, Co-evolution, Decomposition, Indirect preference information, Preference learning}, abstract = {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.} }
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@article{XinCheChe2018review, author = {Xin, B. and Chen, L. and Chen, J. and Ishibuchi, Hisao and Hirota, K. and Liu, B.}, journal = {{IEEE} Access}, title = {Interactive Multiobjective Optimization: A Review of the State-of-the-Art}, year = 2018, volume = 6, pages = {41256--41279}, doi = {10.1109/ACCESS.2018.2856832}, keywords = {Decision making, Evolutionary computation, Pareto optimization, Evolutionary multiobjective optimization, interactive multiobjective optimization, multiple criteria decision making, preference information, preference models}, abstract = {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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@incollection{AikBurLi2006, year = 2006, volume = 4193, series = {Lecture Notes in Computer Science}, address = { Heidelberg, Germany}, publisher = {Springer}, editor = {Runarsson, Thomas Philip and Hans-Georg Beyer and Edmund K. Burke and Juan-Juli{\'a}n Merelo and Darrell Whitley and Xin Yao }, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {IX}}, author = {Uwe Aickelin and Edmund K. Burke and Jingpeng Li}, title = {Improved Squeaky Wheel Optimisation for Driver Scheduling}, pages = {182--191} }
@incollection{AisRoy2010:isorms, year = 2010, volume = 142, publisher = {Springer, US}, editor = { Matthias Ehrgott and Jos{\'e} Rui Figueira and Salvatore Greco }, series = {International Series in Operations Research \& Management Science}, booktitle = {Trends in Multiple Criteria Decision Analysis}, author = { Hassene Aissi and Bernard Roy }, title = {Robustness in Multi-criteria Decision Aiding}, chapter = 4, pages = {87--121} }
@incollection{AkiSanYan2019optuna, key = {SIGKDD}, month = jul, address = { New York, NY}, publisher = {ACM Press}, year = 2019, editor = {Teredesai and others}, booktitle = {25th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining}, doi = {10.1145/3292500.3330701}, author = {Takuya Akiba and Shotaro Sano and Toshihiko Yanase and Takeru Ohta and Masanori Koyama}, title = {Optuna: A Next-generation Hyperparameter Optimization Framework}, pages = {2623--2631} }
@techreport{AktAtaGur2007conic, author = {S. M. Akt{\"u}rk and Alper Atamt{\"u}rk and S. G{\"u}rel}, title = {A Strong Conic Quadratic Reformulation for Machine-Job Assignment with Controllable Processing Times}, institution = {University of California-Berkeley}, year = 2007, type = {Research Report}, number = {BCOL.07.01} }
@incollection{AlaSolGhe07, author = {I. Alaya and Christine Solnon and Khaled Gh{\'e}dira}, title = {Ant Colony Optimization for Multi-Objective Optimization Problems}, booktitle = {19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007)}, year = 2007, volume = 1, publisher = {IEEE Computer Society Press}, address = {Los Alamitos, CA}, pages = {450--457} }
@inproceedings{AlaSolGhe2004:bioma, url = {https://books.google.be/books?id=0ZLsAAAACAAJ}, editor = {Bogdan Filipi{\v c} and Jurij {\v S}ilc }, year = 2004, booktitle = {International Conference on Bioinspired Optimization Methods and their Applications (BIOMA 2004)}, author = {I. Alaya and Christine Solnon and Khaled Gh{\'e}dira}, title = {Ant algorithm for the multi-dimensional knapsack problem}, pages = {63--72} }
@incollection{AlbChi2007gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2007, editor = {Dirk Thierens and others}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007}, author = { Alba, Enrique and Chicano, Francisco }, title = {{ACOhg}: dealing with huge graphs}, pages = {10--17}, doi = {10.1145/1276958.1276961} }
@incollection{AliSimHar2019, doi = {10.1145/3321707}, isbn = {978-1-4503-6111-8}, address = { New York, NY}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019}, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, author = {Alissa, Mohamad and Sim, Kevin and Emma Hart }, title = {Algorithm Selection Using Deep Learning without Feature Extraction}, pages = {198--206} }
@incollection{AllBurHyd2009reusable, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009}, address = { New York, NY}, year = 2009, publisher = {ACM Press}, editor = { Franz Rothlauf }, author = {Allen, Sam and Edmund K. Burke and Matthew R. Hyde and Graham Kendall }, title = {Evolving reusable 3d packing heuristics with genetic programming}, pages = {931--938}, doi = {10.1145/1569901.1570029}, keywords = {hyper-heuristic} }
@incollection{AllKno2010variables, volume = 6238, year = 2010, address = { Heidelberg, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, booktitle = {Parallel Problem Solving from Nature, PPSN XI}, author = { Allmendinger, Richard and Joshua D. Knowles }, title = {Evolutionary Optimization on Problems Subject to Changes of Variables}, editor = {Schaefer, Robert and Carlos Cotta and Ko{\l}odziej, Joanna and G{\"u}nther Rudolph }, pages = {151--160}, abstract = {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} }
@inproceedings{AllKno2011ecta, author = { Allmendinger, Richard and Joshua D. Knowles }, title = {Evolutionary Search in Lethal Environments}, booktitle = {International Conference on Evolutionary Computation Theory and Applications}, year = 2011, pages = {63--72}, publisher = {SciTePress}, doi = {10.5220/0003673000630072}, epub = {https://www.scitepress.org/papers/2011/36730/36730.pdf} }
@incollection{AllKno2011policy, address = { New York, NY}, publisher = {ACM Press}, year = 2011, editor = {Natalio Krasnogor and Pier Luca Lanzi}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011}, author = { Allmendinger, Richard and Joshua D. Knowles }, title = {Policy Learning in Resource-Constrained Optimization}, pages = {1971--1979}, doi = {10.1145/2001576.2001841}, abstract = {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({$\lambda$}), 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.}, isbn = {978-1-4503-0557-0}, langid = {english} }
@inproceedings{AllMouLiu2019human, year = 2019, publisher = {{AAAI} Press}, booktitle = {Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference}, editor = {Roman Bart{\'{a}}k and Keith W. Brawner}, author = {Joseph Allen and Ahmed Moussa and Xudong Liu}, title = {Human-in-the-Loop Learning of Qualitative Preference Models}, pages = {108--111}, doi = {10.48550/arXiv.1909.09064} }
@phdthesis{Allmendinger2012phd, author = { Allmendinger, Richard }, title = {Tuning Evolutionary Search for Closed-Loop Optimization}, school = {The University of Manchester, UK}, year = 2012, month = jan }
@inproceedings{AlsTsa2009, address = {Hamburg, Germany}, publisher = {University of Hamburg}, editor = {M. Caserta and Stefan Vo{\ss} }, year = 2010, booktitle = {Proceedings of MIC 2009, the 8th Metaheuristics International Conference}, title = {Guided {Pareto} local search and its application to the 0/1 multi-objective knapsack problems}, author = {Alsheddy, A. and Tsang, E.} }
@inproceedings{AmaAliThr2019nips, year = 2019, editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman Garnett}, booktitle = {Advances in Neural Information Processing Systems (NeurIPS 32)}, title = {Linear Stochastic Bandits Under Safety Constraints}, author = {Amani, Sanae and Alizadeh, Mahnoosh and Thrampoulidis, Christos}, pages = {9256--9266}, epub = {http://papers.nips.cc/paper/9124-linear-stochastic-bandits-under-safety-constraints.pdf} }
@incollection{AndVidIve1993, publisher = {Springer}, year = 1993, editor = { Vidal, Ren{\'e} Victor Valqui }, booktitle = {Applied Simulated Annealing}, title = {Design of a Teleprocessing Communication Network Using Simulated Annealing}, author = { Klaus Andersen and Vidal, Ren{\'e} Victor Valqui and Villy B{\ae}k Iversen }, pages = {201--215} }
@incollection{Andersen99, author = { J. H. Andersen and R. S. Powell }, title = {The Use of Continuous Decision Variables in an Optimising Fixed Speed Pump Scheduling Algorithm}, booktitle = {Computing and Control for the Water Industry}, pages = {119--128}, publisher = { Research Studies Press Ltd. }, year = 1999, editor = { R. S. Powell and K. S. Hindi } }
@incollection{AngBocPaoVec08, year = 2008, volume = 5361, series = {Lecture Notes in Computer Science}, address = { Heidelberg, Germany}, publisher = {Springer}, editor = {X. Li and others}, fulleditor = {X. Li and M. Kirley and M. Zhang and D. G. Green and V. Ciesielski and Abbass, Hussein A. and Z. Michalewicz and T. Hendtlass and Kalyanmoy Deb and Tan, Kay Chen and J{\"u}rgen Branke and Y. Shi}, booktitle = {Simulated Evolution and Learning, 7th International Conference, SEAL 2008}, title = {Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling}, author = {D. Anghinolfi and A. Boccalatte and M. Paolucci and C. Vecchiola}, pages = {411--420} }
@incollection{Angus2007, editor = {Marcus Randall and Abbass, Hussein A. and Janet Wiles}, volume = 4828, series = {Lecture Notes in Computer Science}, address = { Heidelberg, Germany}, publisher = {Springer}, year = 2007, booktitle = {Progress in Artificial Life (ACAL)}, author = { Daniel Angus }, title = {Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation}, pages = {232--244}, doi = {10.1007/978-3-540-76931-6_21} }
@inproceedings{AnsKamVeeRag2014open, year = 2014, address = { New York, NY}, publisher = {ACM Press}, booktitle = {Proceedings of the 23rd International Conference on Parallel Architectures and Compilation}, key = {PACT}, author = {J. Ansel and S. Kamil and K. Veeramachaneni and J. Ragan-Kelley and J. Bosboom and Una-May O'Reilly and S. Amarasinghe}, title = {{OpenTuner}: An extensible framework for program autotuning}, pages = {303--315}, doi = {10.1145/2628071.2628092} }
@inproceedings{AnsMalSamSelTie2015:ijcai, publisher = {IJCAI/AAAI Press, Menlo Park, CA}, editor = {Qiang Yang and Michael Wooldridge}, year = 2015, booktitle = {Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15)}, author = { Carlos Ans{\'o}tegui and Yuri Malitsky and Horst Samulowitz and Meinolf Sellmann and Kevin Tierney }, title = {Model-Based Genetic Algorithms for Algorithm Configuration}, pages = {733--739}, keywords = {GGA++}, epub = {https://www.ijcai.org/Abstract/15/109} }
@inproceedings{AnsMalSel2014isacpp, year = 2014, publisher = {{AAAI} Press}, booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence}, editor = {David Stracuzzi and others}, author = { Carlos Ans{\'o}tegui and Yuri Malitsky and Meinolf Sellmann }, title = {{MaxSAT} by Improved Instance-Specific Algorithm Configuration}, pages = {2594--2600} }
@incollection{AnsSelTie2009cp, year = 2009, volume = 5732, series = {Lecture Notes in Computer Science}, address = { Heidelberg, Germany}, publisher = {Springer}, booktitle = {Principles and Practice of Constraint Programming, CP 2009}, editor = { Ian P. Gent }, author = { Carlos Ans{\'o}tegui and Meinolf Sellmann and Kevin Tierney }, title = {A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms}, pages = {142--157}, doi = {10.1007/978-3-642-04244-7_14}, keywords = {GGA} }
@techreport{AppBixChvCoo95:tr, author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook }, title = {Finding Cuts in the {TSP}}, institution = {DIMACS Center, Rutgers University, Piscataway, NJ, USA}, year = 1995, number = {95--05}, month = mar }
@techreport{AppBixChvCoo99:tr, author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook }, title = {Finding Tours in the {TSP}}, institution = {Forschungsinstitut f{\"u}r Diskrete Mathematik, University of Bonn, Germany}, year = 1999, number = 99885 }
@book{AppEtAl06, author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook }, title = {The Traveling Salesman Problem: A Computational Study}, publisher = {Princeton University Press, Princeton, NJ}, year = 2006 }
@inproceedings{AprGloKel2003, volume = 1, month = dec, address = { New York, NY}, publisher = {ACM Press}, editor = {Stephen E. Chick and Paul J. Sanchez and David M. Ferrin and Douglas J. Morrice}, year = 2003, booktitle = {Proceedings of the 35th Winter Simulation Conference: Driving Innovation}, author = { Jay April and Fred Glover and James P. Kelly and Manuel Laguna }, title = {Simulation-based optimization: Practical introduction to simulation optimization}, pages = {71--78}, doi = {10.1109/WSC.2003.1261410} }
@book{AroBar2009, title = {Computational complexity: a modern approach}, author = {Arora, Sanjeev and Barak, Boaz}, year = 2009, publisher = {Cambridge University Press} }
@incollection{ArzCebPer2019qap, isbn = {978-1-4503-6748-6}, address = { New York, NY}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019}, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, author = {Etor Arza and Josu Ceberio and Aritz P{\'{e}}rez and Irurozki, Ekhine }, title = {Approaching the quadratic assignment problem with kernels of mallows models under the hamming distance}, doi = {10.1145/3319619.3321976}, keywords = {QAP, EDA, Mallows} }
@incollection{AsaIwaMiy96, series = {{DIMACS} Series on Discrete Mathematics and Theoretical Computer Science}, volume = 26, year = 1996, address = { Providence, RI}, publisher = {American Mathematical Society}, booktitle = {Cliques, Coloring, and Satisfiability: Second {DIMACS} Implementation Challenge}, editor = {David S. Johnson and Michael A. Trick }, author = {Y. Asahiro and K. Iwama and E. Miyano}, title = {Random Generation of Test Instances with Controlled Attributes}, pages = {377--393} }
@phdthesis{Asch95PhD, author = { N. Ascheuer }, title = {Hamiltonian Path Problems in the On-line Optimization of Flexible Manufacturing Systems}, school = {Technische Universit{\"a}t Berlin}, year = 1995, address = {Berlin, Germany} }
@incollection{Atkinson00, author = { R. Atkinson and Jakobus E. van Zyl and Godfrey A. Walters and Dragan A. Savic }, title = {Genetic algorithm optimisation of level-controlled pumping station operation}, booktitle = {Water network modelling for optimal design and management}, pages = {79--90}, publisher = {Centre for Water Systems, Exeter, UK}, year = 2000 }
@incollection{AudDanOrb10, editor = {K. Naono and K. Teranishi and J. Cavazos and R. Suda}, year = 2010, publisher = {Springer}, booktitle = {Software Automatic Tuning: From Concepts to State-of-the-Art Results}, author = { Charles Audet and Cong-Kien Dang and Dominique Orban }, title = {Algorithmic Parameter Optimization of the {DFO} Method with the {OPAL} Framework}, pages = {255--274} }
@incollection{AugBadBroZit2009gecco, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009}, address = { New York, NY}, year = 2009, publisher = {ACM Press}, editor = { Franz Rothlauf }, author = { Anne Auger and Johannes Bader and Dimo Brockhoff and Eckart Zitzler }, title = {Articulating User Preferences in Many-Objective Problems by Sampling the Weighted Hypervolume}, pages = {555--562} }
@incollection{AugBadBroZit2009gecco2, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009}, address = { New York, NY}, year = 2009, publisher = {ACM Press}, editor = { Franz Rothlauf }, author = { Anne Auger and Johannes Bader and Dimo Brockhoff and Eckart Zitzler }, title = {Investigating and Exploiting the Bias of the Weighted Hypervolume to Articulate User Preferences}, pages = {563--570} }
@incollection{AugBadBroZit2009hv, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009}, address = { New York, NY}, year = 2009, publisher = {ACM Press}, editor = { Franz Rothlauf }, title = {Theory of the hypervolume indicator: optimal $\mu$-distributions and the choice of the reference point}, author = { Anne Auger and Johannes Bader and Dimo Brockhoff and Eckart Zitzler }, pages = {87--102} }
@incollection{AugBroLop2012dagstuhl, doi = {10.4230/DagRep.2.1.50}, series = {Dagstuhl Reports}, volume = {2(1)}, year = 2012, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Germany}, booktitle = {Learning in Multiobjective Optimization (Dagstuhl Seminar 12041)}, editor = { Salvatore Greco and Joshua D. Knowles and Kaisa Miettinen and Eckart Zitzler }, author = { Anne Auger and Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Kaisa Miettinen and Boris Naujoks and G{\"u}nther Rudolph }, title = {Which questions should be asked to find the most appropriate method for decision making and problem solving? ({Working} {Group} ``{Algorithm} {Design} {Methods}'')}, pages = {92--93} }
@book{AugDoe2011, editor = { Anne Auger and Benjamin Doerr }, title = {Theory of Randomized Search Heuristics: Foundations and Recent Developments}, series = {Series on Theoretical Computer Science}, volume = 1, publisher = {World Scientific Publishing Co., Singapore}, year = 2011 }
@inproceedings{AugHan2005cec, address = {Piscataway, NJ}, publisher = {IEEE Press}, month = sep, year = 2005, booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005)}, key = {IEEE CEC}, author = { Anne Auger and Nikolaus Hansen }, title = {A restart {CMA} evolution strategy with increasing population size}, pages = {1769--1776}, doi = {10.1109/CEC.2005.1554902}, keywords = {IPOP-CMA-ES} }
@inproceedings{AugHan2005lrcmaes, address = {Piscataway, NJ}, publisher = {IEEE Press}, month = sep, year = 2005, booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005)}, key = {IEEE CEC}, author = { Anne Auger and Nikolaus Hansen }, title = {Performance evaluation of an advanced local search evolutionary algorithm}, pages = {1777--1784}, keywords = {LR-CMAES} }
@incollection{AvrAllLop2021evo, volume = {12694}, series = {Lecture Notes in Computer Science}, address = { Cham, Switzerland}, publisher = {Springer}, booktitle = {Applications of Evolutionary Computation}, year = 2021, editor = {Pedro Castillo and Jim{\'e}nez Laredo, Juan Luis }, author = { Andreea Avramescu and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {A Multi-Objective Multi-Type Facility Location Problem for the Delivery of Personalised Medicine}, pages = {388--403}, doi = {10.1007/978-3-030-72699-7_25}, abstract = {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.}, supplement = {https://doi.org/10.5281/zenodo.4495162}, keywords = {Personalised medicine, Biopharmaceuticals Supply chain, Facility location-allocation, Evolutionary multi-objective optimisation} }
@incollection{AydYavOzyYasStu2017, address = { New York, NY}, publisher = {ACM Press}, year = 2017, editor = { Peter A. N. Bosman }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2017}, author = { Do\v{g}an Ayd{\i}n and G{\"{u}}rcan Yavuz and Serdar \"Ozy\"on and Celal Yasar and Thomas St{\"u}tzle }, title = {Artificial Bee Colony Framework to Non-convex Economic Dispatch Problem with Valve Point Effects: A Case Study}, pages = {1311--1318} }
@incollection{AyoAllLop2023gecco, location = {Lisbon, Portugal}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2023}, annote = {ISBN: 979-8-4007-0120-7}, address = { New York, NY}, year = 2023, publisher = {ACM Press}, editor = {Silva, Sara and Lu{\'i}s Paquete }, author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu and Arnaud Liefooghe }, title = {Applying {Ising} Machines to Multi-Objective {QUBOs}}, pages = {2166--2174}, doi = {10.1145/3583133.3596312}, abstract = {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.}, numpages = 9, keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO} }
@incollection{AyoAllLop2022gecco, location = {Boston, Massachusetts}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2022}, address = { New York, NY}, year = 2022, publisher = {ACM Press}, editor = { Jonathan E. Fieldsend and Markus Wagner }, author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu }, title = {Multi-Objective {QUBO} Solver: Bi-Objective Quadratic Assignment Problem}, pages = {467--475}, doi = {10.1145/3512290.3528698}, abstract = {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.}, numpages = 9, keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO} }
@incollection{AyoAllLop2022or, booktitle = {Operations Research Proceedings 2022, OR 2022}, address = { Cham, Switzerland}, series = {Lecture Notes in Operations Research}, year = 2022, publisher = {Springer}, editor = {Oliver Grothe and Stefan Nickel and Steffen Rebennack and Oliver Stein}, author = { Ayodele, Mayowa and Allmendinger, Richard and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Parizy, Matthieu }, title = {A Study of Scalarisation Techniques for Multi-objective {QUBO} Solving}, pages = {393--399}, doi = {10.1007/978-3-031-24907-5_47} }
@incollection{Ayodele2022penalty, address = { Cham, Switzerland}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, year = 2022, booktitle = {Proceedings of EvoCOP 2022 -- 22nd European Conference on Evolutionary Computation in Combinatorial Optimization }, editor = { P{\'e}rez C{\'a}ceres, Leslie and Verel, S{\'e}bastien }, title = {Penalty Weights in {QUBO} Formulations: Permutation Problems}, author = { Ayodele, Mayowa }, pages = {159--174} }
@incollection{AziDoeDre2021, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021}, address = { New York, NY}, year = 2021, publisher = {ACM Press}, editor = { Chicano, Francisco and Krzysztof Krawiec }, author = {Aziz-Alaoui, Amine and Carola Doerr and Johann Dr{\'e}o }, title = {Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks}, pages = {1365--1374}, doi = {10.1145/3449726.3463155} }
@misc{BBCOMP2017, title = {Black Box Optimization Competition}, author = {Ilya Loshchilov and T. Glasmachers }, year = 2017, url = {https://bbcomp.ini.rub.de/} }
@misc{BBOB2016bi, author = { Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Dejan Tusar and Tea Tu{\v s}ar and Tobias Wagner }, title = {{GECCO} Workshop on Real-Parameter Black-Box Optimization Benchmarking ({BBOB} 2016): Focus on multi-objective problems}, howpublished = {\url{https://numbbo.github.io/workshops/BBOB-2016/}}, year = 2016 }
@incollection{ZitLauBleu2004tutorial, year = 2004, address = {Berlin\slash Heidelberg}, publisher = {Springer}, volume = 535, series = {Lecture Notes in Economics and Mathematical Systems}, editor = { Xavier Gandibleux and Marc Sevaux and Kenneth S{\"o}rensen and V. {T'Kindt} }, booktitle = {Metaheuristics for Multiobjective Optimisation}, title = {A tutorial on evolutionary multiobjective optimization}, author = { Eckart Zitzler and Marco Laumanns and S. Bleuler }, pages = {3--37} }
@incollection{BLTZ2003a, address = { Heidelberg, Germany}, publisher = {Springer}, volume = 2632, series = {Lecture Notes in Computer Science}, editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele }, year = 2003, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003}, author = { S. Bleuler and Marco Laumanns and Lothar Thiele and Eckart Zitzler }, title = {{PISA} -- A Platform and Programming Language Independent Interface for Search Algorithms }, pages = {494--508} }
@misc{Bab2008spear, author = { Domagoj Babi{\'c} }, title = {Spear theorem prover}, howpublished = {\url{https://www.domagoj-babic.com/index.php/ResearchProjects/Spear}}, year = 2008 }
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@misc{BezLopStu2013:lion-supp, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Deconstructing Multi-Objective Evolutionary Algorithms: An Iterative Analysis on the Permutation Flowshop: Supplementary material}, howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/}}, year = 2013 }
@incollection{BezLopStu2013evocop, address = { Heidelberg, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, year = 2013, volume = 7832, booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization }, editor = { Martin Middendorf and Christian Blum }, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {An Analysis of Local Search for the Bi-objective Bidimensional Knapsack Problem}, pages = {85--96}, doi = {10.1007/978-3-642-37198-1_8} }
@techreport{BezLopStu2014:automoeaTR, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatic Com\-ponent-Wise Design of Multi-Objective Evolutionary Algorithms}, institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium}, year = 2014, number = {TR/IRIDIA/2014-012}, month = aug }
@incollection{BezLopStu2014:lion, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = 8426, booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8}, publisher = {Springer}, year = 2014, editor = { Panos M. Pardalos and Mauricio G. C. Resende and Chrysafis Vogiatzis and Jose L. Walteros}, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Deconstructing Multi-Objective Evolutionary Algorithms: An Iterative Analysis on the Permutation Flowshop}, pages = {57--172}, doi = {10.1007/978-3-319-09584-4_16}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/} }
@incollection{BezLopStu2014:ppsn, year = 2014, volume = 8672, address = { Heidelberg, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith}, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}}, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization}, doi = {10.1007/978-3-319-10762-2_50}, pages = {508--517} }
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@misc{BezLopStu2015:supp, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatic Component-Wise Design of Multi-Objective Evolutionary Algorithms}, howpublished = {\url{https://github.com/iridia-ulb/automoea-tevc-2016}}, year = 2015 }
@misc{BezLopStu2015emoDEsupp, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {To {DE} or Not to {DE}? {Multi}-objective Differential Evolution Revisited from a Component-Wise Perspective: {Supplementary} material}, howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2015-001/}}, year = 2015 }
@incollection{BezLopStu2015emode, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}}, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = 9018, year = 2015, publisher = {Springer}, editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} }, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {To {DE} or Not to {DE}? {Multi}-objective Differential Evolution Revisited from a Component-Wise Perspective}, pages = {48--63}, doi = {10.1007/978-3-319-15934-8_4} }
@incollection{BezLopStu2015moead, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}}, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = 9018, year = 2015, publisher = {Springer}, editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} }, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Comparing De\-com\-po\-sition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms}, pages = {396--410}, doi = {10.1007/978-3-319-15934-8_27} }
@misc{BezLopStu2016supp, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms}, howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2015-007/}}, year = 2017 }
@techreport{BezLopStu2017:techreport-005, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms}, institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium}, year = 2017, number = {TR/IRIDIA/2017-005}, month = feb }
@incollection{BezLopStu2017emo, editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu Jin and Christian Grimme}, year = 2017, volume = 10173, series = {Lecture Notes in Computer Science}, address = { Cham, Switzerland}, publisher = {Springer International Publishing}, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2017}, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {An Empirical Assessment of the Properties of Inverted Generational Distance Indicators on Multi- and Many-objective Optimization}, pages = {31--45}, doi = {10.1007/978-3-319-54157-0_3} }
@misc{BezLopStu2019ec-supp, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms: Supplementary material}, howpublished = {\url{https://github.com/iridia-ulb/automoea-ecj-2020}}, year = 2019 }
@inproceedings{WanSunJin2019multi, year = 2019, address = {Piscataway, NJ}, publisher = {IEEE Press}, booktitle = {Proceedings of the 2019 Congress on Evolutionary Computation (CEC 2019)}, key = {IEEE CEC}, title = {A Multi-indicator based Selection Strategy for Evolutionary Many-objective Optimization}, author = { Wang, Hao and Sun, Chaoli and Yaochu Jin and Qin, Shufen and Yu, Haibo}, pages = {2042--2049}, annote = {unbounded archive} }
@incollection{BezLopStu2019gecco, isbn = {978-1-4503-6111-8}, address = { New York, NY}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019}, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Archiver Effects on the Performance of State-of-the-art Multi- and Many-objective Evolutionary Algorithms}, supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/}, doi = {10.1145/3321707.3321789} }
@misc{BezLopStu2019gecco-supp, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Archiver Effects on the Performance of State-of-the-art Multi- and Many-objective Evolutionary Algorithms: Supplementary material}, howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2019-004/}}, year = 2019 }
@incollection{BezLopStu2020chapter, address = { Cham, Switzerland}, publisher = {Springer International Publishing}, editor = { Thomas Bartz-Beielstein and Bogdan Filipi{\v c} and P. Koro{\v s}ec and Talbi, El-Ghazali }, year = 2020, booktitle = {High-Performance Simulation-Based Optimization}, author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle }, title = {Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration}, pages = {69--92}, doi = {10.1007/978-3-030-18764-4_4}, abstract = {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.} }
@phdthesis{Bezerra2016PhD, author = { Leonardo C. T. Bezerra }, title = {A component-wise approach to multi-objective evolutionary algorithms: from flexible frameworks to automatic design}, school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium}, year = 2016, annote = {Supervised by Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez } }
@incollection{BiaGamDor02:ppsn, anote = {IC.34}, address = { Heidelberg, Germany}, publisher = {Springer}, editor = { Juan-Juli{\'a}n Merelo and others}, aeditor = { Juan-Juli{\'a}n Merelo and P. Adamidis and Hans-Georg Beyer and J.-L. Fern\'{a}ndez-Villacanas and Hans-Paul Schwefel }, volume = 2439, series = {Lecture Notes in Computer Science}, year = 2002, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {VII}}, author = { Leonora Bianchi and L. M. Gambardella and Marco Dorigo }, title = {An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem}, pages = {883--892} }
@inproceedings{Bie14:sat, publisher = {University of Helsinki}, series = {Science Series of Publications B}, volume = {B-2014-2}, year = 2014, editor = {A. Belov and D. Diepold and M. Heule and M. J\"{a}rvisalo}, booktitle = {Proceedings of SAT Competition 2014: Solver and Benchmark Descriptions}, title = {Yet another Local Search Solver and {Lingeling} and Friends Entering the {SAT} Competition 2014}, author = {Armin Biere}, pages = {39--40} }
@incollection{BieBozEim2020dynaac, publisher = {IOS Press}, editor = {Giuseppe De Giacomo and Alejandro Catala and Bistra Dilkina and Michela Milano and Senén Barro and Alberto BugarÃn and Jérôme Lang}, series = {Frontiers in Artificial Intelligence and Applications}, volume = 325, year = 2020, booktitle = {Proceedings of the 24th European Conference on Artificial Intelligence (ECAI)}, author = { Biedenkapp, Andr{\'e} and Bozkurt, H. Furkan and Eimer, Theresa and Frank Hutter and Marius Thomas Lindauer }, title = {Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework}, epub = {https://ecai2020.eu/papers/1237_paper.pdf}, pages = {427--434} }
@incollection{BieLinEggFraFawHoo2017, publisher = {{AAAI} Press}, month = feb, year = 2017, editor = {Satinder P. Singh and Shaul Markovitch}, booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence}, author = { Biedenkapp, Andr{\'e} and Marius Thomas Lindauer and Katharina Eggensperger and Frank Hutter and Chris Fawcett and Holger H. Hoos }, title = {Efficient Parameter Importance Analysis via Ablation with Surrogates}, doi = {10.1609/aaai.v31i1.10657} }
@incollection{BieMarLinHut2018lion, address = { Cham, Switzerland}, series = {Lecture Notes in Computer Science}, volume = 11353, booktitle = {Learning and Intelligent Optimization, 12th International Conference, LION 12}, publisher = {Springer}, year = 2018, editor = { Roberto Battiti and Mauro Brunato and Ilias Kotsireas and Panos M. Pardalos }, author = { Biedenkapp, Andr{\'e} and Marben, Joshua and Marius Thomas Lindauer and Frank Hutter }, title = {{CAVE}: Configuration assessment, visualization and evaluation}, pages = {115--130}, doi = {10.1007/978-3-030-05348-2_10} }
@incollection{BilPar1995:aisb, booktitle = {Evolutionary Computing, AISB Workshop}, address = { Berlin, Germany}, series = {Lecture Notes in Computer Science}, volume = 993, year = 1995, publisher = {Springer}, editor = {T. C. Fogarty}, title = {The Ant Colony Metaphor for Searching Continuous Design Spaces}, author = {George Bilchev and Ian C. Parmee}, pages = {25--39}, doi = {10.1007/3-540-60469-3_22} }
@incollection{BirBalDor06, address = { New York, NY}, series = {Operations Research/Computer Science Interfaces Series}, volume = 39, editor = {K. F. Doerner and M. Gendreau and P. Greistorfer and W. J. Gutjahr and R. F. Hartl and M. Reimann}, year = 2006, publisher = {Springer}, booktitle = {Metaheuristics -- Progress in Complex Systems Optimization}, author = { Mauro Birattari and Prasanna Balaprakash and Marco Dorigo }, title = {The {ACO/F-RACE} algorithm for combinatorial optimization under uncertainty}, pages = {189--203} }
@incollection{BirChiSaeStu2011, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, booktitle = {Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2011}, publisher = {Springer}, year = 2011, editor = {T. Berthold and A. M. Gleixner and S. Heinz and T. Koch}, author = { Mauro Birattari and Marco Chiarandini and Marco Saerens and Thomas St{\"u}tzle }, title = {Learning Graphical Models for Algorithm Configuration} }
@incollection{BirDicDor02:ants, address = { Heidelberg, Germany}, publisher = {Springer}, editor = { Marco Dorigo and others}, fulleditor = { Marco Dorigo and Gianni A. {Di Caro} and M. Sampels }, volume = 2463, series = {Lecture Notes in Computer Science}, year = 2002, booktitle = {Ant Algorithms, Third International Workshop, ANTS 2002}, author = { Mauro Birattari and Gianni A. {Di Caro} and Marco Dorigo }, title = {Toward the formal foundation of Ant Programming}, pages = {188--201} }
@book{BirKleLop2009nltk, title = {Natural language processing with {Python}: analyzing text with the natural language toolkit}, author = {Bird, Steven and Klein, Ewan and Loper, Edward}, year = 2009, publisher = {O'Reilly Media, Inc.} }
@incollection{BirStuPaqVar02:gecco, publisher = {Morgan Kaufmann Publishers, San Francisco, CA}, editor = { Langdon, William B. and others}, year = 2002, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2002}, author = { Mauro Birattari and Thomas St{\"u}tzle and Lu{\'i}s Paquete and Klaus Varrentrapp }, title = {A Racing Algorithm for Configuring Metaheuristics}, pages = {11--18}, keywords = {F-race}, epub = {https://dl.acm.org/doi/pdf/10.5555/2955491.2955494} }
@incollection{BirYuaBal2010:emaoa, editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss }, year = 2010, address = { Berlin, Germany}, publisher = {Springer}, booktitle = {Experimental Methods for the Analysis of Optimization Algorithms}, author = { Mauro Birattari and Zhi Yuan and Prasanna Balaprakash and Thomas St{\"u}tzle }, title = {{F}-Race and Iterated {F}-Race: An Overview}, pages = {311--336}, keywords = {F-race, iterated F-race, irace, tuning}, doi = {10.1007/978-3-642-02538-9_13} }
@inproceedings{BirYuaBalStu2010:mic, address = {Hamburg, Germany}, publisher = {University of Hamburg}, editor = {M. Caserta and Stefan Vo{\ss} }, year = 2010, booktitle = {Proceedings of MIC 2009, the 8th Metaheuristics International Conference}, author = { Mauro Birattari and Zhi Yuan and Prasanna Balaprakash and Thomas St{\"u}tzle }, title = {Parameter Adaptation in Ant Colony Optimization} }
@book{Birattari09tuning, title = {Tuning Metaheuristics: A Machine Learning Perspective}, doi = {10.1007/978-3-642-00483-4}, author = { Mauro Birattari }, year = 2009, volume = 197, series = {Studies in Computational Intelligence}, publisher = {Springer}, address = {Berlin\slash Heidelberg}, annote = {Based on the PhD thesis~\cite{Birattari2004PhD}} }
@phdthesis{Birattari2004PhD, author = { Mauro Birattari }, title = {The Problem of Tuning Metaheuristics as Seen from a Machine Learning Perspective}, school = {IRIDIA, {\'E}cole polytechnique, Universit{\'e} Libre de Bruxelles, Belgium}, year = 2004, annote = {Supervised by Marco Dorigo} }
@inproceedings{BisIzzYam2010:pagmo, title = {A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation}, author = {Biscani, Francesco and Dario Izzo and Yam, Chit Hong}, booktitle = {Astrodynamics Tools and Techniques (ICATT 2010), 4th International Conference on}, year = 2010, url = {http://arxiv.org/abs/1004.3824}, keywords = {PaGMO} }
@incollection{BisMerTraPre12:gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2012, editor = {Terence Soule and Jason H. Moore}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012}, author = { Bernd Bischl and Olaf Mersmann and Heike Trautmann and Mike Preuss }, title = {Algorithm Selection Based on Exploratory Landscape Analysis and Cost-sensitive Learning}, pages = {313--320}, keywords = {continuous optimization, landscape analysis, algorithm selection} }
@book{Bishop2006, title = {Pattern recognition and machine learning}, author = {Bishop, Christopher M.}, year = 2006, publisher = {Springer} }
@inproceedings{BiyMarAli2019acc, year = 2019, publisher = {{IEEE}}, booktitle = {2019 American Control Conference ({ACC})}, key = {ACC2019}, author = {Erdem B{\i }y{\i }k and Jonathan Margoliash and Shahrouz Ryan Alimo and Dorsa Sadigh}, title = {Efficient and Safe Exploration in Deterministic {Markov} Decision Processes with Unknown Transition Models}, pages = {1792--1799}, doi = {10.23919/ACC.2019.8815276} }
@incollection{BleBlu04:disjoint, aeditor = { G{\"u}nther R. Raidl and S. Cagnoni and J{\"u}rgen Branke and D. W. Corne and R. Drechsler and Y. Jin and C. G. Johnson and Penousal Machado and E. Marchiori and R. Rothlauf and G. D. Smith and G. Squillero}, booktitle = {Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2004}, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = 3005, year = 2004, publisher = {Springer}, editor = { G{\"u}nther R. Raidl and others}, author = { Mar{\'i}a J. Blesa and Christian Blum }, title = {Ant Colony Optimization for the Maximum Edge-Disjoint Paths Problem}, pages = {160--169} }
@inproceedings{BliMcDPer2006emnlp, year = 2006, editor = {Jurafsky, Dan and Gaussier, Eric}, series = {Empirical Methods in Natural Language Processing}, booktitle = {Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP2006}, title = {Domain adaptation with structural correspondence learning}, author = {Blitzer, John and McDonald, Ryan and Pereira, Fernando}, pages = {120--128} }
@incollection{BloHooJouKesTra2016:lion, address = { Cham, Switzerland}, series = {Lecture Notes in Computer Science}, volume = 10079, booktitle = {Learning and Intelligent Optimization, 10th International Conference, LION 10}, publisher = {Springer}, year = 2016, editor = {Paola Festa and Meinolf Sellmann and Joaquin Vanschoren }, author = { Aymeric Blot and Holger H. Hoos and Laetitia Jourdan and Marie-El{\'e}onore Kessaci-Marmion and Heike Trautmann }, title = {{MO-ParamILS}: A Multi-objective Automatic Algorithm Configuration Framework}, pages = {32--47}, doi = {10.1007/978-3-319-50349-3_3} }
@incollection{BloJouKess2017gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2017, editor = { Peter A. N. Bosman }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017}, author = { Aymeric Blot and Laetitia Jourdan and Marie-El{\'e}onore Kessaci-Marmion }, title = {Automatic design of multi-objective local search algorithms: case study on a bi-objective permutation flowshop scheduling problem}, pages = {227--234}, doi = {10.1145/3071178.3071323} }
@incollection{BloLopKesJou2018ppsn, volume = 11101, year = 2018, address = { Cham, Switzerland}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley }, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}}, author = { Aymeric Blot and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marie-El{\'e}onore Kessaci-Marmion and Laetitia Jourdan }, title = {New Initialisation Techniques for Multi-Objective Local Search: Application to the Bi-objective Permutation Flowshop}, doi = {10.1007/978-3-319-99253-2_26}, pages = {323--334} }
@incollection{BloPerJouKesHoo2017emo, editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu Jin and Christian Grimme}, year = 2017, volume = 10173, series = {Lecture Notes in Computer Science}, address = { Cham, Switzerland}, publisher = {Springer International Publishing}, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2017}, author = { Aymeric Blot and Alexis Pernet and Laetitia Jourdan and Marie-El{\'e}onore Kessaci-Marmion and Holger H. Hoos }, title = {Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation}, pages = {61--76} }
@incollection{BluBauPer06:ants, address = { Heidelberg, Germany}, publisher = {Springer}, year = 2006, volume = 4150, series = {Lecture Notes in Computer Science}, editor = { Marco Dorigo and others}, fulleditor = { Marco Dorigo and L. M. Gambardella and Mauro Birattari and Martinoli, A. and Poli, R. and Thomas St{\"u}tzle }, booktitle = {Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006}, author = { Christian Blum and J. Bautista and J. Pereira }, title = {{Beam-ACO} applied to assembly line balancing}, pages = {96--107}, doi = {10.1007/11839088_9} }
@techreport{BluBleLop08:lcs, author = { Christian Blum and Mar{\'i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Beam Search for the Longest Common Subsequence Problem}, institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya}, year = 2008, number = {LSI-08-29}, note = {Published in Computers \& Operations Research~\cite{BluBleLop09-BeamSearch-LCS}} }
@incollection{BluCotFerGal07:evocop, address = { Berlin, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 4446, year = 2007, editor = { Carlos Cotta and others}, booktitle = {Proceedings of EvoCOP 2007 -- Seventh European Conference on Evolutionary Computation in Combinatorial Optimisation}, author = { Christian Blum and Carlos Cotta and Antonio J. Fern{\'a}ndez and J. E. Gallardo }, title = {A probabilistic beam search algorithm for the shortest common supersequence problem}, pages = {36--47} }
@incollection{BluLop2011ieh, author = { Christian Blum and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, booktitle = {The Industrial Electronics Handbook: Intelligent Systems}, title = {Ant Colony Optimization}, publisher = {CRC Press}, year = 2011, edition = {2nd}, isbn = 9781439802830, url = {http://www.crcpress.com/product/isbn/9781439802830}, annnote = {http://www.eng.auburn.edu/~wilambm/ieh/} }
@incollection{BluMas2007hm, address = { Heidelberg, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 4771, editor = { Thomas Bartz-Beielstein and Mar{\'i}a J. Blesa and Christian Blum and Boris Naujoks and Andrea Roli and G{\"u}nther Rudolph and M. Sampels }, year = 2007, booktitle = {Hybrid Metaheuristics}, author = { Christian Blum and M. Mastrolilli}, title = {Using Branch {\&} Bound Concepts in Construction-Based Metaheuristics: {Exploiting} the Dual Problem Knowledge}, pages = {123--139} }
@book{BluMer08:si-book, title = {Swarm Intelligence--Introduction and Applications}, year = 2008, editor = { Christian Blum and D. Merkle }, series = {Natural Computing Series}, publisher = {Springer Verlag, Berlin, Germany} }
@book{BluRai2016:book, author = { Christian Blum and G{\"u}nther R. Raidl }, title = {Hybrid Metaheuristics---Powerful Tools for Optimization}, publisher = {Springer}, year = 2016, series = {Artificial Intelligence: Foundations, Theory, and Algorithms}, address = { Berlin, Germany} }
@incollection{BluRol2008hybrid, series = {Studies in Computational Intelligence}, volume = 114, year = 2008, address = { Berlin, Germany}, publisher = {Springer}, editor = { Christian Blum and Mar{\'i}a J. Blesa and Andrea Roli and M. Sampels }, booktitle = {Hybrid Metaheuristics: An emergent approach for optimization}, title = {Hybrid metaheuristics: an introduction}, author = { Christian Blum and Andrea Roli }, pages = {1--30} }
@incollection{BluYab06:hm, address = { Heidelberg, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 4030, editor = {F. Almeida and others}, aeditor = {F. Almeida and M. Blesa and C. Blum and J. M. Moreno and M. P{\'e}rez and A. Roli and M. Sampels }, year = 2006, booktitle = {Hybrid Metaheuristics}, author = { Christian Blum and M. {Y{\'a}bar Vall{\`e}s} }, title = {Multi-level ant colony optimization for {DNA} sequencing by hybridization}, pages = {94--109}, doi = {10.1007/11890584} }
@phdthesis{Boese1996, author = {K. D. Boese}, title = {Models for Iterative Global Optimization}, school = {University of California, Computer Science Department, Los Angeles, CA}, year = 1996 }
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@incollection{BosKerNeu2019, publisher = {{ACM}}, editor = { Tobias Friedrich and Carola Doerr and Arnold, Dirk V.}, year = 2019, booktitle = {Proceedings of the 15th {ACM}/{SIGEVO} Conference on Foundations of Genetic Algorithms}, author = { Jakob Bossek and Pascal Kerschke and Neumann, Aneta and Markus Wagner and Frank Neumann and Heike Trautmann }, title = {Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}, pages = {58--71} }
@inproceedings{Boulos01, author = { Paul F. Boulos and Chun Hou Orr and Werner de Schaetzen and J. G. Chatila and Michael Moore and Paul Hsiung and Devan Thomas }, title = {Optimal pump operation of water distribution systems using genetic algorithms}, booktitle = {AWWA Distribution System Symp.}, year = 2001, address = {Denver, USA}, publisher = {American Water Works Association} }
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@book{BoxHunHun1978stat, title = {Statistics for experimenters: an introduction to design, data analysis, and model building}, author = {Box, G. E. P. and Hunter, W. G. and Hunter, J. S.}, year = 1978, publisher = {John Wiley \& Sons}, address = { New York, NY} }
@incollection{Bra88:lnpam, author = {A. Brandt}, title = {Multilevel Computations: {Review} and Recent Developments}, booktitle = {Multigrid Methods: Theory, Applications, and Supercomputing, Proceedings of the 3rd Copper Mountain Conference on Multigrid Methods}, pages = {35--62}, year = 1988, editor = {S. F. McCormick}, volume = 110, series = {Lecture Notes in Pure and Applied Mathematics}, publisher = {Marcel Dekker}, address = { New York, NY} }
@inproceedings{BraBarWhiHubHin2007wfg, author = {L. Bradstreet and L. Barone and L. While and S. Huband and P. Hingston}, booktitle = {{IEEE} Symposium on Computational Intelligence in Multicriteria Decision-Making, {IEEE} {MCDM}}, title = {Use of the {WFG} Toolkit and {PISA} for Comparison of {MOEAs}}, year = 2007, pages = {382--389} }
@incollection{BarOjaGar2018ppsn, volume = 11101, year = 2018, address = { Cham, Switzerland}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, editor = { Anne Auger and Carlos M. Fonseca and Louren{\c c}o, N. and Penousal Machado and Lu{\'i}s Paquete and Darrell Whitley }, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XV}}, author = { Barba-Gonz{\'a}lez, Crist{\'o}bal and Vesa Ojalehto and Jos{\'e} Garc{\'i}a-Nieto and Nebro, Antonio J. and Kaisa Miettinen and Jos{\'{e}} F. Aldana-Montes}, title = {Artificial Decision Maker Driven by {PSO}: An Approach for Testing Reference Point Based Interactive Methods}, doi = {10.1007/978-3-319-99253-2_22}, pages = {274--285}, keywords = {machine decision-maker} }
@incollection{BraCorGre2015dagstuhl, keywords = {multiple criteria decision making, evolutionary multiobjective optimization}, doi = {10.4230/DagRep.5.1.96}, volume = {5(1)}, year = 2015, series = {Dagstuhl Reports}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Germany}, booktitle = {Understanding Complexity in Multiobjective Optimization (Dagstuhl Seminar 15031)}, editor = { Salvatore Greco and Kathrin Klamroth and Joshua D. Knowles and G{\"u}nther Rudolph }, author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Kadzi{\'n}ski, Mi{\l}osz and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Vincent Mousseau and Mauro Munerato and Roman S{\l}owi{\'n}ski }, title = {Behavior-Realistic Artificial Decision-Makers to Test Preference-Based Multi-objective Optimization Method ({Working} {Group} ``{Machine} {Decision}-{Making}'')}, pages = {110--116} }
@incollection{BraDeb2055integrating, author = { J{\"u}rgen Branke and Kalyanmoy Deb }, title = {Integrating User Preferences into Evolutionary Multi-Objective Optimization}, booktitle = {Knowledge Incorporation in Evolutionary Computation}, publisher = {Springer}, year = 2005, editor = { Yaochu Jin }, pages = {461--477}, address = {Berlin\slash Heidelberg}, abstract = {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.}, doi = {10.1007/978-3-540-44511-1_21} }
@incollection{BraFerLuq2016lncs, address = { Cham, Switzerland}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = 2016, booktitle = {Smart Cities (Smart-CT 2016)}, editor = { Alba, Enrique and Chicano, Francisco and Gabriel J. Luque }, author = {Bravo, Yesnier and Javier Ferrer and Gabriel J. Luque and Alba, Enrique }, title = {Smart Mobility by Optimizing the Traffic Lights: A New Tool for Traffic Control Centers}, pages = {147--156}, abstract = {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 M{\'a}laga city allows us to validate the approach and show its benefits for other cities as well.}, doi = {10.1007/978-3-319-39595-1_15}, keywords = {Multi-objective optimization, Smart mobility, Traffic lights planning} }
@book{BraMar2002:book, author = { Jean-Pierre Brans and Bertrand Mareschal }, title = {{PROMETHEE-GAIA}. Une m{\'e}thode d'aide {\`a} la d{\'e}cision en pr{\'e}sence de crit{\`e}res multiples}, year = 2002, isbn = {2-7298-1253-9}, publisher = {Editions Ellipses}, address = { Paris, France} }
@incollection{BraMar2005:mcda, editor = { Jos{\'e} Rui Figueira and Salvatore Greco and Matthias Ehrgott }, year = 2005, publisher = {Springer}, booktitle = {Multiple Criteria Decision Analysis, State of the Art Surveys}, author = { Jean-Pierre Brans and Bertrand Mareschal }, title = {{PROMETHEE} Methods}, chapter = 5, pages = {163--195} }
@incollection{BraSchSch2001gecco, publisher = {Morgan Kaufmann Publishers, San Francisco, CA}, editor = {Erik D. Goodman}, year = 2001, booktitle = {Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, GECCO 2001}, author = { J{\"u}rgen Branke and C. Schmidt and H. Schmeck}, title = {Efficient fitness estimation in noisy environments}, pages = {243--250} }
@techreport{BranCorrGreSlow2014, author = { J{\"u}rgen Branke and Salvatore Corrente and Salvatore Greco and Roman S{\l}owi{\'n}ski and Zielniewicz, P.}, title = {Using {Choquet} integral as preference model in interactive evolutionary multiobjective optimization}, institution = {WBS, University of Warwick}, year = 2014 }
@incollection{BranElo2011gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2011, editor = {Natalio Krasnogor and Pier Luca Lanzi}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011}, author = { J{\"u}rgen Branke and Jawad Elomari }, title = {Simultaneous tuning of metaheuristic parameters for various computing budgets}, pages = {263--264}, doi = {10.1145/2001858.2002006}, keywords = {meta-optimization, offline parameter optimization} }
@incollection{BranElo2013lion, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = 7997, booktitle = {Learning and Intelligent Optimization, 7th International Conference, LION 7}, publisher = {Springer}, year = 2013, editor = { Panos M. Pardalos and G. Nicosia}, author = { J{\"u}rgen Branke and Jawad Elomari }, title = {Racing with a Fixed Budget and a Self-Adaptive Significance Level} }
@book{BreFriSto1984trees, title = {Classification and regression trees}, author = {Breiman, Leo and Friedman, Jerome and Stone, Charles J. and Olshen, Richard A.}, year = 1984, publisher = {CRC Press} }
@incollection{BreSch2011ea, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = 7401, booktitle = {Artificial Evolution: 10th International Conference, Evolution Artificielle, EA, 2011}, publisher = {Springer}, year = 2012, editor = { Jin-Kao Hao and Legrand, Pierrick and Collet, Pierre and Monmarch{\'e}, Nicolas and Lutton, Evelyne and Schoenauer, Marc}, author = {M\'aty\'as Brendel and Marc Schoenauer }, title = {Learn-and-Optimize: A Parameter Tuning Framework for Evolutionary {AI} Planning}, pages = {145--155}, doi = {10.1007/978-3-642-35533-2_13} }
@incollection{BreSch2011gecco, year = 2011, address = { New York, NY}, publisher = {ACM Press}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2011}, editor = {Natalio Krasnogor and Pier Luca Lanzi}, author = {M{\'a}ty{\'a}s Brendel and Marc Schoenauer }, title = {Instance-based Parameter Tuning for Evolutionary {AI} Planning}, pages = {591--598}, doi = {10.1145/2001858.2002053} }
@incollection{BriFri2009emo, address = { Heidelberg, Germany}, publisher = {Springer}, year = 2009, series = {Lecture Notes in Computer Science}, volume = 5467, editor = { Matthias Ehrgott and Carlos M. Fonseca and Xavier Gandibleux and Jin-Kao Hao and Marc Sevaux }, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2009}, author = { Karl Bringmann and Tobias Friedrich }, title = {Approximating the Least Hypervolume Contributor: {NP}-Hard in General, But Fast in Practice}, pages = {6--20}, annote = {Extended version published in \cite{BriFri2012tcs}} }
@incollection{BriFri2010gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2010, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010}, editor = {Martin Pelikan and J{\"u}rgen Branke }, author = { Karl Bringmann and Tobias Friedrich }, title = {The Maximum Hypervolume Set Yields Near-optimal Approximation}, pages = {511--518}, annote = {Proved that hypervolume approximates the additive $\epsilon$-indicator, converging quickly as $N$ increases, that is, sets that maximize hypervolume are near optimal on additive $\epsilon$ too, with the gap diminishing as quickly as O(1/N).} }
@incollection{BriFri2010ppsn, volume = 6238, year = 2010, address = { Heidelberg, Germany}, publisher = {Springer}, editor = {Schaefer, Robert and Cotta, Carlos and Kolodziej, Joanna and G{\"u}nther Rudolph }, series = {Lecture Notes in Computer Science}, booktitle = {Parallel Problem Solving from Nature, PPSN XI}, title = {Tight bounds for the approximation ratio of the hypervolume indicator}, author = { Karl Bringmann and Tobias Friedrich }, pages = {607--616} }
@incollection{BriFri2011gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2011, editor = {Natalio Krasnogor and Pier Luca Lanzi}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011}, title = {Convergence of Hypervolume-Based Archiving Algorithms~{I}: Effectiveness}, author = { Karl Bringmann and Tobias Friedrich }, pages = {745--752}, doi = {10.1145/2001576.2001678}, annote = {Extended version published as \cite{BriFri2014convergence}} }
@incollection{BriFri2012gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2012, editor = {Terence Soule and Jason H. Moore}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012}, title = {Convergence of Hypervolume-Based Archiving Algorithms~{II}: Competitiveness}, author = { Karl Bringmann and Tobias Friedrich }, pages = {457--464}, doi = {10.1145/2330163.2330229}, annote = {Extended version published as \cite{BriFri2014convergence}} }
@incollection{BriFriKli2014generic, year = 2014, volume = 8672, address = { Heidelberg, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, editor = { Thomas Bartz-Beielstein and J{\"u}rgen Branke and Bogdan Filipi{\v c} and Jim Smith}, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XIII}}, title = {Generic postprocessing via subset selection for hypervolume and epsilon-indicator}, author = { Karl Bringmann and Tobias Friedrich and Patrick Klitzke}, pages = {518--527} }
@incollection{BriFriKli2014subset, address = { New York, NY}, publisher = {ACM Press}, year = 2014, editor = {Christian Igel and Dirk V. Arnold}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014}, title = {Two-dimensional subset selection for hypervolume and epsilon-indicator}, author = { Karl Bringmann and Tobias Friedrich and Patrick Klitzke}, doi = {10.1145/2576768.2598276} }
@inproceedings{BriPoz2012cec, year = 2012, address = {Piscataway, NJ}, publisher = {IEEE Press}, booktitle = {Proceedings of the 2012 Congress on Evolutionary Computation (CEC 2012)}, key = {IEEE CEC}, title = {Using archiving methods to control convergence and diversity for many-objective problems in particle swarm optimization}, author = {Britto, Andre and Pozo, Aurora}, pages = {1--8}, doi = {10.1109/CEC.2012.6256149} }
@incollection{BrigFri2009foga, isbn = {978-1-60558-414-0}, publisher = {{ACM}}, editor = {Ivan I. Garibay and Thomas Jansen and R. Paul Wiegand and Annie S. Wu}, year = 2009, booktitle = {Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA)}, author = { Karl Bringmann and Tobias Friedrich }, title = {Don't be greedy when calculating hypervolume contributions}, pages = {103--112}, annote = {Extended version published in \cite{BriFri2010eff}} }
@inproceedings{BrinFriNeuWag2011, publisher = {IJCAI/AAAI Press, Menlo Park, CA}, editor = {Toby Walsh}, year = 2011, booktitle = {Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11)}, author = { Karl Bringmann and Tobias Friedrich and Frank Neumann and Markus Wagner }, title = {Approximation-guided Evolutionary Multi-objective Optimization}, pages = {1198--1203} }
@incollection{Bro2015emo, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}}, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = 9018, year = 2015, publisher = {Springer}, editor = { Ant{\'o}nio Gaspar{-}Cunha and Carlos Henggeler Antunes and Carlos A. {Coello Coello} }, title = {A Bug in the Multiobjective Optimizer {IBEA}: Salutary Lessons for Code Release and a Performance Re-Assessment}, author = { Dimo Brockhoff }, doi = {10.1007/978-3-319-15934-8_13}, pages = {187--201} }
@incollection{BroCalLop2018dagstuhl, keywords = {multiple criteria decision making, evolutionary multiobjective optimization}, doi = {10.4230/DagRep.8.1.33}, volume = {8(1)}, year = 2018, series = {Dagstuhl Reports}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Germany}, booktitle = {Personalized Multiobjective Optimization: An Analytics Perspective (Dagstuhl Seminar 18031)}, editor = { Kathrin Klamroth and Joshua D. Knowles and G{\"u}nther Rudolph and Margaret M. Wiecek }, author = { Dimo Brockhoff and Roberto Calandra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Frank Neumann and Selvakumar Ulaganathan}, title = {Meta-modeling for (interactive) multi-objective optimization (WG5)}, pages = {85--94} }
@incollection{BroFriHebKle2007gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2007, editor = {Dirk Thierens and others}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007}, author = { Dimo Brockhoff and Tobias Friedrich and N. Hebbinghaus and C. Klein and Frank Neumann and Eckart Zitzler }, title = {Do Additional Objectives Make a Problem Harder?}, pages = {765--772}, doi = {10.1145/1276958.1277114} }
@incollection{BroLopNau2012ppsn, volume = 7491, year = 2012, address = { Heidelberg, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, fulleditor = { Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone}, editor = { Carlos A. {Coello Coello} and others}, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {XII}, Part {I}}, author = { Dimo Brockhoff and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Boris Naujoks and G{\"u}nther Rudolph }, title = {Runtime Analysis of Simple Interactive Evolutionary Biobjective Optimization Algorithms}, pages = {123--132}, doi = {10.1007/978-3-642-32937-1_13}, abstract = {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.} }
@incollection{BroSaxDeb2008handling, address = {Berlin\slash Heidelberg}, publisher = {Springer}, series = {Natural Computing Series}, year = 2008, booktitle = {Multiobjective Problem Solving from Nature}, author = { Dimo Brockhoff and Saxena, Dhish Kumar and Kalyanmoy Deb and Eckart Zitzler }, editor = { Joshua D. Knowles and David Corne and Kalyanmoy Deb and Chair, Deva Raj}, title = {On Handling a Large Number of Objectives A Posteriori and During Optimization}, pages = {377--403}, abstract = {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.}, doi = {10.1007/978-3-540-72964-8_18} }
@incollection{BroTus2019bench, doi = {10.1145/3319619}, isbn = {978-1-4503-6748-6}, address = { New York, NY}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019}, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, title = {Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbed}, author = { Dimo Brockhoff and Tea Tu{\v s}ar }, pages = {1905--1911}, keywords = {unbounded archive} }
@incollection{BroWagTrau2012r2, address = { New York, NY}, publisher = {ACM Press}, year = 2012, editor = {Terence Soule and Jason H. Moore}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2012}, title = {On the properties of the {R2} indicator}, author = { Dimo Brockhoff and Tobias Wagner and Heike Trautmann }, pages = {465--472}, annote = {Proof that R2 is weakly Pareto compliant.} }
@incollection{BroZit2006allobjectives, year = 2006, volume = 4193, series = {Lecture Notes in Computer Science}, address = { Heidelberg, Germany}, publisher = {Springer}, editor = {Runarsson, Thomas Philip and Hans-Georg Beyer and Edmund K. Burke and Juan-Juli{\'a}n Merelo and Darrell Whitley and Xin Yao }, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {IX}}, author = { Dimo Brockhoff and Eckart Zitzler }, title = {Are All Objectives Necessary? {On} Dimensionality Reduction in Evolutionary Multiobjective Optimization}, pages = {533--542}, abstract = {Most of the available multiobjective evolutionary algorithms (MOEA) for approximating the Pareto set have been designed for and tested on low dimensional problems ($\leq$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.} }
@incollection{BroZit2006dimensionality, author = { Dimo Brockhoff and Eckart Zitzler }, editor = {Waldmann, Karl-Heinz and Stocker, Ulrike M.}, title = {Dimensionality Reduction in Multiobjective Optimization: The Minimum Objective Subset Problem}, booktitle = {Operations Research Proceedings 2006}, year = 2007, publisher = {Springer}, address = {Berlin\slash Heidelberg}, pages = {423--429}, abstract = {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}, doi = {10.1007/978-3-540-69995-8_68}, keywords = {objective reduction} }
@inproceedings{BroZit2007hypervolumeReduction, address = {Piscataway, NJ}, publisher = {IEEE Press}, year = 2007, booktitle = {Proceedings of the 2007 Congress on Evolutionary Computation (CEC 2007)}, key = {IEEE CEC}, author = { Dimo Brockhoff and Eckart Zitzler }, title = {Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods}, pages = {2086--2093}, doi = {10.1109/CEC.2007.4424730}, keywords = {objective reduction} }
@inproceedings{BruRit2018cec, year = 2018, address = {Piscataway, NJ}, publisher = {IEEE Press}, booktitle = {Proceedings of the 2018 Congress on Evolutionary Computation (CEC 2018)}, key = {IEEE CEC}, author = { Artur Brum and Marcus Ritt}, title = {Automatic Design of Heuristics for Minimizing the Makespan in Permutation Flow Shops}, pages = {1--8}, doi = {10.1109/CEC.2018.8477787} }
@incollection{BruRit2018evo, address = { Heidelberg, Germany}, publisher = {Springer}, volume = 10782, series = {Lecture Notes in Computer Science}, year = 2018, booktitle = {Proceedings of EvoCOP 2018 -- 18th European Conference on Evolutionary Computation in Combinatorial Optimization }, editor = { Arnaud Liefooghe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, author = { Artur Brum and Marcus Ritt}, title = {Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time}, pages = {85--100}, doi = {10.1007/978-3-319-77449-7_6} }
@incollection{BuiRiz04:gecco, address = { Heidelberg, Germany}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 3102, editor = { Kalyanmoy Deb and others}, year = 2004, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, Part I}, author = {T. N. Bui and Rizzo, Jr, J. R. }, title = {Finding Maximum Cliques with Distributed Ants}, pages = {24--35} }
@techreport{BurByk2012, author = { Edmund K. Burke and Yuri Bykov }, title = {The Late Acceptance Hill-Climbing Heuristic}, institution = {University of Stirling}, number = {CSM-192}, year = 2012 }
@incollection{BurHydKen2007gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2007, editor = {Dirk Thierens and others}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007}, author = { Edmund K. Burke and Matthew R. Hyde and Graham Kendall and John R. Woodward}, title = {Automatic Heuristic Generation with Genetic Programming: Evolving a Jack-of-all-trades or a Master of One}, pages = {1559--1565}, doi = {10.1145/1276958.1277273} }
@incollection{BurHydKen2019hb, publisher = {Springer}, series = {International Series in Operations Research \& Management Science}, volume = 272, booktitle = {Handbook of Metaheuristics}, year = 2019, editor = { Michel Gendreau and Jean-Yves Potvin }, author = { Edmund K. Burke and Matthew R. Hyde and Graham Kendall and Gabriela Ochoa and Ender {\"O}zcan and John R. Woodward}, title = {A Classification of Hyper-Heuristic Approaches: Revisited}, chapter = 14, pages = {453--477}, doi = {10.1007/978-3-319-91086-4_14} }
@incollection{Burkard:QAP, volume = 2, editor = { Panos M. Pardalos and D.-Z. Du }, year = 1998, publisher = {Kluwer Academic Publishers}, booktitle = {Handbook of Combinatorial Optimization}, author = { Burkard, Rainer E. and Eranda {\c C}ela and Panos M. Pardalos and L. S. Pitsoulis }, title = {The quadratic assignment problem}, pages = {241--338} }
@incollection{Buz2019signif, isbn = {978-1-4503-6748-6}, address = { New York, NY}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019}, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, author = {Maxim Buzdalov}, title = {Towards better estimation of statistical significance when comparing evolutionary algorithms}, pages = {1782--1788}, doi = {10.1145/3319619.3326899} }
@techreport{CI-235-07, author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold }, title = {On the Complexity of Computing the Hypervolume Indicator}, institution = {University of Dortmund}, year = 2007, number = {CI-235/07}, month = dec, note = {Published in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}} }
@misc{COSEAL, title = {COnfiguration and SElection of ALgorithms}, key = {COSEAL}, howpublished = {http://www.coseal.net}, year = 2017 }
@misc{CPLEX, author = {IBM}, title = {{ILOG} {CPLEX} Optimizer}, year = 2017, howpublished = {\url{http://www.ibm.com/software/integration/optimization/cplex-optimizer/}} }
@incollection{CalShiCebDoe2019bayesian, doi = {10.1145/3319619}, isbn = {978-1-4503-6748-6}, address = { New York, NY}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2019}, publisher = {ACM Press}, year = 2019, editor = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Anne Auger and Thomas St{\"u}tzle }, author = {Calvo, Borja and Shir, Ofer M. and Josu Ceberio and Carola Doerr and Wang, Hao and Thomas B{\"a}ck and Jos{\'e} A. Lozano }, title = {Bayesian Performance Analysis for Black-box Optimization Benchmarking}, pages = {1789--1797}, numpages = 9, acmid = 3326888, keywords = {bayesian inference, benchmarking, black-box optimization, evolutionary algorithms, performance measures, plackett-luce model} }
@incollection{CamDorStu2018ants, volume = 11172, series = {Lecture Notes in Computer Science}, address = { Heidelberg, Germany}, publisher = {Springer}, editor = { Marco Dorigo and Mauro Birattari and Christensen, Anders L. and Reina, Andreagiovanni and Vito Trianni }, year = 2018, booktitle = {Swarm Intelligence, 11th International Conference, ANTS 2018}, author = {Camacho-Villal\'{o}n, Christian Leonardo and Marco Dorigo and Thomas St{\"u}tzle }, title = {Why the Intelligent Water Drops Cannot Be Considered as a Novel Algorithm}, pages = {302--314} }
@incollection{CamPas2010lion, doi = {10.1007/978-3-642-13800-3}, address = { Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, volume = 6073, booktitle = {Learning and Intelligent Optimization, 4th International Conference, LION 4}, publisher = {Springer}, year = 2010, editor = { Christian Blum and Roberto Battiti }, title = {Adapting to a realistic decision maker: experiments towards a reactive multi-objective optimizer}, author = { Paolo Campigotto and Andrea Passerini }, pages = {338--341} }
@incollection{CamStuDor2020ants, volume = 12421, series = {Lecture Notes in Computer Science}, address = { Heidelberg, Germany}, publisher = {Springer}, editor = { Marco Dorigo and Thomas St{\"u}tzle and Mar{\'i}a J. Blesa and Christian Blum and Heiko Hamann and Heinrich, Mary Katherine}, year = 2020, booktitle = {Swarm Intelligence, 12th International Conference, ANTS 2020}, title = {Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty}, author = {Camacho-Villal\'{o}n, Christian Leonardo and Thomas St{\"u}tzle and Marco Dorigo }, pages = {121--133} }
@unpublished{CamTriLop2017pseudo, author = {Felipe Campelo and \'Athila R. Trindade and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Pseudoreplication in Racing Methods for Tuning Metaheuristics}, note = {In preparation}, year = 2017 }
@book{Can00:book, author = {E. Cant{\'u}-Paz}, title = {Efficient and Accurate Parallel Genetic Algorithms}, publisher = {Kluwer Academic Publishers, Boston, MA}, year = 2000 }
@inproceedings{CarJesMar2003, author = {P. Cardoso and M. Jesus and A. Marquez}, title = {{MONACO}: multi-objective network optimisation based on an {ACO}}, booktitle = {Proc. X Encuentros de Geometr\'ia Computacional}, year = 2003, address = {Seville, Spain} }
@incollection{CarPinOli2017recipe, address = { Heidelberg, Germany}, publisher = {Springer}, isbn = {978-3-319-55695-6}, year = 2017, volume = 10196, series = {Lecture Notes in Computer Science}, booktitle = {Proceedings of the 20th European Conference on Genetic Programming, EuroGP 2017}, editor = {James McDermott and Mauro Castelli and Luk{\'{a}}s Sekanina and Evert Haasdijk and Pablo Garc{\'i}a-S{\'a}nchez }, author = {de S{\'{a}}, Alex Guimar{\~{a}}es Cardoso and Pinto, Walter Jos{\'{e}} G. S. and Oliveira, Luiz Ot{\'{a}}vio Vilas Boas and Gisele Pappa }, title = {{RECIPE:} {A} Grammar-Based Framework for Automatically Evolving Classification Pipelines}, pages = {246--261}, doi = {10.1007/978-3-319-55696-3_16} }
@incollection{CarProSha2013votes, address = { New York, NY}, publisher = {ACM Press}, year = 2013, editor = {Michael J. Kearns and R. Preston McAfee and {\'{E}}va Tardos}, booktitle = {Proceedings of the Fourteenth ACM Conference on Electronic Commerce}, title = {When Do Noisy Votes Reveal the Truth?}, author = {Ioannis Caragiannis and Ariel D. Procaccia and Nisarg Shah}, doi = {10.1145/2482540.2482570}, keywords = {computer social choice, mallows model, sample complexity}, pages = {143--160}, abstract = {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.} }
@incollection{CebMenLoz2015mallows, address = { New York, NY}, publisher = {ACM Press}, year = 2015, editor = {Sara Silva and Anna I. Esparcia{-}Alc{\'{a}}zar }, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015}, title = {Kernels of {Mallows} Models for Solving Permutation-based Problems}, author = { Josu Ceberio and Alexander Mendiburu and Jos{\'e} A. Lozano }, pages = {505--512} }
@book{Cela:QAP, author = { Eranda {\c C}ela }, title = {The Quadratic Assignment Problem: Theory and Algorithms}, publisher = {Kluwer Academic Publishers}, year = 1998, address = {Dordrecht, The Netherlands} }
@inproceedings{CesOddSmi2000:aaai, publisher = {{AAAI} Press\slash {MIT} Press, Menlo Park, CA}, year = 2000, booktitle = {Proceedings of AAAI 2000 -- Seventeenth National Conference on Artificial Intelligence}, editor = {Henry A. Kautz and Bruce W. Porter}, author = { Amadeo Cesta and Angelo Oddi and Stephen F. Smith }, title = {Iterative Flattening: A Scalable Method for Solving Multi-Capacity Scheduling Problems}, pages = {742--747} }
@mastersthesis{Chang99, author = { S. T. H. Chang }, title = {Optimizing the Real Time Operation of a Pumping Station at a Water Filtration Plant using Genetic Algorithms}, school = {Department of Civil and Environmental Engineering, The University of Adelaide}, year = 1999, type = {Honors Thesis} }
@inproceedings{Chase89, author = { Donald V. Chase and Lindell E. Ormsbee }, title = {Optimal pump operation of water distribution systems with multiple storage tanks}, booktitle = {Proceedings of American Water Works Association Computer Specialty Conference}, pages = {205--214}, year = 1989, address = {Denver, USA}, organization = {AWWA} }
@inproceedings{Chase91, author = { Donald V. Chase and Lindell E. Ormsbee }, title = {An alternate formulation of time as a decision variable to facilitate real-time operation of water supply systems}, booktitle = {Proceedings of the 18th Annual Conference of Water Resources Planning and Management}, pages = {923--927}, year = 1991, address = { New York, NY}, organization = {ASCE} }
@incollection{CheBuzDoeDan2023aac, publisher = {{ACM}}, editor = { Chicano, Francisco and Tobias Friedrich and K{\"o}tzing, Timo and Franz Rothlauf }, year = 2023, booktitle = {Proceedings of the 17th {ACM}/{SIGEVO} Conference on Foundations of Genetic Algorithms}, author = {Chen, Deyao and Buzdalov, Maxim and Carola Doerr and Nguyen Dang}, title = {Using Automated Algorithm Configuration for Parameter Control}, pages = {38--49}, doi = {10.1145/3594805.3607127} }
@inproceedings{CheGaoChen2005scga, title = {{SCGA}: Controlling genetic algorithms with {Sarsa}(0)}, author = {Chen, Fei and Gao, Yang and Chen, Zhao-qian and Chen, Shi-fu}, booktitle = {Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on}, volume = 1, pages = {1177--1183}, year = 2005, publisher = {IEEE}, doi = {10.1109/CIMCA.2005.1631422} }
@incollection{CheGinBecMol2013moda, address = { Heidelberg, Germany}, publisher = {Springer International Publishing}, booktitle = {mODa 10--Advances in Model-Oriented Design and Analysis}, year = 2013, editor = {Ucinski, Dariusz and Atkinson, Anthony C. and Patan, Maciej}, author = {Chevalier, Cl{\'e}ment and Ginsbourger, David and Bect, Julien and Molchanov, Ilya}, title = {Estimating and Quantifying Uncertainties on Level Sets Using the {Vorob}'ev Expectation and Deviation with {Gaussian} Process Models}, pages = {35--43}, abstract = {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.}, doi = {10.1007/978-3-319-00218-7_5} }
@inproceedings{CheIshSha2021clustering, title = {Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization}, author = {Chen, Weiyu and Ishibuchi, Hisao and Shang, Ke}, booktitle = {2021 IEEE International Conference on Systems, Man, and Cybernetics}, year = 2021, organization = {IEEE}, pages = {468--475} }
@incollection{CheKanTay1991ijcai, publisher = {Morgan Kaufmann Publishers}, editor = {Mylopoulos, John and Reiter, Raymond}, year = 1995, booktitle = {Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI-91)}, author = {Cheeseman, Peter C. and Kanefsky, Bob and Taylor, William M.}, title = {Where the Really Hard Problems Are}, pages = {331--340} }
@inproceedings{CheXuChe04, publisher = {IEEE Press}, year = 2004, booktitle = {Proceedings of the International Conference on Machine Learning and Cybernetics}, editor = {Cloete, Ian and Wong, Kit-Po and Berthold, Michael}, author = {L. Chen and X. H. Xu and Y. X. Chen}, title = {An adaptive ant colony clustering algorithm}, pages = {1387--1392} }
@inproceedings{CheIshSha2020subset, year = 2020, address = {Piscataway, NJ}, publisher = {IEEE Press}, booktitle = {Proceedings of the 2020 Congress on Evolutionary Computation (CEC 2020)}, key = {IEEE CEC}, title = {Modified Distance-based Subset Selection for Evolutionary Multi-objective Optimization Algorithms}, author = {Chen, Weiyu and Ishibuchi, Hisao and Shang, Ke}, pages = {1--8}, keywords = {IGD+} }
@inproceedings{CheXinChe2017vdmlibrary, year = 2017, address = {Piscataway, NJ}, publisher = {IEEE Press}, booktitle = {Proceedings of the 2017 Congress on Evolutionary Computation (CEC 2017)}, key = {IEEE CEC}, author = {Chen, Lu and Xin, Bin and Chen, Jie and Juan Li}, title = {A virtual-decision-maker library considering personalities and dynamically changing preference structures for interactive multiobjective optimization}, pages = {636--641}, doi = {10.1109/CEC.2017.7969370}, keywords = {machine DM, interactive EMOA} }
@incollection{ChiDerVer2023fourier, doi = {10.1145/3583131}, location = {Lisbon, Portugal}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023}, annote = {ISBN: 9798400701191}, address = { New York, NY}, year = 2023, publisher = {ACM Press}, editor = {Silva, Sara and Lu{\'i}s Paquete }, author = { Chicano, Francisco and Bilel Derbel and Verel, S{\'e}bastien }, title = {Fourier Transform-based Surrogates for Permutation Problems}, pages = {275--283} }
@incollection{ChiGoe2010, editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss }, year = 2010, address = { Berlin, Germany}, publisher = {Springer}, booktitle = {Experimental Methods for the Analysis of Optimization Algorithms}, author = { Marco Chiarandini and Yuri Goegebeur}, title = {Mixed Models for the Analysis of Optimization Algorithms}, pages = {225--264}, annote = {Preliminary version available as \emph{Tech.\ Rep.} MF-2009-07-001 at the The Danish Mathematical Society}, doi = {10.1007/978-3-642-02538-9} }
@phdthesis{ChiarandiniPhD, author = { Marco Chiarandini }, title = {Stochastic Local Search Methods for Highly Constrained Combinatorial Optimisation Problems}, school = {FB Informatik, TU Darmstadt, Germany}, year = 2005 }
@misc{Chieng2014, author = {Tsung-Che Chiang}, title = {nsga3cpp: A {C++} implementation of {NSGA-III}}, howpublished = {\url{http://web.ntnu.edu.tw/~tcchiang/publications/nsga3cpp/nsga3cpp.htm}}, year = 2014 }
@inproceedings{ChrSchBur2011patus, publisher = {IEEE Computer Society}, year = 2011, series = {IPDPS '11}, booktitle = {Proceedings of the 2011 IEEE International Parallel \& Distributed Processing Symposium}, editor = {Frank Mueller}, author = {Matthias Christen and Olaf Schenk and Helmar Burkhart}, title = {{PATUS:} A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures}, pages = {676--687}, doi = {10.1109/IPDPS.2011.70} }
@techreport{ChrVan2018, author = {Jan Christiaens and Greet Vanden Berghe}, title = {Slack Induction by String Removals for Vehicle Routing Problems}, institution = {Department of Computing Science, KU Leuven, Gent, Belgium}, year = 2018, number = {7-05-2018} }
@techreport{Christofides1976, title = {Worst-case analysis of a new heuristic for the travelling salesman problem}, author = { Christofides, Nicos }, year = 1976, number = 388, institution = {Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, PA} }
@incollection{ChuLop2021gecco, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO Companion 2021}, address = { New York, NY}, year = 2021, publisher = {ACM Press}, editor = { Chicano, Francisco and Krzysztof Krawiec }, author = { Tinkle Chugh and Manuel L{\'o}pez-Ib{\'a}{\~n}ez }, title = {Maximising Hypervolume and Minimising $\epsilon$-Indicators using Bayesian Optimisation over Sets}, doi = {10.1145/3449726.3463178}, keywords = {multi-objective, surrogate models, epsilon, hypervolume}, supplement = {https://doi.org/10.5281/zenodo.4675569}, pages = {1326--1334} }
@incollection{ChuNuaJanPho06, address = { New York, NY}, publisher = {ACM Press}, year = 2006, editor = {M. Cattolico and others}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006}, author = {S. Chusanapiputt and D. Nualhong and S. Jantarang and S. Phoomvuthisarn}, title = {Selective self-adaptive approach to ant system for solving unit commitment problem}, pages = {1729--1736} }
@phdthesis{Chugh2017phd, author = { Tinkle Chugh }, title = {Handling expensive multiobjective optimization problems with evolutionary algorithms}, school = {University of Jyv{\"a}skyl{\"a}}, year = 2017 }
@inproceedings{Chugh2020scalar, year = 2020, address = {Piscataway, NJ}, publisher = {IEEE Press}, booktitle = {Proceedings of the 2020 Congress on Evolutionary Computation (CEC 2020)}, key = {IEEE CEC}, author = { Tinkle Chugh }, title = {Scalarizing Functions in Bayesian Multiobjective Optimization}, pages = {1--8}, doi = {10.1109/CEC48606.2020.9185706} }
@incollection{CinFerLopAl2021evocop, address = { Cham, Switzerland}, publisher = {Springer}, volume = 12692, series = {Lecture Notes in Computer Science}, year = 2021, booktitle = {Proceedings of EvoCOP 2021 -- 21th European Conference on Evolutionary Computation in Combinatorial Optimization }, editor = { Christine Zarges and Verel, S{\'e}bastien }, author = { Christian Cintrano and Javier Ferrer and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Alba, Enrique }, title = {Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs}, abstract = {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.}, keywords = {Hybrid algorithms, Evolutionary algorithms, Simulation optimization, Uncertainty, Traffic light planning}, pages = {17--33}, doi = {10.1007/978-3-030-72904-2_2}, annote = {Extended version published in Evolutionary Computation journal~\cite{CinFerLopAlb2022irace}.} }
@inproceedings{CirJohMcGZha2001, author = {Jill Cirasella and David S. Johnson and Lyle A. McGeoch and Weixiong Zhang}, title = {The Asymmetric Traveling Salesman Problem: Algorithms, Instance Generators, and Tests}, booktitle = {Algorithm Engineering and Experimentation, Third International Workshop, {ALENEX} 2001, Washington, DC, USA, January 5-6, 2001, Revised Papers}, pages = {32--59}, series = {Lecture Notes in Computer Science}, volume = 2153, publisher = {Springer}, address = { Berlin, Germany}, year = 2001, doi = {10.1007/3-540-44808-X_3}, editor = {Adam L. Buchsbaum and Jack Snoeyink} }
@inproceedings{ClaKar1992electronic, author = {Jon Claerbout and Martin Karrenbach}, year = 1992, title = {Electronic documents give reproducible research a new meaning}, booktitle = {SEG Technical Program Expanded Abstracts 1992}, publisher = {Society of Exploration Geophysicists}, pages = {601--604}, doi = {10.1190/1.1822162}, annote = {Proposed a reproducibility taxonomy, defined reproducibility and taxonomy} }
@misc{CleKen2011spso, author = { Clerc, Maurice and J. Kennedy }, title = {Standard {PSO} 2011}, howpublished = {Particle Swarm Central}, year = 2011, url = {http://www.particleswarm.info/} }
@unpublished{Clerc2012spso, title = {Standard {Particle} {Swarm} {Optimisation}}, author = { Clerc, Maurice }, url = {https://hal.archives-ouvertes.fr/hal-00764996}, numpages = 15, year = 2012, month = sep, hal_id = {hal-00764996}, hal_version = {v1}, keywords = {particle swarm optimisation}, abstract = {Since 2006, three successive standard PSO versions have been put on line on the Particle Swarm Central (\url{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.}, note = {hal-00764996} }
@incollection{Coe2015multi, title = {Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges}, author = { Carlos A. {Coello Coello} }, booktitle = {Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences}, pages = {3--18}, year = 2015, doi = {10.1007/978-3-319-11541-2_1}, publisher = {Springer} }
@book{CoeLamVVe2007:book, author = { Carlos A. {Coello Coello} and Gary B. Lamont and David A. {Van Veldhuizen} }, title = {Evolutionary Algorithms for Solving Multi-Objective Problems}, year = 2007, publisher = {Springer}, address = { New York, NY}, edition = {2nd}, doi = {10.1007/978-0-387-36797-2} }
@incollection{CoeSie2004igd, year = 2004, address = { Heidelberg, Germany}, publisher = {Springer}, volume = 2972, series = {Lecture Notes in Artificial Intelligence}, booktitle = {Proceedings of MICAI}, editor = {Monroy, Ra{\'u}l and Arroyo-Figueroa, Gustavo and Sucar, Luis Enrique and Sossa, Humberto}, author = { Carlos A. {Coello Coello} and Reyes-Sierra, Margarita}, title = {A Study of the Parallelization of a Coevolutionary Multi-objective Evolutionary Algorithm}, pages = {688--697}, keywords = {IGD}, annote = {Introduces Inverted Generational Distance (IGD)} }
@inproceedings{Coello2000cec, month = jul, address = {Piscataway, NJ}, publisher = {IEEE Press}, year = 2000, booktitle = {Proceedings of the 2000 Congress on Evolutionary Computation (CEC'00)}, key = {IEEE CEC}, author = { Carlos A. {Coello Coello} }, title = {Handling Preferences in Evolutionary Multiobjective Optimization: A Survey}, pages = {30--37} }
@incollection{Coello2017results, volume = 10687, series = {Lecture Notes in Computer Science}, year = 2017, address = { Cham, Switzerland}, publisher = {Springer International Publishing}, booktitle = {Theory and Practice of Natural Computing - 6th International Conference, {TPNC} 2017}, editor = {Carlos Mart{\'i}n{-}Vide and Roman Neruda and Miguel A. Vega{-}Rodr{\'i}guez}, author = { Carlos A. {Coello Coello} }, title = {Recent Results and Open Problems in Evolutionary Multiobjective Optimization}, pages = {3--21} }
@book{Cohen1995ai, author = {Paul R. Cohen}, title = {Empirical Methods for Artificial Intelligence}, publisher = {MIT Press}, address = {Cambridge, MA}, year = 1995 }
@incollection{Cohen82, author = { G. Cohen }, title = {Optimal Control of Water Supply Networks}, booktitle = {Optimization and Control of Dynamic Operational Research Models}, pages = {251--276}, publisher = {North-Holland Publishing Company}, year = 1982, editor = { S. G. Tzafestas }, volume = 4, chapter = 8, address = {Amsterdam} }
@inproceedings{ColDorMan92:ecal, publisher = {MIT Press, Cambridge, MA}, editor = {F. J. Varela and P. Bourgine}, year = 1992, booktitle = {Proceedings of the First European Conference on Artificial Life}, author = { Alberto Colorni and Marco Dorigo and Vittorio Maniezzo }, title = {Distributed Optimization by Ant Colonies}, pages = {134--142} }
@incollection{ColMonGauSli07, volume = 4926, series = {Lecture Notes in Computer Science}, address = { Heidelberg, Germany}, publisher = {Springer}, year = 2008, doi = {10.1007/978-3-540-79305-2}, shorteditor = {Monmarch{\'e}, Nicolas and others}, editor = {Monmarch{\'e}, Nicolas and Talbi, El-Ghazali and Collet, Pierre and Marc Schoenauer and Lutton, Evelyne}, booktitle = {Artificial Evolution}, author = {Sonia Colas and Nicolas Monmarch{\'e} and Pierre Gaucher and Mohamed Slimane}, pages = {87--99}, title = {Artificial Ants for the Optimization of Virtual Keyboard Arrangement for Disabled People} }
@book{ConSchVic2009, author = {Andrew R. Conn and Katya Scheinberg and Luis N. Vicente}, title = {Introduction to Derivative-Free Optimization}, publisher = {Society for Industrial and Applied Mathematics, Philadelphia, PA, USA}, year = 2009, series = {MPS--SIAM Series on Optimization} }
@misc{ConcordeSolver, author = { David Applegate and Robert E. Bixby and Va{\v{s}}ek Chv{\'a}tal and William J. Cook }, title = {Concorde {TSP} Solver}, howpublished = {\url{http://www.math.uwaterloo.ca/tsp/concorde.html}}, note = {Version visited last on 15 April 2014}, year = 2014 }
@book{Conover99:pns, author = { W. J. Conover }, title = {Practical Nonparametric Statistics}, publisher = {John Wiley \& Sons}, address = { New York, NY}, year = 1999, edition = {3rd} }
@inproceedings{Cook1971, author = {Cook, Stephen A.}, title = {The Complexity of Theorem-proving Procedures}, booktitle = {Proceedings of the Third Annual ACM Symposium on Theory of Computing}, series = {STOC '71}, year = 1971, location = {Shaker Heights, Ohio, USA}, pages = {151--158}, numpages = 8, doi = {10.1145/800157.805047}, acmid = 805047, publisher = {ACM} }
@book{Cook2012, author = { William J. Cook }, title = {In Pursuit of the Traveling Salesman}, publisher = {Princeton University Press, Princeton, NJ}, year = 2012 }
@incollection{Cook2019, year = 2019, editor = {Bernhard Steffen and Gerhard Woeginger}, address = { Cham, Switzerland}, publisher = {Springer}, volume = 10000, series = {Lecture Notes in Computer Science}, booktitle = {Computing and Software Science: State of the Art and Perspectives}, title = {Computing in Combinatorial Optimization}, author = { William J. Cook }, pages = {27--47}, doi = {10.1007/978-3-319-91908-9_3} }
@incollection{CorKno2001pesa2, publisher = {Morgan Kaufmann Publishers, San Francisco, CA}, editor = {Erik D. Goodman}, year = 2001, booktitle = {Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, GECCO 2001}, author = { David Corne and Jerram, Nick R. and Joshua D. Knowles and Oates, Martin J.}, title = {{PESA-II}: Region-Based Selection in Evolutionary Multiobjective Optimization}, pages = {283--290}, numpages = 8, doi = {10.5555/2955239.2955289} }
@inproceedings{CorKno2003cec, year = 2003, address = {Piscataway, NJ}, publisher = {IEEE Press}, month = dec, booktitle = {Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03)}, key = {IEEE CEC}, author = { David Corne and Joshua D. Knowles }, title = {Some Multiobjective Optimizers are Better than Others}, pages = {2506--2512} }
@incollection{CorKno2003nfl, address = { Heidelberg, Germany}, publisher = {Springer}, volume = 2632, series = {Lecture Notes in Computer Science}, editor = { Carlos M. Fonseca and Peter J. Fleming and Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele }, year = 2003, booktitle = { Evolutionary Multi-criterion Optimization, EMO 2003}, author = { David Corne and Joshua D. Knowles }, title = {No free lunch and free leftovers theorems for multiobjective optimisation problems}, pages = {327--341}, doi = {10.1007/3-540-36970-8_23} }
@incollection{CorKnoOat2000ppsn, anote = {IC.29}, address = { Heidelberg, Germany}, publisher = {Springer}, editor = { Marc Schoenauer and others}, aeditor = { Marc Schoenauer and Kalyanmoy Deb and G{\"u}nther Rudolph and Xin Yao and E. Lutton and Juan-Juli{\'a}n Merelo and Hans-Paul Schwefel }, year = 2000, volume = 1917, series = {Lecture Notes in Computer Science}, booktitle = {Parallel Problem Solving from Nature -- {PPSN} {VI}}, author = { David Corne and Joshua D. Knowles and M. J. Oates}, title = {The {Pareto} Envelope-Based Selection Algorithm for Multiobjective Optimization}, pages = {839--848} }
@book{CorLeiRiv2009, title = {Introduction to algorithms}, author = {Cormen, Thomas H. and Leiserson, Charles E. and Rivest, Ronald L. and Stein, Clifford}, year = 2009, publisher = {MIT Press}, address = {Cambridge, MA} }
@incollection{CorRey2011gecco, address = { New York, NY}, publisher = {ACM Press}, year = 2011, editor = {Natalio Krasnogor and Pier Luca Lanzi}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011}, author = { David Corne and Reynolds, Alan}, title = {Evaluating optimization algorithms: bounds on the performance of optimizers on unseen problems}, pages = {707--710}, doi = {10.1145/2001858.2002073}, supplement = {http://is.gd/evalopt} }
@inproceedings{CorViaHerMor2000bwas, month = sep # { 7--9}, year = 2000, date = {2000-09-07/2000-09-09}, organization = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium}, editor = { Marco Dorigo and others}, fulleditor = { Marco Dorigo and Martin Middendorf and Thomas St{\"u}tzle }, booktitle = {Abstract proceedings of ANTS 2000 -- From Ant Colonies to Artificial Ants: Second International Workshop on Ant Algorithms}, author = { Oscar Cord{\'o}n and I. Fern{\'a}ndez de Viana and Francisco Herrera and L. Moreno}, title = {A New {ACO} Model Integrating Evolutionary Computation Concepts: The Best-Worst Ant System}, pages = {22--29} }
@incollection{CowKenSou2000hyper, publisher = {Springer}, volume = 2079, series = {Lecture Notes in Computer Science}, year = 2000, editor = {Edmund K. Burke and Wilhelm Erben}, booktitle = {PATAT 2000: Proceedings of the 3rd International Conference of the Practice and Theory of Automated Timetabling}, author = {Peter I. Cowling and Graham Kendall and Eric Soubeiga}, title = {A Hyperheuristic Approach to Scheduling a Sales Summit}, pages = {176--190}, doi = {10.1007/3-540-44629-X_11}, annote = {First mention of the term hyper-heuristic.} }
@book{Crawley2012rbook, author = {M. J. Crawley}, title = {The \proglang{R} Book}, publisher = {Wiley}, year = 2012, edition = {2nd} }
@techreport{CroGloThoTra1963, author = {W. B. Crowston and F. Glover and G. L. Thompson and J. D. Trawick}, title = {Probabilistic and Parametric Learning Combinations of Local Job Shop Scheduling Rules}, institution = {GSIA, Carnegie-Mellon University, Pittsburgh, PA, USA}, year = 1963, number = {No.\ 117}, type = {ONR Research Memorandum} }
@techreport{Cul92, author = { Joseph C. Culberson }, title = {Iterated Greedy Graph Coloring and the Difficulty Landscape}, institution = {Department of Computing Science, The University of Alberta, Edmonton, Alberta, Canada}, year = 1992, number = {92-07} }
@inproceedings{CulBeaPap95, author = { Joseph C. Culberson and A. Beacham and D. Papp}, title = {Hiding our Colors}, booktitle = {Proceedings of the CP'95 Workshop on Studying and Solving Really Hard Problems}, pages = {31--42}, year = 1995, address = {Cassis, France}, month = sep }
@incollection{CulLuo1996, series = {{DIMACS} Series on Discrete Mathematics and Theoretical Computer Science}, volume = 26, year = 1996, address = { Providence, RI}, publisher = {American Mathematical Society}, booktitle = {Cliques, Coloring, and Satisfiability: Second {DIMACS} Implementation Challenge}, editor = {David S. Johnson and Michael A. Trick }, author = { Joseph C. Culberson and F. Luo}, title = {Exploring the $k$-colorable Landscape with Iterated Greedy}, pages = {245--284} }
@book{Cumming2012, author = {Jeff Cumming}, title = {Understanding the New Statistics -- Effect Sizes, Confidence Intervals, and Meta-analysis}, publisher = {Taylor \& Francis}, year = 2012 }
@incollection{DanDeC2014, year = 2014, publisher = {SciTePress}, booktitle = {{ICORES} 2014 - Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems}, editor = {Bego{\~{n}}a Vitoriano and Eric Pinson and Fernando Valente}, author = {Nguyen {Dang Thi Thanh} and Patrick {De Causmaecker}}, title = {Motivations for the Development of a Multi-objective Algorithm Configurator}, pages = {328--333} }
@incollection{DanDec2016neighborhood, address = { Cham, Switzerland}, series = {Lecture Notes in Computer Science}, volume = 10079, booktitle = {Learning and Intelligent Optimization, 10th International Conference, LION 10}, publisher = {Springer}, year = 2016, editor = {Paola Festa and Meinolf Sellmann and Joaquin Vanschoren }, title = {Characterization of Neighborhood Behaviours in a Multi-neighborhood Local Search Algorithm}, author = {Nguyen {Dang Thi Thanh} and Patrick {De Causmaecker}}, pages = {234--239} }
@incollection{DanDec2019analysis, address = { Cham, Switzerland}, series = {Lecture Notes in Computer Science}, volume = 11968, booktitle = {Learning and Intelligent Optimization, 13th International Conference, LION 13}, publisher = {Springer}, year = 2019, editor = {Nikolaos F. Matsatsinis and Yannis Marinakis and Panos M. Pardalos }, author = {Nguyen Dang and Patrick {De Causmaecker}}, title = {Analysis of Algorithm Components and Parameters: Some Case Studies}, pages = {288--303}, abstract = {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, fANO