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References

[1]

ACM.
Artifact Review and Badging Version 1.1.
https://www.acm.org/publications/policies/artifactreviewandbadgingcurrent,
August 2020.
[ bib ]

[2]

Emile H. L. Aarts, Jan H. M. Korst, and Wil Michiels.
Simulated Annealing.
In E. K. Burke and G. Kendall, editors, Search Methodologies,
pages 187–210. Springer, Boston, MA, 2005.
[ bib 
DOI ]

[3]

Hussein A. Abbass.
The selfadaptive Pareto differential evolution algorithm.
In Proceedings of the 2002 Congress on Evolutionary Computation
(CEC'02), pages 831–836, Piscataway, NJ, 2002. IEEE Press.
[ bib ]

[4]

Hussein A. Abbass, Ruhul Sarker, and Charles Newton.
PDE: a Paretofrontier differential evolution approach for
multiobjective optimization problems.
In Proceedings of the 2001 Congress on Evolutionary Computation
(CEC'01), pages 971–978, Piscataway, NJ, 2001. IEEE Press.
[ bib ]

[5]

F. Ben Abdelaziz, S. Krichen, and J. Chaouachi.
A hybrid heuristic for multiobjective knapsack problems.
In M. G. C. Resende and J. Pinho de Souza, editors, Proceedings
of MIC 1997, the 2nd Metaheuristics International Conference, pages
205–212, 1997.
[ bib 
DOI ]

[6]

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

[7]

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

[8]

A. Acan.
An external memory implementation in ant colony optimization.
In M. Dorigo et al., editors, Ant Colony Optimization and Swarm
Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of
Lecture Notes in Computer Science, pages 73–84. Springer, Heidelberg,
Germany, 2004.
[ bib ]
Keywords: memorybased ACO

[9]

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

[10]

Tobias Achterberg.
SCIP: Solving constraint integer programs.
Mathematical Programming Computation, 1(1):1–41, July 2009.
[ bib ]
http://mpc.zib.de/archive/2009/1/Achterberg2009_Article_SCIPSolvingConstraintIntegerPr.pdf

[11]

HéctorGabriel AcostaMesa, Fernando RechyRamírez, Efrén
MezuraMontes, Nicandro CruzRamírez, and Rodolfo Hernández
Jiménez.
Application of time series discretization using evolutionary
programming for classification of precancerous cervical lesions.
Journal of Biomedical Informatics, 49:73–83, 2014.
[ bib 
DOI ]
Keywords: irace

[12]

Bernardetta Addis, Marco Locatelli, and Fabio Schoen.
Disk Packing in a Square: A New Global Optimization Approach.
INFORMS Journal on Computing, 20(4):516–524, 2008.
[ bib 
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[13]

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

[14]

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

[15]

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

[16]

Hernán E. Aguirre and Kiyoshi Tanaka.
ManyObjective Optimization by Space Partitioning and Adaptive
εRanking on MNKLandscapes.
In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.K. Hao, and
M. Sevaux, editors, Evolutionary Multicriterion Optimization, EMO
2009, volume 5467 of Lecture Notes in Computer Science, pages
407–422. Springer, Heidelberg, Germany, 2009.
[ bib ]

[17]

Hernán E. Aguirre.
Advances on Manyobjective Evolutionary Optimization.
In C. Blum and E. Alba, editors, GECCO (Companion), pages
641–666, New York, NY, 2013. ACM Press.
[ bib ]
Keywords: manyobjective evolutionary optimization

[18]

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

[19]

A. Aho, J. Hopcroft, and J. Ullman.
Data structures and algorithms.
AddisonWesley, Reading, MA, 1983.
[ bib ]

[20]

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

[21]

R. K. Ahuja, T. Magnanti, and J. B. Orlin.
Network Flows: Theory, Algorithms and Applications.
PrenticeHall, 1993.
[ bib ]

[22]

Uwe Aickelin, Edmund K. Burke, and Jingpeng Li.
Improved Squeaky Wheel Optimisation for Driver Scheduling.
In T. P. Runarsson, H.G. Beyer, E. K. Burke, J.J. Merelo,
D. Whitley, and X. Yao, editors, Proceedings of PPSNIX, Ninth
International Conference on Parallel Problem Solving from Nature, volume
4193 of Lecture Notes in Computer Science, pages 182–191. Springer,
Heidelberg, Germany, 2006.
[ bib ]

[23]

Sandip Aine, Rajeev Kumar, and P. P. Chakrabarti.
Adaptive parameter control of evolutionary algorithms to improve
qualitytime tradeoff.
Applied Soft Computing, 9(2):527–540, 2009.
[ bib 
DOI ]
Keywords: anytime

[24]

Hassene Aissi and Bernard Roy.
Robustness in Multicriteria Decision Aiding.
In M. Ehrgott, J. R. Figueira, and S. Greco, editors, Trends in
Multiple Criteria Decision Analysis, volume 142 of International Series
in Operations Research & Management Science, chapter 4, pages 87–121.
Springer, US, 2010.
[ bib ]

[25]

S. M. Aktürk, Alper Atamtürk, and S. Gürel.
A Strong Conic Quadratic Reformulation for MachineJob
Assignment with Controllable Processing Times.
Research Report BCOL.07.01, University of CaliforniaBerkeley, 2007.
[ bib ]

[26]

I. Alaya, Christine Solnon, and Khaled Ghédira.
Ant Colony Optimization for MultiObjective Optimization
Problems.
In 19th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI 2007), volume 1, pages 450–457. IEEE Computer Society
Press, Los Alamitos, CA, 2007.
[ bib ]

[27]

I. Alaya, Christine Solnon, and Khaled Ghédira.
Ant algorithm for the multidimensional knapsack problem.
In B. Filipič and J. Šilc, editors, International
Conference on Bioinspired Optimization Methods and their Applications (BIOMA
2004), pages 63–72, 2004.
[ bib 
http ]

[28]

Enrique Alba and Francisco Chicano.
ACOhg: dealing with huge graphs.
In D. Thierens et al., editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2007, pages 10–17. ACM Press,
New York, NY, 2007.
[ bib 
DOI ]

[29]

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

[30]

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

[31]

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

[32]

Mohamad Alissa, Kevin Sim, and Emma Hart.
Algorithm Selection Using Deep Learning without Feature
Extraction.
In M. LópezIbáñez, A. Auger, and T. Stützle,
editors, Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2019, pages 198–206, New York, NY, 2019. ACM Press.
[ bib 
DOI ]

[33]

Ali Allahverdi and Harun Aydilek.
Algorithms for nowait flowshops with total completion time
subject to makespan.
International Journal of Advanced Manufacturing Technology,
pages 1–15, 2013.
[ bib ]

[34]

Richard Allmendinger and Joshua D. Knowles.
Evolutionary Search in Lethal Environments.
In International Conference on Evolutionary Computation Theory
and Applications, pages 63–72. SciTePress, 2011.
[ bib 
DOI 
http ]

[35]

Richard Allmendinger.
Tuning evolutionary search for closedloop optimization.
PhD thesis, The University of Manchester, UK, 2012.
[ bib ]

[36]

Christian Almeder.
A hybrid optimization approach for multilevel capacitated
lotsizing problems.
European Journal of Operational Research, 200(2):599–606,
2010.
[ bib 
DOI ]
Solving multilevel capacitated lotsizing problems
is still a challenging task, in spite of increasing
computational power and faster algorithms. In this
paper a new approach combining an antbased
algorithm with an exact solver for (mixedinteger)
linear programs is presented. A MAXMIN 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
mixedinteger problems are developed and integrated
into the ant algorithm. This hybrid approach
provides superior results for small and mediumsized
problems in comparison to the existing approaches in
the literature. For largescale problems the
performance of this method is among the best
Keywords: Ant colony optimization, Manufacturing, Material
requirements planning, Mixedinteger programming

[37]

A. Alsheddy and E. Tsang.
Guided Pareto local search and its application to the 0/1
multiobjective knapsack problems.
In M. Caserta and S. Voß, editors, Proceedings of MIC 2009,
the 8th Metaheuristics International Conference, Hamburg, Germany, 2010.
University of Hamburg.
[ bib ]

[38]

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

[39]

Sanae Amani, Mahnoosh Alizadeh, and Christos Thrampoulidis.
Linear Stochastic Bandits Under Safety Constraints.
In H. M. Wallach, H. Larochelle, A. Beygelzimer,
F. d'AlchéBuc, E. B. Fox, and R. Garnett, editors, Advances in
Neural Information Processing Systems (NIPS 32), pages 9256–9266, 2019.
[ bib 
http ]

[40]

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

[41]

Christophe Andrieu, Nando de Freitas, Arnaud Doucet, and Michael I. Jordan.
An Introduction to MCMC for Machine Learning.
Machine Learning, 50(12):5–43, 2003.
[ bib ]

[42]

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

[43]

Klaus Andersen, René Victor Valqui Vidal, and Villy Bæk Iversen.
Design of a Teleprocessing Communication Network Using Simulated
Annealing.
In R. V. V. Vidal, editor, Applied Simulated Annealing, pages
201–215. Springer, 1993.
[ bib ]

[44]

J. H. Andersen and R. S. Powell.
The Use of Continuous Decision Variables in an Optimising Fixed
Speed Pump Scheduling Algorithm.
In R. S. Powell and K. S. Hindi, editors, Computing and Control
for the Water Industry, pages 119–128. Research Studies Press Ltd., 1999.
[ bib ]

[45]

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

[46]

Eric Angel, Evripidis Bampis, and Laurent Gourvés.
Approximating the Pareto curve with local search for the
bicriteria TSP(1,2) problem.
Theoretical Computer Science, 310(13):135–146, 2004.
[ bib 
DOI ]
Keywords: Archiving, Local search, Multicriteria TSP,
Approximation algorithms

[47]

D. Anghinolfi, A. Boccalatte, M. Paolucci, and C. Vecchiola.
Performance Evaluation of an Adaptive Ant Colony Optimization
Applied to Single Machine Scheduling.
In X. Li et al., editors, Simulated Evolution and Learning, 7th
International Conference, SEAL 2008, volume 5361 of Lecture Notes in
Computer Science, pages 411–420. Springer, Heidelberg, Germany, 2008.
[ bib ]

[48]

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

[49]

Daniel Angus.
PopulationBased Ant Colony Optimisation for Multiobjective
Function Optimisation.
In M. Randall, H. A. Abbass, and J. Wiles, editors, Progress in
Artificial Life (ACAL), volume 4828 of Lecture Notes in Computer
Science, pages 232–244. Springer, Heidelberg, Germany, 2007.
[ bib 
DOI ]

[50]

Kurt Anstreicher, Nathan Brixius, JeanPierre Goux, and Jeff Linderoth.
Solving large quadratic assignment problems on computational
grids.
Mathematical Programming Series B, 91(3):563–588, February
2002.
[ bib 
DOI ]
The quadratic assignment problem (QAP) is among the hardest
combinatorial optimization problems. Some instances of size
n = 30 have remained unsolved for decades. The solution of
these problems requires both improvements in mathematical
programming algorithms and the utilization of powerful
computational platforms. In this article we describe a novel
approach to solve QAPs using a stateoftheart
branchandbound 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.

[51]

J. Ansel, S. Kamil, K. Veeramachaneni, J. RaganKelley, J. Bosboom, U. M.
O'Reilly, and S. Amarasinghe.
OpenTuner: An extensible framework for program autotuning.
In Proceedings of the 23rd International Conference on Parallel
Architectures and Compilation, pages 303–315, New York, NY, 2014. ACM
Press.
[ bib 
DOI ]

[52]

Carlos Ansótegui, Yuri Malitsky, Horst Samulowitz, Meinolf Sellmann, and
Kevin Tierney.
ModelBased Genetic Algorithms for Algorithm Configuration.
In Q. Yang and M. Wooldridge, editors, Proceedings of the
TwentyFourth International Joint Conference on Artificial Intelligence
(IJCAI15), pages 733–739. IJCAI/AAAI Press, Menlo Park, CA, 2015.
[ bib 
DOI ]
Keywords: GGA++

[53]

Carlos Ansótegui, Yuri Malitsky, and Meinolf Sellmann.
MaxSAT by Improved InstanceSpecific Algorithm Configuration.
In D. Stracuzzi et al., editors, Proceedings of the AAAI
Conference on Artificial Intelligence, pages 2594–2600. AAAI Press, 2014.
[ bib ]

[54]

Carlos Ansótegui, Meinolf Sellmann, and Kevin Tierney.
A GenderBased Genetic Algorithm for the Automatic Configuration
of Algorithms.
In I. P. Gent, editor, Principles and Practice of Constraint
Programming, CP 2009, volume 5732 of Lecture Notes in Computer
Science, pages 142–157. Springer, Heidelberg, Germany, 2009.
[ bib 
DOI ]
Keywords: GGA

[55]

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

[56]

David Applegate, Robert E. Bixby, Vasek Chvátal, and William J. Cook.
Finding Cuts in the TSP.
Technical Report 95–05, DIMACS Center, Rutgers University,
Piscataway, NJ, USA, March 1995.
[ bib ]

[57]

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

[58]

David Applegate, Robert E. Bixby, Vasek Chvátal, and William J. Cook.
Finding Tours in the TSP.
Technical Report 99885, Forschungsinstitut für Diskrete
Mathematik, University of Bonn, Germany, 1999.
[ bib ]

[59]

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

[60]

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

[61]

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

[62]

David Applegate, Robert E. Bixby, Vasek Chvátal, and William J. Cook.
The Traveling Salesman Problem: A Computational Study.
Princeton University Press, Princeton, NJ, 2006.
[ bib ]

[63]

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

[64]

Jay April, Fred Glover, James P. Kelly, and Manuel Laguna.
Simulationbased optimization: Practical introduction to
simulation optimization.
In S. E. Chick, P. J. Sanchez, D. M. Ferrin, and D. J. Morrice,
editors, Proceedings of the 35th Winter Simulation Conference: Driving
Innovation, volume 1, pages 71–78, New York, NY, December 2003. ACM Press.
[ bib 
DOI ]

[65]

Florian Arnold, Ítalo Santana, Kenneth Sörensen, and Thibaut Vidal.
PILS: Exploring highorder neighborhoods bypattern mining and
injection.
Arxiv preprint arXiv:1912.11462, 2019.
[ bib 
http ]

[66]

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

[67]

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

[68]

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

[69]

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

[70]

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

[71]

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

[72]

Etor Arza, Josu Ceberio, Aritz Pérez, and Ekhine Irurozki.
Approaching the quadratic assignment problem with kernels of
mallows models under the hamming distance.
In M. LópezIbáñez, A. Auger, and T. Stützle,
editors, GECCO'19 Companion. ACM Press, New York, NY, 2019.
[ bib 
DOI ]
Keywords: QAP, EDA, Mallows

[73]

Y. Asahiro, K. Iwama, and E. Miyano.
Random Generation of Test Instances with Controlled Attributes.
In D. S. Johnson and M. A. Trick, editors, Cliques, Coloring,
and Satisfiability: Second DIMACS Implementation Challenge, volume 26 of
DIMACS Series on Discrete Mathematics and Theoretical Computer
Science, pages 377–393. American Mathematical Society, Providence, RI,
1996.
[ bib ]

[74]

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

[75]

N. Ascheuer.
Hamiltonian Path Problems in the Online Optimization of
Flexible Manufacturing Systems.
PhD thesis, Technische Universität Berlin, Berlin, Germany, 1995.
[ bib ]

[76]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,
Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin.
Attention Is All You Need.
Arxiv preprint arXiv:1706.03762, 2017.
[ bib 
http ]
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoderdecoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 EnglishtoGerman translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 EnglishtoFrench translation task, our model establishes a new singlemodel stateoftheart BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

[77]

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

[78]

R. Atkinson, Jakobus E. van Zyl, Godfrey A. Walters, and Dragan A. Savic.
Genetic algorithm optimisation of levelcontrolled pumping
station operation.
In Water network modelling for optimal design and management,
pages 79–90. Centre for Water Systems, Exeter, UK, 2000.
[ bib ]

[79]

Charles Audet, CongKien Dang, and Dominique Orban.
Algorithmic Parameter Optimization of the DFO Method with the
OPAL Framework.
In K. Naono, K. Teranishi, J. Cavazos, and R. Suda, editors,
Software Automatic Tuning: From Concepts to StateoftheArt Results, pages
255–274. Springer, 2010.
[ bib ]

[80]

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

[81]

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

[82]

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

[83]

Peter Auer.
Using Confidence Bounds for ExploitationExploration
Tradeoffs.
Journal of Machine Learning Research, 3:397–422, November
2002.
[ bib ]
We show how a standard tool from statistics — namely
confidence bounds — can be used to elegantly deal with
situations which exhibit an exploitationexploration
tradeoff. Our technique for designing and analyzing
algorithms for such situations is general and can be applied
when an algorithm has to make exploitationversusexploration
decisions based on uncertain information provided by a random
process. We apply our technique to two models with such an
exploitationexploration tradeoff. 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}).

[84]

Peter Auer, Nicolo CesaBianchi, and Paul Fischer.
Finitetime analysis of the multiarmed bandit problem.
Machine Learning, 47(23):235–256, 2002.
[ bib ]

[85]

Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler.
Articulating User Preferences in ManyObjective Problems by
Sampling the Weighted Hypervolume.
In F. Rothlauf, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2009, pages 555–562. ACM Press,
New York, NY, 2009.
[ bib ]

[86]

Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler.
Investigating and Exploiting the Bias of the Weighted
Hypervolume to Articulate User Preferences.
In F. Rothlauf, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2009, pages 563–570. ACM Press,
New York, NY, 2009.
[ bib ]

[87]

Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler.
Theory of the hypervolume indicator: optimal μdistributions
and the choice of the reference point.
In F. Rothlauf, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2009, pages 87–102. ACM Press,
New York, NY, 2009.
[ bib ]

[88]

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

[89]

Anne Auger, Dimo Brockhoff, Manuel LópezIbáñez, Kaisa Miettinen,
Boris Naujoks, and Günther Rudolph.
Which questions should be asked to find the most appropriate
method for decision making and problem solving? (Working Group
“Algorithm Design Methods”).
In S. Greco, J. D. Knowles, K. Miettinen, and E. Zitzler, editors,
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Anne Auger and Nikolaus Hansen.
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Anne Auger and Nikolaus Hansen.
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Andreea Avramescu, Richard Allmendinger, and Manuel LópezIbáñez.
A MultiObjective MultiType Facility Location Problem for the
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in Computer Science, pages 388–403. Springer, Cham, Switzerland, 2021.
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Advances in personalised medicine targeting specific
subpopulations 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
multiobjective mathematical model for the
locationallocation 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 multiobjective genetic algorithm with a novel
population initialisation procedure is used as solution
method.
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We consider the optimization of a computer model where each
simulation either fails or returns a valid output
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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
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from this extended criterion. We also study the practical
performances of this algorithm, both on simulated data and on
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Locating distribution centers is critical for humanitarians
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Improvement Strategies for the FRace Algorithm: Sampling
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Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
Adaptive Sampling Size and Importance Sampling in
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Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
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Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Zhi Yuan, and Marco
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Consider the following restricted (symmetric or
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given an initial ordering of the n cities and an
integer k > 0, find a minimumcost
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
dynamicprogramming algorithm that solves this
problem in time linear in n, though exponential in
k. Some important realworld 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 sourcesink paths in this
network are in onetoone correspondence with tours
that satisfy the postulated precedence
constraints. In this paper we discuss an
implementation of the dynamicprogramming algorithm
for the general case when the integer k is replaced
with cityspecific 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 JustinTime
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 exponentialsize neighborhood. For this
case, we implement an iterated version of our
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tour produced by a first application of the
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Steven C. Bankes.
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Agentbased models (ABM) are examples of complex adaptive
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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
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Thomas BartzBeielstein.
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Grid search and manual search are the most widely
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efficient for hyperparameter optimization than
trials on a grid. Empirical evidence comes from a
comparison with a large previous study that used
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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
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32dimensional configuration space found
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hyperparameters really matter, but that different
hyperparameters 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 hyperparameters because most hyperparameters do
not matter much. We anticipate that growing interest
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The goal of multiobjective optimization is to find
a set of best compromise solutions for typically
conflicting objectives. Due to the complex nature of
most reallife 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
multiobjective optimizers providing them, unary
quality measures are usually applied. Among these,
the hypervolume indicator (or
Smetric) is of particular relevance due to
its favorable properties. Moreover, this indicator
has been successfully integrated into stochastic
optimizers, such as evolutionary algorithms, where
it serves as a guidance criterion for finding good
approximations to the Pareto front. Recent results
show that computing the hypervolume indicator can be
seen as solving a specialized version of Klee's
Measure Problem. In general, Klee's Measure Problem
can be solved with O(n logn +
n^{d/2}logn) comparisons for an input instance of
size n in d dimensions; as of this writing, it
is unknown whether a lower bound higher than
Ω(n logn) can be proven. In this article,
we derive a lower bound of Ω(nlogn) for the
complexity of computing the hypervolume indicator in
any number of dimensions d>1 by reducing the
socalled UniformGap problem to it. For
the three dimensional case, we also present a
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Nicola Beume and Günther Rudolph.
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Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
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Automatic Generation of MultiObjective ACO Algorithms for the
Biobjective Knapsack.
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Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
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Automatic Generation of MOACO Algorithms for the Biobjective
Bidimensional Knapsack Problem: Supplementary material.
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Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
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Knapsack: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2012016/, 2013.
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[202]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Deconstructing MultiObjective Evolutionary Algorithms: An
Iterative Analysis on the Permutation Flowshop: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2013010/, 2013.
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[203]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
An Analysis of Local Search for the Biobjective Bidimensional
Knapsack Problem.
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[204]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic ComponentWise Design of MultiObjective
Evolutionary Algorithms.
Technical Report TR/IRIDIA/2014012, IRIDIA, Université Libre de
Bruxelles, Belgium, August 2014.
[ bib ]

[205]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Deconstructing MultiObjective Evolutionary Algorithms: An
Iterative Analysis on the Permutation Flowshop.
In P. M. Pardalos, M. G. C. Resende, C. Vogiatzis, and J. L.
Walteros, editors, Learning and Intelligent Optimization, 8th
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Computer Science, pages 57–172. Springer, Heidelberg, Germany, 2014.
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supplementary material ]

[206]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic Design of Evolutionary Algorithms for MultiObjective
Combinatorial Optimization.
In T. BartzBeielstein, J. Branke, B. Filipič, and J. Smith,
editors, PPSN 2014, volume 8672 of Lecture Notes in Computer
Science, pages 508–517. Springer, Heidelberg, Germany, 2014.
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[207]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic Design of Evolutionary Algorithms for MultiObjective
Combinatorial Optimization.
http://iridia.ulb.ac.be/supp/IridiaSupp2014007/, 2014.
[ bib ]

[208]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic ComponentWise Design of MultiObjective Evolutionary
Algorithms.
http://iridia.ulb.ac.be/supp/IridiaSupp2014010/, 2015.
[ bib ]

[209]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
To DE or Not to DE? Multiobjective Differential Evolution
Revisited from a ComponentWise Perspective: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2015001/, 2015.
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[210]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
To DE or Not to DE? Multiobjective Differential Evolution
Revisited from a ComponentWise Perspective.
In A. GasparCunha, C. H. Antunes, and C. A. Coello Coello,
editors, Evolutionary Multicriterion Optimization, EMO 2015 Part I,
volume 9018 of Lecture Notes in Computer Science, pages 48–63.
Springer, Heidelberg, Germany, 2015.
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[211]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Comparing DecompositionBased and Automatically
ComponentWise Designed MultiObjective Evolutionary Algorithms.
In A. GasparCunha, C. H. Antunes, and C. A. Coello Coello,
editors, Evolutionary Multicriterion Optimization, EMO 2015 Part I,
volume 9018 of Lecture Notes in Computer Science, pages 396–410.
Springer, Heidelberg, Germany, 2015.
[ bib 
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[212]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic ComponentWise Design of MultiObjective Evolutionary
Algorithms.
IEEE Transactions on Evolutionary Computation, 20(3):403–417,
2016.
[ bib 
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pdf 
supplementary material ]

[213]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatically designing and understanding evolutionary
algorithms for multi and manyobjective optimization, 2016.
To be submitted.
[ bib ]

[214]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
A LargeScale Experimental Evaluation of HighPerforming Multi
and ManyObjective Evolutionary Algorithms.
http://iridia.ulb.ac.be/supp/IridiaSupp2015007/, 2017.
[ bib ]

[215]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
A LargeScale Experimental Evaluation of HighPerforming Multi
and ManyObjective Evolutionary Algorithms.
Technical Report TR/IRIDIA/2017005, IRIDIA, Université Libre de
Bruxelles, Belgium, February 2017.
[ bib ]

[216]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
A LargeScale Experimental Evaluation of HighPerforming Multi
and ManyObjective Evolutionary Algorithms.
Evolutionary Computation, 26(4):621–656, 2018.
[ bib 
DOI 
pdf 
supplementary material ]
Research on multiobjective 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
higherlevel algorithmic components related to
multiobjective optimization (MO), which characterize each
particular MOEA, and the underlying parameterssuch 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 lowperforming 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 manyobjective
problems. For example, under certain conditions,
indicatorbased MOEAs are more competitive for such problems
than previously assumed. We also analyze problemspecific
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.

[217]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
An Empirical Assessment of the Properties of Inverted
Generational Distance Indicators on Multi and Manyobjective Optimization.
In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. M.
Wiecek, Y. Jin, and C. Grimme, editors, Evolutionary Multicriterion
Optimization, EMO 2017, Lecture Notes in Computer Science, pages 31–45.
Springer International Publishing, Cham, Switzerland, 2017.
[ bib 
DOI ]

[218]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
An empirical assessment of the properties of inverted
generational distance indicators on multi and manyobjective optimization:
Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2016006/, 2016.
[ bib ]

[219]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatically Designing StateoftheArt Multi and
ManyObjective Evolutionary Algorithms.
Evolutionary Computation, 28(2):195–226, 2020.
[ bib 
DOI 
pdf 
supplementary material ]
A recent comparison of wellestablished multiobjective
evolutionary algorithms (MOEAs) has helped better identify
the current stateoftheart 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
stateoftheart performance for multi and manyobjective
continuous optimization. Our work is based on two main
considerations. The first is that highperforming 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 highperforming
MOEA designs that optimize a given performance metric and
present stateoftheart performance. In the second part, we
propose a multiobjective formulation for the automatic MOEA
design, which proves critical for the context of
manyobjective optimization due to the disagreement of
established performance metrics. Our proposed formulation
leads to an automatically designed MOEA that presents
stateoftheart performance according to a set of metrics,
rather than a single one.

[220]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatically Designing StateoftheArt Multi and
ManyObjective Evolutionary Algorithms: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2016004/, 2019.
[ bib ]

[221]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Archiver Effects on the Performance of Stateoftheart Multi
and Manyobjective Evolutionary Algorithms: Supplementary material.
In M. LópezIbáñez, A. Auger, and T. Stützle,
editors, Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2019. ACM Press, New York, NY, 2019.
[ bib 
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pdf 
supplementary material ]

[222]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Archiver Effects on the Performance of Stateoftheart Multi
and Manyobjective Evolutionary Algorithms: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2019004/, 2019.
[ bib ]

[223]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic Configuration of Multiobjective Optimizers and
Multiobjective Configuration.
In T. BartzBeielstein, B. Filipič, P. Korošec, and E.G.
Talbi, editors, HighPerformance SimulationBased Optimization, pages
69–92. Springer International Publishing, Cham, Switzerland, 2020.
[ bib 
DOI ]
Heuristic optimizers are an important tool in academia and industry, and their performanceoptimizing 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 multiobjective optimization intersect. The first is the automatic configuration of multiobjective 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, highperforming multiobjective evolutionary algorithms. The second aspect is the research on multiobjective configuration, that is, the possibility of using multiple performance metrics for the evaluation of algorithm configurations. We highlight some few examples in this direction.

[224]

Leonardo C. T. Bezerra.
A componentwise approach to multiobjective evolutionary
algorithms: from flexible frameworks to automatic design.
PhD thesis, IRIDIA, École polytechnique, Université Libre de
Bruxelles, Belgium, 2016.
[ bib ]
Supervised by Thomas Stützle and Manuel LópezIbáñez

[225]

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

[226]

Leonora Bianchi, Marco Dorigo, L. M. Gambardella, and Walter J. Gutjahr.
A survey on metaheuristics for stochastic combinatorial
optimization.
Natural Computing, 8(2):239–287, 2009.
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[227]

Leonora Bianchi, L. M. Gambardella, and Marco Dorigo.
An Ant Colony Optimization Approach to the Probabilistic
Traveling Salesman Problem.
In J. J. Merelo et al., editors, Parallel Problem Solving from
Nature, PPSN VII, volume 2439 of Lecture Notes in Computer Science,
pages 883–892. Springer, Heidelberg, Germany, 2002.
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[228]

Armin Biere.
Yet another Local Search Solver and Lingeling and Friends
Entering the SAT Competition 2014.
In A. Belov, D. Diepold, M. Heule, and M. Järvisalo, editors,
Proceedings of SAT Competition 2014: Solver and Benchmark Descriptions,
volume B20142 of Science Series of Publications B, pages 39–40.
University of Helsinki, 2014.
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[229]

André Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter,
Chris Fawcett, and Holger H. Hoos.
Efficient Parameter Importance Analysis via Ablation with
Surrogates.
In S. P. Singh and S. Markovitch, editors, Proceedings of the
AAAI Conference on Artificial Intelligence. AAAI Press, February 2017.
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[230]

André Biedenkapp, Joshua Marben, Marius Lindauer, and Frank Hutter.
Cave: Configuration assessment, visualization and evaluation.
In R. Battiti, M. Brunato, I. Kotsireas, and P. M. Pardalos, editors,
Learning and Intelligent Optimization, 12th International Conference,
LION 12, volume 11353 of Lecture Notes in Computer Science, pages
115–130, Cham, Switzerland, 2018. Springer.
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[231]

George Bilchev and Ian C. Parmee.
The Ant Colony Metaphor for Searching Continuous Design Spaces.
In T. C. Fogarty, editor, Evolutionary Computing, AISB
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[ bib 
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[232]

M. Binois, D. Ginsbourger, and O. Roustant.
Quantifying uncertainty on Pareto fronts with Gaussian
process conditional simulations.
European Journal of Operational Research, 243(2):386–394,
2015.
[ bib 
DOI ]
Multiobjective optimization algorithms aim at finding
Paretooptimal solutions. Recovering Pareto fronts or Pareto
sets from a limited number of function evaluations are
challenging problems. A popular approach in the case of
expensivetoevaluate functions is to appeal to
metamodels. Kriging has been shown efficient as a base for
sequential multiobjective 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 Krigingbased multiobjective 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 nondominated
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 Krigingbased
multiobjective optimization algorithms to accurately learn
the Pareto front.
Keywords: Attainment function, Expected Hypervolume Improvement,
Kriging, Multiobjective optimization, Vorob'ev expectation

[233]

Mauro Birattari, Prasanna Balaprakash, and Marco Dorigo.
The ACO/FRACE algorithm for combinatorial optimization under
uncertainty.
In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F.
Hartl, and M. Reimann, editors, Metaheuristics – Progress in Complex
Systems Optimization, volume 39 of Operations Research/Computer Science
Interfaces Series, pages 189–203. Springer, New York, NY, 2006.
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[234]

Mauro Birattari, Prasanna Balaprakash, Thomas Stützle, and Marco Dorigo.
Estimation Based Local Search for Stochastic Combinatorial
Optimization.
INFORMS Journal on Computing, 20(4):644–658, 2008.
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[235]

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

[236]

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

[237]

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

[238]

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

[239]

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

[240]

Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, and Thomas Stützle.
FRace and Iterated FRace: An Overview.
In T. BartzBeielstein, M. Chiarandini, L. Paquete, and M. Preuss,
editors, Experimental Methods for the Analysis of Optimization
Algorithms, pages 311–336. Springer, Berlin, Germany, 2010.
[ bib 
DOI ]
Keywords: Frace, iterated Frace, irace, tuning

[241]

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

[242]

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

[243]

Mauro Birattari.
Tuning Metaheuristics: A Machine Learning Perspective, volume
197 of Studies in Computational Intelligence.
Springer, Berlin, Heidelberg, 2009.
[ bib 
DOI ]

[244]

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

[245]

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

[246]

Francesco Biscani, Dario Izzo, and Chit Hong Yam.
A Global Optimisation Toolbox for Massively Parallel Engineering
Optimisation.
Arxiv preprint arXiv:1004.3824, 2010.
[ bib 
http ]
A software platform for global optimisation, called PaGMO,
has been developed within the Advanced Concepts Team (ACT) at
the European Space Agency, and was recently released as an
opensource project. PaGMO is built to tackle
highdimensional global optimisation problems, and it has
been successfully used to find solutions to reallife
engineering problems among which the preliminary design of
interplanetary spacecraft trajectories  both chemical
(including multiple flybys and deepspace maneuvers) and
lowthrust (limited, at the moment, to single phase
trajectories), the inverse design of nanostructured
radiators and the design of nonreactive 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 objectoriented architecture
providing a clean and easilyextensible optimisation
framework. Adoption of multithreaded programming ensures the
efficient exploitation of modern multicore 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
speedup and improve the optimisation process. In addition to
the C++ interface, PaGMO's capabilities are exposed to the
highlevel language Python, so that it is possible to easily
use PaGMO in an interactive session and take advantage of the
numerous scientific Python libraries available.
Keywords: PaGMO

[247]

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

[248]

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

[249]

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

[250]

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

[251]

Erdem Biyik, Jonathan Margoliash, Shahrouz Ryan Alimo, and Dorsa
Sadigh.
Efficient and Safe Exploration in Deterministic Markov
Decision Processes with Unknown Transition Models.
In 2019 American Control Conference (ACC), pages 1792–1799.
IEEE, 2019.
[ bib 
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[252]

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

[253]

Craig Blackmore, Oliver Ray, and Kerstin Eder.
Automatically Tuning the GCC Compiler to Optimize the
Performance of Applications Running on Embedded Systems.
Arxiv preprint arXiv:1703.08228, 2017.
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[254]

María J. Blesa and Christian Blum.
Ant Colony Optimization for the Maximum EdgeDisjoint Paths
Problem.
In G. R. Raidl et al., editors, Applications of Evolutionary
Computing, Proceedings of EvoWorkshops 2004, volume 3005 of Lecture
Notes in Computer Science, pages 160–169. Springer, Heidelberg, Germany,
2004.
[ bib ]

[255]

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

[256]

Aymeric Blot, Holger H. Hoos, Laetitia Jourdan, MarieEléonore
KessaciMarmion, and Heike Trautmann.
MOParamILS: A Multiobjective Automatic Algorithm
Configuration Framework.
In P. Festa, M. Sellmann, and J. Vanschoren, editors, Learning
and Intelligent Optimization, 10th International Conference, LION 10, volume
10079 of Lecture Notes in Computer Science, pages 32–47. Springer,
Cham, Switzerland, 2016.
[ bib ]

[257]

Aymeric Blot, Laetitia Jourdan, and MarieEléonore KessaciMarmion.
Automatic design of multiobjective local search algorithms:
case study on a biobjective permutation flowshop scheduling problem.
In P. A. N. Bosman, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2017, pages 227–234. ACM Press,
New York, NY, 2017.
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[258]

Aymeric Blot, Manuel LópezIbáñez, MarieEléonore
KessaciMarmion, and Laetitia Jourdan.
New Initialisation Techniques for MultiObjective Local Search:
Application to the Biobjective Permutation Flowshop.
In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete,
and D. Whitley, editors, Parallel Problem Solving from Nature  PPSN
XV, volume 11101 of Lecture Notes in Computer Science, pages 323–334.
Springer, Cham, 2018.
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[259]

Aymeric Blot, Alexis Pernet, Laetitia Jourdan, MarieEléonore
KessaciMarmion, and Holger H. Hoos.
Automatically Configuring Multiobjective Local Search Using
Multiobjective Optimisation.
In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. M.
Wiecek, Y. Jin, and C. Grimme, editors, Evolutionary Multicriterion
Optimization, EMO 2017, Lecture Notes in Computer Science, pages 61–76.
Springer International Publishing, Cham, Switzerland, 2017.
[ bib ]

[260]

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

[261]

Christian Blum.
BeamACO for simple assembly line balancing.
INFORMS Journal on Computing, 20(4):618–627, 2008.
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DOI ]

[262]

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

[263]

Christian Blum, María J. Blesa, and Manuel LópezIbáñez.
Beam Search for the Longest Common Subsequence Problem.
Technical Report LSI0829, Department LSI, Universitat
Politècnica de Catalunya, 2008.
Published in Computers & Operations
Research [264].
[ bib ]

[264]

Christian Blum, María J. Blesa, and Manuel LópezIbáñez.
Beam search for the longest common subsequence problem.
Computers & Operations Research, 36(12):3178–3186, 2009.
[ bib 
DOI 
pdf ]
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 stateoftheart approaches not only
in solution quality but often also in computation time.

[265]

Christian Blum, Borja Calvo, and María J. Blesa.
FrogCOL and FrogMIS: New Decentralized Algorithms for
Finding Large Independent Sets in Graphs.
Swarm Intelligence, 9(23):205–227, 2015.
[ bib 
DOI ]
Keywords: irace

[266]

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

[267]

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

[268]

Christian Blum and Marco Dorigo.
Search Bias in Ant Colony Optimization: On the Role of
CompetitionBalanced Systems.
IEEE Transactions on Evolutionary Computation, 9(2):159–174,
2005.
[ bib ]

[269]

Christian Blum and Manuel LópezIbáñez.
Ant Colony Optimization.
In The Industrial Electronics Handbook: Intelligent Systems.
CRC Press, 2nd edition, 2011.
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[270]

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

[271]

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

[272]

Christian Blum and Gabriela Ochoa.
A comparative analysis of two matheuristics by means of merged
local optima networks.
European Journal of Operational Research, 290(1):36–56, 2021.
[ bib ]

[273]

Christian Blum, Pedro Pinacho, Manuel LópezIbáñez, and José A.
Lozano.
Construct, Merge, Solve & Adapt: A New General Algorithm for
Combinatorial Optimization.
Computers & Operations Research, 68:75–88, 2016.
[ bib 
DOI ]
Keywords: irace

[274]

Christian Blum, Jakob Puchinger, Günther R. Raidl, and Andrea Roli.
Hybrid Metaheuristics in Combinatorial Optimization: A Survey.
Applied Soft Computing, 11(6):4135–4151, 2011.
[ bib ]

[275]

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

[276]

Christian Blum and Andrea Roli.
Metaheuristics in Combinatorial Optimization: Overview and
Conceptual Comparison.
ACM Computing Surveys, 35(3):268–308, 2003.
[ bib ]

[277]

Christian Blum and Andrea Roli.
Hybrid metaheuristics: an introduction.
In C. Blum, M. J. Blesa, A. Roli, and M. Sampels, editors,
Hybrid Metaheuristics: An emergent approach for optimization, volume 114 of
Studies in Computational Intelligence, pages 1–30. Springer, Berlin,
Germany, 2008.
[ bib ]

[278]

Christian Blum and M. Sampels.
An Ant Colony Optimization Algorithm for Shop Scheduling
Problems.
Journal of Mathematical Modelling and Algorithms,
3(3):285–308, 2004.
[ bib 
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[279]

Christian Blum and M. Yábar Vallès.
Multilevel ant colony optimization for DNA sequencing by
hybridization.
In F. Almeida et al., editors, Hybrid Metaheuristics, volume
4030 of Lecture Notes in Computer Science, pages 94–109. Springer,
Heidelberg, Germany, 2006.
[ bib 
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[280]

Christian Blum, M. Yábar Vallès, and María J. Blesa.
An ant colony optimization algorithm for DNA sequencing by
hybridization.
Computers & Operations Research, 35(11):3620–3635, 2008.
[ bib ]

[281]

Kenneth D. Boese, Andrew B. Kahng, and Sudhakar Muddu.
A New Adaptive MultiStart Technique for Combinatorial Global
Optimization.
Operations Research Letters, 16(2):101–113, 1994.
[ bib ]
Keywords: bigvalley hypothesis, TSP, landscape analysis

[282]

K. D. Boese.
Models for Iterative Global Optimization.
PhD thesis, University of California, Computer Science Department,
Los Angeles, CA, 1996.
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[283]

Marko Bohanec.
Decision making: a computerscience and informationtechnology
viewpoint.
Interdisciplinary Description of Complex Systems, 7(2):22–37,
2009.
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[284]

Ihor O. Bohachevsky, Mark E. Johnson, and Myron L. Stein.
Generalized Simulated Annealing for Function Optimization.
Technometrics, 28(3):209–217, 1986.
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[285]

Béla Bollobás.
Random Graphs.
Cambridge University Press, New York, NY, 2nd edition, 2001.
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Grady Booch, James E. Rumbaugh, and Ivar Jacobson.
The Unified Modeling Language User Guide.
AddisonWesley, 2nd edition, 2005.
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[287]

P. C. Borges and Michael Pilegaard Hansen.
A basis for future successes in multiobjective combinatorial
optimization.
Technical Report IMMREP19988, Institute of Mathematical Modelling,
Technical University of Denmark, Lyngby, Denmark, 1998.
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[288]

P. C. Borges.
CHESS  Changing Horizon Efficient Set Search: A simple
principle for multiobjective optimization.
Journal of Heuristics, 6(3):405–418, 2000.
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[289]

Allan Borodin and Ran ElYaniv.
Online computation and competitive analysis.
Cambridge University Press, New York, NY, 1998.
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[290]

Endre Boros, Peter L. Hammer, and Gabriel Tavares.
Local search heuristics for Quadratic Unconstrained Binary
Optimization (QUBO).
Journal of Heuristics, 13(2):99–132, 2007.
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[291]

Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, and Hannah R.
Rothstein.
Introduction to MetaAnalysis.
Wiley, 2009.
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[292]

JeanCharles de Borda.
Mémoire sur les Élections au Scrutin.
Histoire de l'Académie Royal des Sciences, 1781.
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Keywords: ranking

[293]

Bernhard E. Boser, Isabelle Guyon, and Vladimir Vapnik.
A Training Algorithm for Optimal Margin Classifiers.
In D. Haussler, editor, COLT'92, pages 144–152. ACM Press,
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Proposed SVM

[294]

Hozefa M. Botee and Eric Bonabeau.
Evolving Ant Colony Optimization.
Advances in Complex Systems, 1:149–159, 1998.
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[295]

Marco Botte and Anita Schöbel.
Dominance for multiobjective robust optimization concepts.
European Journal of Operational Research, 273(2):430–440,
2019.
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[296]

Salim Bouamama, Christian Blum, and Abdellah Boukerram.
A Populationbased Iterated Greedy Algorithm for the Minimum
Weight Vertex Cover Problem.
Applied Soft Computing, 12(6):1632–1639, 2012.
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[297]

Géraldine Bous, Philippe Fortemps, François Glineur, and Marc Pirlot.
ACUTA: A novel method for eliciting additive value functions
on the basis of holistic preference statements.
European Journal of Operational Research, 206(2):435–444,
2010.
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K. Bouleimen and H. Lecocq.
A new efficient simulated annealing algorithm for the
resourceconstrained project scheduling problem and its multiple mode
version.
European Journal of Operational Research, 149(2):268–281,
2003.
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This paper describes new simulated annealing (SA)
algorithms for the resourceconstrained 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.
Keywords: multimode resourceconstrained project scheduling,
project scheduling, simulated annealing

[299]

Paul F. Boulos, Chun Hou Orr, Werner de Schaetzen, J. G. Chatila, Michael
Moore, Paul Hsiung, and Devan Thomas.
Optimal pump operation of water distribution systems using
genetic algorithms.
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V. Bowman and Jr. Joseph.
On the Relationship of the Tchebycheff Norm and the Efficient
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George E. P. Box and Norman R. Draper.
Response surfaces, mixtures, and ridge analyses.
John Wiley & Sons, 2007.
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A. Brandt.
Multilevel Computations: Review and Recent Developments.
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[305]

Jürgen Branke, Salvatore Corrente, Salvatore Greco, Milosz Kadzinski,
Manuel LópezIbáñez, Vincent Mousseau, Mauro Munerato, and Roman
Slowiński.
BehaviorRealistic Artificial DecisionMakers to Test
PreferenceBased Multiobjective Optimization Method (Working Group
“Machine DecisionMaking”).
In S. Greco, K. Klamroth, J. D. Knowles, and G. Rudolph, editors,
Understanding Complexity in Multiobjective Optimization (Dagstuhl
Seminar 15031), volume 5(1) of Dagstuhl Reports, pages 110–116.
Schloss Dagstuhl–LeibnizZentrum für Informatik, Germany, 2015.
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Keywords: multiple criteria decision making, evolutionary
multiobjective optimization

[306]

Yesnier Bravo, Javier Ferrer, Gabriel J. Luque, and Enrique Alba.
Smart Mobility by Optimizing the Traffic Lights: A New Tool for
Traffic Control Centers.
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(SmartCT 2016), Lecture Notes in Computer Science, pages 147–156.
Springer, Cham, Switzerland, 2016.
[ bib 
DOI ]
Urban traffic planning is a fertile area of Smart Cities to
improve efficiency, environmental care, and safety, since the
traffic jams and congestion are one of the biggest sources of
pollution and noise. Traffic lights play an important role in
solving these problems since they control the flow of the
vehicular network at the city. However, the increasing number
of vehicles makes necessary to go from a local control at one
single intersection to a holistic approach considering a
large urban area, only possible using advanced computational
resources and techniques. Here we propose HITUL, a system
that supports the decisions of the traffic control managers
in a large urban area. HITUL takes the real traffic
conditions and compute optimal traffic lights plans using
bioinspired techniques and microsimulations. We compare our
system against plans provided by experts. Our solutions not
only enable continuous traffic flows but reduce the
pollution. A case study of Málaga city allows us to
validate the approach and show its benefits for other cities
as well.
Keywords: Multiobjective optimization, Smart mobility, Traffic lights
planning

[307]

Jürgen Branke, Salvatore Greco, Roman Slowiński, and P Zielniewicz.
Interactive evolutionary multiobjective optimization driven by
robust ordinal regression.
Bulletin of the Polish Academy of Sciences: Technical Sciences,
58(3):347–358, 2010.
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[308]

S. C. Brailsford, Walter J. Gutjahr, M. S. Rauner, and W. Zeppelzauer.
Combined Discreteevent Simulation and Ant Colony Optimisation
Approach for Selecting Optimal Screening Policies for Diabetic Retinopathy.
Computational Management Science, 4(1):59–83, 2006.
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[309]

Jürgen Branke, T. Kaussler, and H. Schmeck.
Guidance in evolutionary multiobjective optimization.
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[310]

JeanPierre Brans and Bertrand Mareschal.
PROMETHEEGAIA. Une méthode d'aide à la décision
en présence de critères multiples.
Editions Ellipses, Paris, FR, 2002.
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[311]

JeanPierre Brans and Bertrand Mareschal.
PROMETHEE Methods.
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163–195. Springer, 2005.
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[312]

Jürgen Branke, S. Nguyen, C. W. Pickardt, and M. Zhang.
Automated Design of Production Scheduling Heuristics: A Review.
IEEE Transactions on Evolutionary Computation, 20(1):110–124,
2016.
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[313]

Jürgen Branke, C. Schmidt, and H. Schmeck.
Efficient fitness estimation in noisy environments.
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Conference on Genetic and Evolutionary Computation, GECCO 2001, pages
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[314]

Roland Braune and G. Zäpfel.
Shifting Bottleneck Scheduling for Total Weighted Tardiness
Minimization—A Computational Evaluation of Subproblem and Reoptimization
Heuristics.
Computers & Operations Research, 66:130–140, 2016.
[ bib ]

[315]

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

[316]

Jürgen Branke, Salvatore Corrente, Salvatore Greco, Roman Slowiński,
and P. Zielniewicz.
Using Choquet integral as preference model in interactive
evolutionary multiobjective optimization.
European Journal of Operational Research, 250(3):884–901,
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[317]

Jürgen Branke and Jawad Elomari.
Simultaneous tuning of metaheuristic parameters for various
computing budgets.
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ACM Press, New York, NY, 2011.
[ bib 
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[318]

Jürgen Branke and Jawad Elomari.
Racing with a Fixed Budget and a SelfAdaptive Significance
Level.
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2013.
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[319]

Jürgen Branke, S. S. Farid, and N. Shah.
Industry 4.0: a vision for personalized medicine supply chains?
Cell and Gene Therapy Insights, 2(2):263–270, 2016.
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[320]

Jürgen Branke, Salvatore Greco, Roman Slowiński, and Piotr
Zielniewicz.
Learning Value Functions in Interactive Evolutionary
Multiobjective Optimization.
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Karl Bringmann and Tobias Friedrich.
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Dimo Brockhoff, Johannes Bader, Lothar Thiele, and Eckart Zitzler.
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[ bib 
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Recently, there has been a large interest in setbased
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 Paretooptimality. 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 WHypE, 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.
Keywords: hypervolume, preferencebased search, multi objective
optimization, evolutionary algorithm

[333]

Dimo Brockhoff, Roberto Calandra, Manuel LópezIbáñez, Frank
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Metamodeling for (interactive) multiobjective optimization
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Eric Brochu, Vlad Cora, and Nando de Freitas.
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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
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Peter Brucker, Johann Hurink, and Frank Werner.
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Artur Brum and Marcus Ritt.
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Artur Brum and Marcus Ritt.
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On the Complexity of Computing the Hypervolume Indicator.
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Christian Leonardo CamachoVillalón, Marco Dorigo, and Thomas Stützle.
Why the Intelligent Water Drops Cannot Be Considered as a Novel
Algorithm.
In M. Dorigo, M. Birattari, A. L. Christensen, A. Reina, and
V. Trianni, editors, Swarm Intelligence, 11th International Conference,
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Christian Leonardo CamachoVillalón, Marco Dorigo, and Thomas Stützle.
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Paolo Campigotto and Andrea Passerini.
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reactive multiobjective optimizer.
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Felipe Campelo and Elizabeth F. Wanner.
Sample size calculations for the experimental comparison of
multiple algorithms on multiple problem instances.
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Gilles Caporossi.
Variable Neighborhood Search for Extremal Vertices : The
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J. Carlier.
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Alex Guimarães Cardoso de Sá, Walter José G. S. Pinto, Luiz
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RECIPE: A GrammarBased Framework for Automatically Evolving
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[382]

Ioannis Caragiannis, Ariel D. Procaccia, and Nisarg Shah.
When Do Noisy Votes Reveal the Truth?
In M. J. Kearns, R. P. McAfee, and É. Tardos, editors,
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143–160, New York, NY, 2013. ACM Press.
[ bib 
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A wellstudied 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
pairwisemajority 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 pairwisemajority consistent rules are
accurate in the limit, and provide a similar result for
another novel family of positiondominance consistent
rules. These characterizations capture three wellknown
distance functions.
Keywords: computer social choice, mallows model, sample complexity

[383]

Yves Caseau and François Laburthe.
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Yves Caseau, Glenn Silverstein, and François Laburthe.
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Diego Cattaruzza, Nabil Absi, Dominique Feillet, and Daniele Vigo.
An Iterated Local Search for the Multicommodity Multitrip
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Josu Ceberio, Ekhine Irurozki, Alexander Mendiburu, and José A. Lozano.
A distancebased ranking model estimation of distribution
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2014.
[ bib 
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The aim of this paper is twofold. First, we introduce a
novel general estimation of distribution algorithm to deal
with permutationbased 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 stateoftheart
approaches. Moreover, from the 220 benchmark instances
tested, the proposed hybrid approach obtains new best known
results in 152 cases. An indepth study of the results
suggests that the successful performance of the introduced
approach is due to the ability of the generalized Mallows
estimation of distribution algorithm to discover promising
regions in the search space.
Keywords: Estimation of distribution algorithms,Generalized Mallows
model,Permutation flowshop scheduling
problem,Permutationsbased optimization problems

[387]

Josu Ceberio, Alexander Mendiburu, and José A. Lozano.
Kernels of Mallows Models for Solving Permutationbased
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Eranda Çela.
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Vladimír Černý.
A Thermodynamical Approach to the Traveling Salesman Problem: An
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Sara Ceschia, Luca Di Gaspero, and Andrea Schaerf.
Design, Engineering, and Experimental Analysis of a Simulated
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Amadeo Cesta, Angelo Oddi, and Stephen F. Smith.
Iterative Flattening: A Scalable Method for Solving
MultiCapacity Scheduling Problems.
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Sara Ceschia and Andrea Schaerf.
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Sara Ceschia, Andrea Schaerf, and Thomas Stützle.
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S. T. H. Chang.
Optimizing the Real Time Operation of a Pumping Station at a
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Honors thesis, Department of Civil and Environmental Engineering, The
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Donald V. Chase and Lindell E. Ormsbee.
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Hsinchun Chen, Roger HL Chiang, and Veda C Storey.
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Fei Chen, Yang Gao, Zhaoqian Chen, and Shifu Chen.
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[403]

Clément Chevalier, David Ginsbourger, Julien Bect, and Ilya Molchanov.
Estimating and Quantifying Uncertainties on Level Sets Using the
Vorob'ev Expectation and Deviation with Gaussian Process Models.
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[ bib 
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Several methods based on Kriging have recently been proposed
for calculating a probability of failure involving
costlytoevaluate 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 setup and provide a
consistency result. We then illustrate how spacefilling
versus adaptive design strategies may sequentially reduce
level set estimation uncertainty.

[404]

Yuning Chen, JinKao Hao, and Fred Glover.
A hybrid metaheuristic approach for the capacitated arc routing
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European Journal of Operational Research, 553(1):25–39, 2016.
[ bib 
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Keywords: irace

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RueyMaw Chen and FuRen Hsieh.
An exchange local search heuristic based scheme for permutation
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Evolutionary algorithms are widely used for solving
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In many realworld optimization problems, like the traffic
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robust (low variance) across all different scenarios.
Previous work has revealed the effectiveness of IRACE for
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parameters, that have various data types (categorical,
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search. Therefore, in this work, we propose a hybridization
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Swagatam Das and Ponnuthurai N. Suganthan.
Differential Evolution: A Survey of the Stateoftheart.
IEEE Transactions on Evolutionary Computation, 15(1), February
2011.
[ bib ]

[500]

Sanjeeb Dash.
Exponential Lower Bounds on the Lengths of Some Classes of
BranchandCut Proofs.
Mathematics of Operations Research, 30(3):678–700, 2005.
[ bib ]

[501]

Jean Daunizeau, Hanneke E. M. den Ouden, Matthias Pessiglione, Stefan J.
Kiebel, Karl J. Friston, and Klaas E. Stephan.
Observing the observer (II): deciding when to decide.
PLoS One, 5(12):e15555, 2010.
[ bib 
DOI ]

[502]

Jean Daunizeau, Hanneke E. M. den Ouden, Matthias Pessiglione, Klaas E.
Stephan, Stefan J. Kiebel, and Karl J. Friston.
Observing the observer (I): metaBayesian models of learning
and decisionmaking.
PLoS One, 5(12):e15554, 2010.
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DOI ]

[503]

Werner de Schaetzen, Dragan A. Savic, and Godfrey A. Walters.
A genetic algorithm approach to pump scheduling in water
supply.
In V. Babovic and L. C. Larsen, editors, Hydroinformatics '98,
pages 897–899, Rotterdam, Balkema, 1998.
[ bib ]

[504]

Thomas Dean and Mark S. Boddy.
An Analysis of TimeDependent Planning.
In H. E. Shrobe, T. M. Mitchell, and R. G. Smith, editors,
Proceedings of the 7th National Conference on Artificial Intelligence,
AAAI88, pages 49–54. AAAI Press/MIT Press, Menlo Park, CA, 1988.
[ bib 
http ]
Keywords: anytime, performance profiles

[505]

Angela Dean and Daniel Voss.
Design and Analysis of Experiments.
Springer, London, UK, 1999.
[ bib 
DOI ]

[506]

Kalyanmoy Deb.
An efficient constraint handling method for genetic algorithms.
Computer Methods in Applied Mechanics and Engineering,
186(2/4):311–338, 2000.
[ bib 
DOI ]

[507]

Kalyanmoy Deb, A. Pratap, S. Agarwal, and T. Meyarivan.
A fast and elitist multiobjective genetic algorithm:
NSGAII.
IEEE Transactions on Evolutionary Computation, 6(2):182–197,
2002.
[ bib 
DOI ]

[508]

Kalyanmoy Deb.
Multiobjective genetic algorithms: problem difficulties and
construction of test problems.
Evolutionary Computation, 7(3):205–230, 1999.
[ bib ]
Naive definition of PLOset

[509]

Kalyanmoy Deb.
Introduction to evolutionary multiobjective optimization.
In J. Branke, K. Deb, K. Miettinen, and R. Slowiński, editors,
Multiobjective Optimization: Interactive and Evolutionary Approaches,
volume 5252 of Lecture Notes in Computer Science, pages 59–96.
Springer, Heidelberg, Germany, 2008.
[ bib 
DOI ]
In its current state, evolutionary multiobjective
optimization (EMO) is an established field of research and
application with more than 150 PhD theses, more than ten
dedicated texts and edited books, commercial softwares and
numerous freely downloadable codes, a biannual conference
series running successfully since 2001, special sessions and
workshops held at all major evolutionary computing
conferences, and fulltime researchers from universities and
industries from all around the globe. In this chapter, we
provide a brief introduction to EMO principles, illustrate
some EMO algorithms with simulated results, and outline the
current research and application potential of EMO. For
solving multiobjective optimization problems, EMO procedures
attempt to find a set of welldistributed Paretooptimal
points, so that an idea of the extent and shape of the
Paretooptimal front can be obtained. Although this task was
the early motivation of EMO research, EMO principles are now
being found to be useful in various other problem solving
tasks, enabling one to treat problems naturally as they
are. One of the major current research thrusts is to combine
EMO procedures with other multiple criterion decision making
(MCDM) tools so as to develop hybrid and interactive
multiobjective optimization algorithms for finding a set of
tradeoff optimal solutions and then choose a preferred
solution for implementation. This chapter provides the
background of EMO principles and their potential to launch
such collaborative studies with MCDM researchers in the
coming years.

[510]

Kalyanmoy Deb.
Multiobjective optimization.
In E. K. Burke and G. Kendall, editors, Search Methodologies,
pages 403–449. Springer, Boston, MA, 2005.
[ bib 
DOI ]

[511]

Kalyanmoy Deb.
MultiObjective Optimization Using Evolutionary Algorithms.
Wiley, Chichester, UK, 2001.
[ bib ]

[512]

Kalyanmoy Deb and S. Agrawal.
A NichedPenalty Approach for Constraint Handling in Genetic
Algorithms.
In A. Dobnikar, N. C. Steele, D. W. Pearson, and R. F. Albrecht,
editors, Artificial Neural Nets and Genetic Algorithms (ICANNGA99),
pages 235–243. Springer Verlag, 1999.
[ bib 
DOI ]
Keywords: polynomial mutation

[513]

Kalyanmoy Deb and Ram Bhushan Agrawal.
Simulated binary crossover for continuous search spaces.
Complex Systems, 9(2):115–148, 1995.
[ bib 
http ]
Keywords: SBX

[514]

Kalyanmoy Deb, S. Agarwal, A. Pratap, and T. Meyarivan.
A fast elitist nondominated sorting genetic algorithm for
multiobjective optimization: NSGAII.
In M. Schoenauer et al., editors, Proceedings of PPSNVI, Sixth
International Conference on Parallel Problem Solving from Nature, volume
1917 of Lecture Notes in Computer Science, pages 849–858. Springer,
Heidelberg, Germany, 2000.
[ bib ]

[515]

Kalyanmoy Deb and Debayan Deb.
Analysing mutation schemes for realparameter genetic
algorithms.
International Journal of Artificial Intelligence and Soft
Computing, 4(1):1–28, 2014.
[ bib ]

[516]

Kalyanmoy Deb and Sachin Jain.
MultiSpeed Gearbox Design Using MultiObjective Evolutionary
Algorithms.
Technical Report 2002001, KanGAL, February 2002.
[ bib ]

[517]

Kalyanmoy Deb and Sachin Jain.
An Evolutionary ManyObjective Optimization Algorithm Using
ReferencePointBased Nondominated Sorting Approach, Part I: Solving
Problems With Box Constraints.
IEEE Transactions on Evolutionary Computation, 18(4):577–601,
2014.
[ bib ]
Proposed NSGAIII

[518]

Kalyanmoy Deb and Murat Köksalan.
Guest Editorial: Special Issue on Preferencebased
Multiobjective Evolutionary Algorithms.
IEEE Transactions on Evolutionary Computation, 14(5):669–670,
October 2010.
[ bib 
DOI ]

[519]

Kalyanmoy Deb and Christie Myburgh.
Breaking the billionvariable barrier in realworld optimization
using a customized evolutionary algorithm.
In T. Friedrich, F. Neumann, and A. M. Sutton, editors,
Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2015, pages 653–660. ACM Press, New York, NY, 2016.
[ bib ]

[520]

Kalyanmoy Deb and Ankur Sinha.
Solving Bilevel MultiObjective Optimization Problems Using
Evolutionary Algorithms.
In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.K. Hao, and
M. Sevaux, editors, Evolutionary Multicriterion Optimization, EMO
2009, volume 5467 of Lecture Notes in Computer Science, pages
110–124. Springer, Heidelberg, Germany, 2009.
[ bib ]

[521]

Kalyanmoy Deb and J. Sundar.
Reference point based multiobjective optimization using
evolutionary algorithms.
In M. Cattolico et al., editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2006, pages 635–642. ACM Press,
New York, NY, 2006.
[ bib 
DOI ]

[522]

Kalyanmoy Deb, Rahul Tewari, Mayur Dixit, and Joydeep Dutta.
Finding tradeoff solutions close to KKT points using
evolutionary multiobjective optimization.
In Proceedings of the 2007 Congress on Evolutionary Computation
(CEC 2007), pages 2109–2116. IEEE Press, Piscataway, NJ, 2007.
[ bib ]

[523]

Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler.
Scalable Test Problems for Evolutionary MultiObjective
Optimization.
Technical Report 112, Computer Engineering and Networks Laboratory
(TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland,
2001.
Do not cite this TR! It is incorrect and it is superseeded
by [524].
[ bib ]
Keywords: DTLZ benchmark

[524]

Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler.
Scalable Test Problems for Evolutionary Multiobjective
Optimization.
In A. Abraham, L. Jain, and R. Goldberg, editors, Evolutionary
Multiobjective Optimization, Advanced Information and Knowledge Processing,
pages 105–145. Springer, London, UK, January 2005.
[ bib ]
Keywords: DTLZ benchmark

[525]

Kalyanmoy Deb, Ling Zhu, and Sandeep Kulkarni.
Handling Multiple Scenarios in Evolutionary MultiObjective
Numerical Optimization.
IEEE Transactions on Evolutionary Computation, 22(6):920–933,
2018.
[ bib 
DOI ]
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, multiscenario consideration has
received a lukewarm attention, particularly in the context of
multiobjective optimization. The usual practice is to
optimize for the worstcase scenario. In this paper, we
review existing methodologies in this direction and set our
goal to suggest a new and potential populationbased method
for handling multiple scenarios by defining scenariowise
domination principle and scenariowise diversitypreserving
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 multiscenario, multiobjective,
optimization study on numerical problems indicates that
multiple scenarios can be handled in an integrated manner
using an EMO framework to find a wellbalanced 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 tradeoff solutions
simultaneously. An achievement of multiobjective tradeoff
and multiscenario tradeoff is algorithmically challenging,
but due to its practical appeal, further research and
application must be spent.
Keywords: scenariobased

[526]

Annelies De Corte and Kenneth Sörensen.
Optimisation of gravityfed water distribution network design: A
critical review.
European Journal of Operational Research, 228(1):1–10, 2013.
[ bib 
DOI ]

[527]

Annelies De Corte and Kenneth Sörensen.
An Iterated Local Search Algorithm for Water Distribution
Network Design Optimization.
Networks, 67(3):187–198, 2016.
[ bib ]

[528]

Annelies De Corte and Kenneth Sörensen.
An Iterated Local Search Algorithm for multiperiod water
distribution network design optimization.
Water, 8(8):359, 2016.
[ bib 
DOI ]

[529]

William A. Dees, Jr. and Patrick G. Karger.
Automated Ripup and Reroute Techniques.
In DAC'82, Proceedings of the 19th Design Automation Workshop,
pages 432–439. IEEE Press, 1982.
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[530]

V. Dekhtyarenko.
Verification of weight coefficients in multicriteria
optimization problems.
ComputerAided Design, 13(6):339–344, 1981.
[ bib ]

[531]

X. Delorme, Xavier Gandibleux, and F. Degoutin.
Evolutionary, constructive and hybrid procedures for the
biobjective set packing problem.
European Journal of Operational Research, 204(2):206–217,
2010.
[ bib ]
This paper cannot be found on internet!! Does it exist?

[532]

Federico Della Croce, Thierry Garaix, and Andrea Grosso.
Iterated Local Search and Very Large Neighborhoods for the
Parallelmachines Total Tardiness Problem.
Computers & Operations Research, 39(6):1213–1217, 2012.
[ bib ]

[533]

Maxence Delorme, Manuel Iori, and Silvano Martello.
Bin packing and cutting stock problems: Mathematical models and
exact algorithms.
European Journal of Operational Research, 255(1):1–20, 2016.
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DOI ]

[534]

Mauro Dell'Amico, Manuel Iori, Silvano Martello, and Michele Monaci.
Heuristic and Exact Algorithms for the Identical Parallel
Machine Scheduling Problem.
INFORMS Journal on Computing, 20(3):333–344, 2016.
[ bib ]

[535]

Maxence Delorme, Manuel Iori, and Silvano Martello.
BPPLIB: a library for bin packing and cutting stock problems.
Optimization Letters, 12(2):235–250, 2018.
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DOI ]

[536]

Mauro Dell'Amico, Manuel Iori, Stefano Novellani, and Thomas Stützle.
A destroy and repair algorithm for the Bike sharing Rebalancing
Problem.
Computers & Operations Research, 71:146–162, 2016.
[ bib 
DOI ]
Keywords: irace

[537]

Robert F. Dell and Mark H. Karwan.
An interactive MCDM weight space reduction method utilizing a
Tchebycheff utility function.
Naval Research Logistics, 37(2):263–277, 1990.
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[538]

Mauro Dell'Amico and Marco Trubian.
Applying Tabu Search to the Job Shop Scheduling Problem.
Annals of Operations Research, 41:231–252, 1993.
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[539]

Stephan Dempe, Gabriele Eichfelder, and Jörg Fliege.
On the effects of combining objectives in multiobjective
optimization.
Mathematical Methods of Operations Research, 82(1):1–18, 2015.
[ bib ]

[540]

JeanLouis Deneubourg, S. Aron, S. Goss, and J.M. Pasteels.
The SelfOrganizing Exploratory Pattern of the Argentine Ant.
Journal of Insect Behavior, 3(2):159–168, 1990.
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[541]

Matthijs L. den Besten.
Simple Metaheuristics for Scheduling.
PhD thesis, FB Informatik, TU Darmstadt, Germany, 2004.
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[542]

Roman Denysiuk, Lino Costa, and Isabel Espírito Santo.
Manyobjective optimization using differential evolution with
variablewise mutation restriction.
In C. Blum and E. Alba, editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2013, pages 591–598. ACM Press,
New York, NY, 2013.
[ bib ]

[543]

Jia Deng, Wei Dong, Richard Socher, LiJia Li, Kai Li, and Li FeiFei.
Imagenet: A largescale hierarchical image database.
In Computer Vision and Pattern Recognition, 2009. CVPR 2009.
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[544]

Joaquín Derrac, Salvador García, Daniel Molina, and Francisco
Herrera.
A practical tutorial on the use of nonparametric statistical
tests as a methodology for comparing evolutionary and swarm intelligence
algorithms.
Swarm and Evolutionary Computation, 1(1):3–18, 2011.
[ bib ]

[545]

Ulrich Derigs and Ulrich Vogel.
Experience with a Framework for Developing Heuristics for
Solving Rich Vehicle Routing Problems.
Journal of Heuristics, 20(1):75–106, 2014.
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[546]

Marcelo De Souza and Marcus Ritt.
An Automatically Designed Recombination Heuristic for the
TestAssignment Problem.
In Proceedings of the 2018 Congress on Evolutionary Computation
(CEC 2018), pages 1–8, Piscataway, NJ, 2018. IEEE Press.
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[547]

Marcelo De Souza and Marcus Ritt.
Automatic GrammarBased Design of Heuristic Algorithms for
Unconstrained Binary Quadratic Programming.
In A. Liefooghe and M. LópezIbáñez, editors,
Proceedings of EvoCOP 2018 – 18th European Conference on Evolutionary
Computation in Combinatorial Optimization, volume 10782 of Lecture
Notes in Computer Science, pages 67–84. Springer, Heidelberg, Germany,
2018.
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[548]

Marcelo De Souza and Marcus Ritt.
Hybrid Heuristic for Unconstrained Binary Quadratic Programming
– Source Code of HHBQP.
https://github.com/souzamarcelo/hhbqp, 2018.
[ bib ]

[549]

Marcelo De Souza, Marcus Ritt, Manuel LópezIbáñez, and Leslie
Pérez Cáceres.
ACVIZ: A Tool for the Visual Analysis of the
Configuration of Algorithms with irace – Source Code.
https://github.com/souzamarcelo/acviz, 2020.
[ bib ]

[550]

Marcelo De Souza, Marcus Ritt, Manuel LópezIbáñez, and Leslie
Pérez Cáceres.
ACVIZ: Algorithm Configuration
Visualizations for irace: Supplementary material.
http://doi.org/10.5281/zenodo.4714582, September 2020.
[ bib ]

[551]

Marcelo De Souza, Marcus Ritt, Manuel LópezIbáñez, and Leslie
Pérez Cáceres.
ACVIZ: A Tool for the Visual Analysis of the
Configuration of Algorithms with irace.
Operations Research Perspectives, 8:100186, 2021.
[ bib 
DOI 
supplementary material ]
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.

[552]

Paolo Detti, Francesco Papalini, and Garazi Zabalo Manrique de Lara.
A multidepot dialaride problem with heterogeneous vehicles
and compatibility constraints in healthcare.
Omega, 70:1–14, 2017.
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[553]

Sven De Vries and Rakesh V. Vohra.
Combinatorial Auctions: A Survey.
INFORMS Journal on Computing, 15(3):284–309, 2003.
[ bib ]

[554]

Sophie Dewez.
On the toll setting problem.
PhD thesis, Faculté de Sciences, Université Libre de
Bruxelles, 2014.
[ bib ]
Supervised by Dr. Martine Labbé

[555]

Juan Esteban Diaz, Julia Handl, and DongLing Xu.
Evolutionary robust optimization in production planning:
interactions between number of objectives, sample size and choice of
robustness measure.
Computers & Operations Research, 79:266–278, 2017.
[ bib 
DOI ]
Keywords: Evolutionary multiobjective optimization, Production
planning, Robust optimization, Simulationbased optimization,
Uncertainty modelling

[556]

Juan Esteban Diaz, Julia Handl, and DongLing Xu.
Integrating metaheuristics, simulation and exact techniques for
production planning of a failureprone manufacturing system.
European Journal of Operational Research, 266(3):976–989,
2018.
[ bib 
DOI ]
Keywords: Genetic algorithms, Combinatorial optimization, Production
planning, Simulationbased optimization, Uncertainty
modelling

[557]

Juan Esteban Diaz and Manuel LópezIbáñez.
Incorporating DecisionMaker's Preferences into the Automatic
Configuration of BiObjective Optimisation Algorithms.
European Journal of Operational Research, 289(3):1209–1222,
2021.
[ bib 
DOI 
supplementary material ]
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 Paretooptimality 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
multiobjective optimisers according to the preferences of
the DM. We evaluate the proposed approach on a wellknown
benchmark problem. Finally, we apply our approach to
reconfiguring, according to different DM's preferences, a
multiobjective optimiser tackling a realworld production
planning problem arising in the manufacturing industry.

[558]

L. C. Dias, Vincent Mousseau, José Rui Figueira, and J. N. Clímaco.
An aggregation/disaggregation approach to obtain robust
conclusions with ELECTRE TRI.
European Journal of Operational Research, 138(2):332–348,
April 2002.
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[559]

Gianni A. Di Caro and Marco Dorigo.
AntNet: Distributed Stigmergetic Control for Communications
Networks.
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[560]

Gianni A. Di Caro, F. Ducatelle, and L. M. Gambardella.
AntHocNet: An adaptive natureinspired algorithm for routing
in mobile ad hoc networks.
European Transactions on Telecommunications, 16(5):443–455,
2005.
[ bib ]

[561]

Diego Díaz, Pablo Valledor, Paula Areces, Jorge Rodil, and Montserrat
Suárez.
An ACO Algorithm to Solve an Extended Cutting Stock Problem
for Scrap Minimization in a Bar Mill.
In M. Dorigo et al., editors, Swarm Intelligence, 9th
International Conference, ANTS 2014, volume 8667 of Lecture Notes in
Computer Science, pages 13–24. Springer, Heidelberg, Germany, 2014.
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[562]

Luca Di Gaspero, Marco Chiarandini, and Andrea Schaerf.
A Study on the ShortTerm Prohibition Mechanisms in Tabu
Search.
In G. Brewka, S. Coradeschi, A. Perini, and P. Traverso, editors,
Proceedings of the 17th European Conference on Artificial Intelligence,
ECAI 2006, Riva del Garda, Italy, August29  September 1, 2006, pages
83–87. IOS Press, 2006.
[ bib ]

[563]

Luca Di Gaspero, Andrea Rendl, and Tommaso Urli.
ConstraintBased Approaches for Balancing Bike Sharing Systems.
In C. Schulte, editor, Principles and Practice of Constraint
Programming, volume 8124 of Lecture Notes in Computer Science, pages
758–773. Springer, Heidelberg, Germany, 2013.
[ bib 
DOI ]
Keywords: Frace

[564]

Luca Di Gaspero, Andrea Rendl, and Tommaso Urli.
A Hybrid ACO+CP for Balancing Bicycle Sharing Systems.
In M. J. Blesa, C. Blum, P. Festa, A. Roli, and M. Sampels, editors,
Hybrid Metaheuristics, volume 7919 of Lecture Notes in Computer
Science, pages 198–212. Springer, Heidelberg, Germany, 2013.
[ bib 
DOI ]
Keywords: Frace

[565]

Luca Di Gaspero and Andrea Schaerf.
EasyLocal++: An objectoriented framework for flexible
design of local search algorithms.
Software — Practice & Experience, 33(8):733–765, July 2003.
[ bib 
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Keywords: software engineering, local search, easylocal

[566]

J.Y. Ding, S. Song, J. N. D. Gupta, R. Zhang, R. Chiong, and C. Wu.
An Improved Iterated Greedy Algorithm with a Tabubased
Reconstruction Strategy for the Nowait Flowshop Scheduling Problem.
Applied Soft Computing, 30:604–613, 2015.
[ bib ]

[567]

Benjamin Doerr, Carola Doerr, and Franziska Ebel.
From blackbox complexity to designing new genetic algorithms.
Theoretical Computer Science, 567:87–104, 2015.
[ bib 
DOI ]

[568]

Karl F. Doerner, Guenther Fuellerer, Manfred Gronalt, Richard F. Hartl, and
Manuel Iori.
Metaheuristics for the Vehicle Routing Problem with Loading
Constraints.
Networks, 49(4):294–307, 2006.
[ bib ]

[569]

Benjamin Doerr, Christian Gießen, Carsten Witt, and Jing Yang.
The (1+λ) evolutionary algorithm with selfadjusting
mutation rate.
Algorithmica, 81(2):593–631, 2019.
[ bib ]

[570]

Karl F. Doerner, Walter J. Gutjahr, Richard F. Hartl, Christine Strauss, and
Christian Stummer.
NatureInspired Metaheuristics in Multiobjective Activity
Crashing.
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[ bib ]

[571]

Karl F. Doerner, Walter J. Gutjahr, Richard F. Hartl, Christine Strauss, and
Christian Stummer.
Pareto Ant Colony Optimization: A Metaheuristic Approach to
Multiobjective Portfolio Selection.
Annals of Operations Research, 131:79–99, 2004.
[ bib ]

[572]

Karl F. Doerner, Walter J. Gutjahr, Richard F. Hartl, Christine Strauss, and
Christian Stummer.
Pareto ant colony optimization with ILP preprocessing in
multiobjective project portfolio selection.
European Journal of Operational Research, 171:830–841, 2006.
[ bib ]

[573]

Karl F. Doerner, Richard F. Hartl, and Marc Reimann.
Are COMPETants more competent for problem solving? The case
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Central European Journal for Operations Research and Economics,
11(2):115–141, 2003.
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Karl F. Doerner, D. Merkle, and Thomas Stützle.
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Benjamin Doerr, Frank Neumann, Dirk Sudholt, and Carsten Witt.
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Dogan Aydin.
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Several probabilistic models for subset choice have been
proposed in the literature, for example, to explain approval
voting data. We show that Marley et al.'s latent scale model
is subsumed by Falmagne and Regenwetter's sizeindependent
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 φmodel as a special case. We
explore its basic properties as well as its relationship to
Fligner and Verducci's multistage ranking model.

[578]

Elizabeth D. Dolan and Jorge J. Moré.
Benchmarking optimization software with performance profiles.
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Pedro Domingos and Geoff Hulten.
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Xingye Dong, Ping, Houkuan Huang, and Maciek Nowak.
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Alberto Franzin and Thomas Stützle.
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Alberto Franzin and Thomas Stützle.
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Alberto Franzin and Thomas Stützle.
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Alberto Franzin and Thomas Stützle.
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A core feature of evolutionary algorithms is their mutation
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(1+1) Evolutionary Algorithm (EA). Our analyses show that
this mutation operator competes with preexisting ones, when
used by the (1+1)EA on classes of problems for which
results on the other mutation operators are available. We
present a “jump” function for which the performance of the
(1+1)EA using any static uniform mutation and any restart
strategy can be worse than the performance of the (1+1)EA
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combinatorial local search algorithms specifically designed
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Matteo Frigo and Steven G. Johnson.
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Satisfiability testing (SAT) is a very active area
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The development of successful metaheuristic
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problem such as satisfiability testing (SAT) is a
challenging task. We investigate an evolutionary
approach to automating the discovery of new local
search heuristics for SAT. We show that several
wellknown SAT local search algorithms such as
Walksat and Novelty are composite heuristics that
are derived from novel combinations of a set of
building blocks. Based on this observation, we
developed CLASS, a genetic programming system that
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discover SAT local search heuristics. New
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competitive with the best Walksat variants,
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Algorithm selection is typically based on models of
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prohibitively expensive. In recent work, we adopted
an online approach, in which models of the runtime
distributions of the available algorithms are
iteratively updated and used to guide the allocation
of computational resources, while solving a sequence
of problem instances. The models are estimated using
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Kaizhou Gao, Yicheng Zhang, Ali Sadollah, and Rong Su.
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Huiru Gao, Haifeng Nie, and Ke Li.
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In this article, I will consider Markov Decision Processes
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infinite horizon cumulative return. The second criterion is
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We give some dos and don'ts for those analysing algorithms
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After growing up together, and mostly growing apart in the
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vs. artificially created, populationbased vs. local search,
etc. A set of problems is generated to compare the solution
quality and computational efforts of these heuristics.
Results of the experimentation show that tabu search and ant
colony perform better for large problems whereas simulated
annealing performs best in smallsize problems. Some
conclusions are also drawn on the interactions between
various problem parameters and the performance of the
heuristics.
Keywords: ACO,Customer order scheduling,Genetic
algorithms,Metaheuristics,Simulated annealing,Tabu
search

[940]

Verena HeidrichMeisner and Christian Igel.
Hoeffding and Bernstein races for selecting policies in
evolutionary direct policy search.
In A. P. Danyluk, L. Bottou, and M. L. Littman, editors,
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Keld Helsgaun.
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Keld Helsgaun.
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Keld Helsgaun.
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Pascal van Hentenryck.
The OPL optimization programming language.
MIT Press, Cambridge, MA, 1999.
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Darrall Henderson, Sheldon H. Jacobson, and Alan W. Johnson.
The Theory and Practice of Simulated Annealing.
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Pascal van Hentenryck and Laurent D. Michel.
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Pascal van Hentenryck and Laurent D. Michel.
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H. Hernández and Christian Blum.
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Francisco Herrera, Manuel Lozano, and Daniel Molina.
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Francisco Herrera, Manuel Lozano, and A. M. Sánchez.
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algorithms used to solve these instances are
stresstested. The technique is applied in three important
domains of combinatorial optimisation, binary constraint
satisfaction, Boolean satisfiability, and the travelling
salesman problem. The problem instances acquired through this
technique are more difficult than the ones found in popular
benchmarks. In this paper, these evolved instances are
analysed with the aim to explain their difficulty in terms of
structural properties, thereby exposing the weaknesses of
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Robert Heumüller, Sebastian Nielebock, Jacob Krüger, and Frank
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Daniel P Heyman and Matthew J Sobel.
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John N. Hooker.
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Holger H. Hoos and Thomas Stützle.
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Holger H. Hoos and Thomas Stützle.
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Holger H. Hoos and Thomas Stützle.
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Holger H. Hoos and Thomas Stützle.
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Holger H. Hoos.
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Holger H. Hoos.
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Holger H. Hoos.
Programming by optimization.
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Christian Horoba and Frank Neumann.
Benefits and drawbacks for the use of epsilondominance in
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Wenbin Hu, Liping Yan, Huan Wang, Bo Du, and Dacheng Tao.
Realtime traffic jams prediction inspired by Biham,
Middleton and Levine (BML) model.
Information Sciences, 2017.
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Keywords: BML model,Prediction,Realtime,Traffic jam,Urban traffic
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Deng Huang, Theodore T. Allen, William I. Notz, and Ning Zeng.
Global Optimization of Stochastic BlackBox Systems via
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Han Huang, Xiaowei Yang, Zhifeng Hao, and Ruichu Cai.
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Evan J. Hughes.
Multiple single objective Pareto sampling.
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Evan J. Hughes.
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Evan J. Hughes.
Manyobjective directed evolutionary line search.
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[992]

Jérémie Humeau, Arnaud Liefooghe, ElGhazali Talbi, and Sébastien
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ParadisEOMO: From Fitness Landscape Analysis to Efficient
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Design and Analysis of Computer Experiments With Branching and
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[994]

Maura Hunt and Manuel LópezIbáñez.
Modeling a DecisionMaker in Goal Programming by means of
Computational Rationality.
In I. Palomares, editor, International Alan Turing Conference on
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This paper extends a simulation of cognitive mechanisms in
the context of multicriteria decisionmaking by using ideas
from computational rationality. Specifically, this paper
improves the simulation of a human decisionmaker (DM) by
considering how resource constraints impact their evaluation
process in an interactive Goal Programming problem. Our
analysis confirms and emphasizes a previous simulation study
by showing key areas that could be effected by cognitive
mechanisms. While the results are promising, the effects
should be validated by future experiments with human DMs.

[995]

M. Hurtgen and J.C. Maun.
Optimal PMU placement using Iterated Local Search.
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Mohamed Saifullah Hussin and Thomas Stützle.
Hierarchical Iterated Local Search for the Quadratic Assignment
Problem.
In M. J. Blesa, C. Blum, L. Di Gaspero, A. Roli, M. Sampels, and
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[998]

Mohamed Saifullah Hussin and Thomas Stützle.
Tabu Search vs. Simulated Annealing for Solving Large Quadratic
Assignment Instances.
Computers & Operations Research, 43:286–291, 2014.
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[999]

Frank Hutter, Domagoj Babić, Holger H. Hoos, and Alan J. Hu.
Boosting Verification by Automatic Tuning of Decision
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In J. Baumgartner and M. Sheeran, editors, FMCAD'07: Proceedings
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Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Kevin P. Murphy.
An experimental investigation of modelbased parameter
optimisation: SPO and beyond.
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[1001]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Automated Configuration of Mixed Integer Programming Solvers.
In A. Lodi, M. Milano, and P. Toth, editors, Integration of AI
and OR Techniques in Constraint Programming for Combinatorial Optimization
Problems, 7th International Conference, CPAIOR 2010, volume 6140 of
Lecture Notes in Computer Science, pages 186–202. Springer, Heidelberg,
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[1002]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Tradeoffs in the Empirical Evaluation of Competing Algorithm
Designs.
Annals of Mathematics and Artificial Intelligence,
60(1–2):65–89, 2010.
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[1003]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Sequential ModelBased Optimization for General Algorithm
Configuration.
In C. A. Coello Coello, editor, Learning and Intelligent
Optimization, 5th International Conference, LION 5, volume 6683 of
Lecture Notes in Computer Science, pages 507–523. Springer, Heidelberg,
Germany, 2011.
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Keywords: SMAC,ROAR

[1004]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Parallel Algorithm Configuration.
In Y. Hamadi and M. Schoenauer, editors, Learning and
Intelligent Optimization, 6th International Conference, LION 6, volume 7219
of Lecture Notes in Computer Science, pages 55–70. Springer,
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[1005]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Bayesian Optimization With Censored Response Data.
Arxiv preprint arXiv:1310.1947, 2013.
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[1006]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Identifying key algorithm parameters and instance features using
forward selection.
In P. M. Pardalos and G. Nicosia, editors, Learning and
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Keywords: parameter importance

[1007]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
An Efficient Approach for Assessing Hyperparameter Importance.
In E. P. Xing and T. Jebara, editors, Proceedings of the 31st
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[1008]

Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Kevin Murphy.
TimeBounded Sequential Parameter Optimization.
In C. Blum and R. Battiti, editors, Learning and Intelligent
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[1009]

Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Thomas Stützle.
ParamILS: An Automatic Algorithm
Configuration Framework.
Journal of Artificial Intelligence Research, 36:267–306,
October 2009.
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[1010]

Frank Hutter, Holger H. Hoos, and Thomas Stützle.
Automatic Algorithm Configuration Based on Local Search.
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[1011]

Frank Hutter, Marius Thomas Lindauer, Adrian Balint, Sam Bayless, Holger H.
Hoos, and Kevin LeytonBrown.
The Configurable SAT Solver Challenge (CSSC).
Artificial Intelligence, 243(1–25), 2017.
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[1012]

Frank Hutter, Marius Thomas Lindauer, Adrian Balint, Sam Bayless, Holger H.
Hoos, and Kevin LeytonBrown.
The Configurable SAT Solver Challenge (CSSC).
Artificial Intelligence, 243:1–25, 2017.
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[1013]

Frank Hutter, Manuel LópezIbáñez, Chris Fawcett, Marius Thomas
Lindauer, Holger H. Hoos, Kevin LeytonBrown, and Thomas Stützle.
AClib: A Benchmark Library for Algorithm Configuration.
In P. M. Pardalos, M. G. C. Resende, C. Vogiatzis, and J. L.
Walteros, editors, Learning and Intelligent Optimization, 8th
International Conference, LION 8, volume 8426 of Lecture Notes in
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[1014]

Frank Hutter, Lin Xu, Holger H. Hoos, and Kevin LeytonBrown.
Algorithm runtime prediction: Methods & evaluation.
Artificial Intelligence, 206:79–111, 2014.
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[1015]

Frank Hutter.
SAT benchmarks used in automated algorithm configuration.
http://www.cs.ubc.ca/labs/beta/Projects/AAC/SATbenchmarks.html, 2007.
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[1016]

Frank Hutter.
Automated Configuration of Algorithms for Solving Hard
Computational Problems.
PhD thesis, University of British Columbia, Department of Computer
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[1017]

Jérémie Humeau, Arnaud Liefooghe, ElGhazali Talbi, and Sébastien
Verel.
ParadisEOMO: From Fitness Landscape Analysis to Efficient
Local Search Algorithms.
Rapport de recherche RR7871, INRIA, France, 2012.
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[1018]

Mauro Birattari.
The race Package for R: Racing Methods
for the Selection of the Best.
Technical Report TR/IRIDIA/2003037, IRIDIA, Université Libre de
Bruxelles, Belgium, 2003.
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[1019]

Mauro Birattari.
On the Estimation of the Expected Performance of a Metaheuristic
on a Class of Instances. How Many Instances, How Many Runs?
Technical Report TR/IRIDIA/2004001, IRIDIA, Université Libre de
Bruxelles, Belgium, 2004.
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[1020]

Krzysztof Socha and Marco Dorigo.
Ant Colony Optimization for MixedVariable Optimization
Problems.
Technical Report TR/IRIDIA/2007019, IRIDIA, Université Libre de
Bruxelles, Belgium, October 2007.
[ bib ]

[1021]

Manuel LópezIbáñez, Luís Paquete, and Thomas Stützle.
Exploratory Analysis of Stochastic Local Search Algorithms in
Biobjective Optimization.
Technical Report TR/IRIDIA/2009015, IRIDIA, Université Libre de
Bruxelles, Belgium, May 2009.
Published as a book chapter [1348].
[ bib ]

[1022]

Manuel LópezIbáñez and Thomas Stützle.
An Analysis of Algorithmic Components for Multiobjective Ant
Colony Optimization: A Case Study on the Biobjective TSP.
Technical Report TR/IRIDIA/2009019, IRIDIA, Université Libre de
Bruxelles, Belgium, June 2009.
Published in the proceedings of Evolution Artificielle,
2009 [1358].
[ bib ]

[1023]

Jérémie DuboisLacoste, Manuel LópezIbáñez, and Thomas
Stützle.
Effective Hybrid Stochastic Local Search Algorithms for
Biobjective Permutation Flowshop Scheduling.
Technical Report TR/IRIDIA/2009020, IRIDIA, Université Libre de
Bruxelles, Belgium, June 2009.
Published in the proceedings of Hybrid Metaheuristics
2009 [615].
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[1024]

Jérémie DuboisLacoste, Manuel LópezIbáñez, and Thomas
Stützle.
Adaptive “Anytime” TwoPhase Local Search.
Technical Report TR/IRIDIA/2009026, IRIDIA, Université Libre de
Bruxelles, Belgium, 2010.
Published in the proceedings of LION 4 [618].
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[1025]

Thomas Stützle, Manuel LópezIbáñez, Paola Pellegrini, Michael
Maur, Marco A. Montes de Oca, Mauro Birattari, and Marco Dorigo.
Parameter Adaptation in Ant Colony Optimization.
Technical Report TR/IRIDIA/2010002, IRIDIA, Université Libre de
Bruxelles, Belgium, January 2010.
Published as a book chapter [1979].
[ bib ]

[1026]

Jérémie DuboisLacoste, Manuel LópezIbáñez, and Thomas
Stützle.
A Hybrid TP+PLS Algorithm for Biobjective FlowShop
Scheduling Problems.
Technical Report TR/IRIDIA/2010019, IRIDIA, Université Libre de
Bruxelles, Belgium, 2010.
Published in Computers & Operations
Research [621].
[ bib 
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[1027]

M. S. Hussin and Thomas Stützle.
Tabu Search vs. Simulated Annealing for Solving Large Quadratic
Assignment Instances.
Technical Report TR/IRIDIA/2010020, IRIDIA, Université Libre de
Bruxelles, Belgium, 2010.
[ bib ]

[1028]

Jérémie DuboisLacoste, Manuel LópezIbáñez, and Thomas
Stützle.
Improving the Anytime Behavior of TwoPhase Local Search.
Technical Report TR/IRIDIA/2010022, IRIDIA, Université Libre de
Bruxelles, Belgium, 2010.
Published in Annals of Mathematics and Artificial
Intelligence [620].
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[1029]

Manuel LópezIbáñez, Joshua D. Knowles, and Marco Laumanns.
On Sequential Online Archiving of Objective Vectors.
Technical Report TR/IRIDIA/2011001, IRIDIA, Université Libre de
Bruxelles, Belgium, 2011.
This is a revised version of the paper published in EMO
2011 [1340].
[ bib 
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[1030]

Mauro Birattari, Marco Chiarandini, Marco Saerens, and Thomas Stützle.
Learning graphical models for parameter tuning.
Technical Report TR/IRIDIA/2011002, IRIDIA, Université Libre de
Bruxelles, Belgium, 2011.
[ bib 
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[1031]

Manuel LópezIbáñez and Thomas Stützle.
The Automatic Design of MultiObjective Ant Colony Optimization
Algorithms.
Technical Report TR/IRIDIA/2011003, IRIDIA, Université Libre de
Bruxelles, Belgium, 2011.
Published in IEEE Transactions on Evolutionary
Computation [1365].
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[1032]

Tianjun Liao, Daniel Molina, Marco A. Montes de Oca, and Thomas Stützle.
A Note on the Effects of Enforcing Bound Constraints on
Algorithm Comparisons using the IEEE CEC'05 Benchmark Function Suite.
Technical Report TR/IRIDIA/2011010, IRIDIA, Université Libre de
Bruxelles, Belgium, 2011.
Published in Evolutionary Computation [1294].
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http ]

[1033]

Tianjun Liao, Daniel Molina, Marco A. Montes de Oca, and Thomas Stützle.
Computational Results for an Automatically Tuned IPOPCMAES
on the CEC'05 Benchmark Set.
Technical Report TR/IRIDIA/2011022, IRIDIA, Université Libre de
Bruxelles, Belgium, 2011.
[ bib ]

[1034]

Manuel LópezIbáñez and Thomas Stützle.
Automatically Improving the Anytime Behaviour of Optimisation
Algorithms.
Technical Report TR/IRIDIA/2012012, IRIDIA, Université Libre de
Bruxelles, Belgium, May 2012.
Published in European Journal of Operations
Research [1366].
[ bib ]

[1035]

Andreea Radulescu, Manuel LópezIbáñez, and Thomas Stützle.
Automatically Improving the Anytime Behaviour of Multiobjective
Evolutionary Algorithms.
Technical Report TR/IRIDIA/2012019, IRIDIA, Université Libre de
Bruxelles, Belgium, 2012.
Published in the proceedings of EMO 2013 [1742].
[ bib ]

[1036]

Tianjun Liao, Thomas Stützle, Marco A. Montes de Oca, and Marco Dorigo.
A Unified Ant Colony Optimization Algorithm for Continuous
Optimization.
Technical Report TR/IRIDIA/2013002, IRIDIA, Université Libre de
Bruxelles, Belgium, 2013.
[ bib ]

[1037]

Franco Mascia, Manuel LópezIbáñez, Jérémie DuboisLacoste,
and Thomas Stützle.
Grammarbased generation of stochastic local search heuristics
through automatic algorithm configuration tools.
Technical Report TR/IRIDIA/2013015, IRIDIA, Université Libre de
Bruxelles, Belgium, 2013.
[ bib ]

[1038]

Manuel LópezIbáñez, Arnaud Liefooghe, and Sébastien Verel.
Local Optimal Sets and Bounded Archiving on Multiobjective
NKLandscapes with Correlated Objectives.
Technical Report TR/IRIDIA/2014009, IRIDIA, Université Libre de
Bruxelles, Belgium, 2014.
[ bib ]

[1039]

Vito Trianni and Manuel LópezIbáñez.
Advantages of MultiObjective Optimisation in Evolutionary
Robotics: Survey and Case Studies.
Technical Report TR/IRIDIA/2014014, IRIDIA, Université Libre de
Bruxelles, Belgium, 2014.
[ bib 
http ]

[1040]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
A LargeScale Experimental Evaluation of HighPerforming Multi
and ManyObjective Evolutionary Algorithms.
Technical Report TR/IRIDIA/2017005, IRIDIA, Université Libre de
Bruxelles, Belgium, November 2017.
[ bib ]

[1041]

Alberto Franzin, Leslie Pérez Cáceres, and Thomas Stützle.
Effect of Transformations of Numerical Parameters in Automatic
Algorithm Configuration.
Technical Report TR/IRIDIA/2017006, IRIDIA, Université Libre de
Bruxelles, Belgium, March 2017.
[ bib 
http ]

[1042]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic Configuration of Multiobjective Optimizers and
Multiobjective Configuration.
Technical Report TR/IRIDIA/2017011, IRIDIA, Université Libre de
Bruxelles, Belgium, November 2017.
Published as [223].
[ bib 
http ]

[1043]

Manuel LópezIbáñez, MarieEléonore Kessaci, and Thomas
Stützle.
Automatic Design of Hybrid Metaheuristics from Algorithmic
Components.
Technical Report TR/IRIDIA/2017012, IRIDIA, Université Libre de
Bruxelles, Belgium, December 2017.
[ bib 
http ]

[1044]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatically Designing StateoftheArt Multi and
ManyObjective Evolutionary Algorithms.
Technical Report TR/IRIDIA/2018001, IRIDIA, Université Libre de
Bruxelles, Belgium, January 2018.
Published as [219].
[ bib 
http ]

[1045]

Alberto Franzin and Thomas Stützle.
Revisiting Simulated Annealing: a ComponentBased Analysis.
Technical Report TR/IRIDIA/2018010, IRIDIA, Université Libre de
Bruxelles, Belgium, 2018.
[ bib 
http ]

[1046]

Christian Leonardo CamachoVillalón, Thomas Stützle, and Marco Dorigo.
PSOX: A ComponentBased Framework for the Automatic Design of
Particle Swarm Optimization Algorithms.
Technical Report TR/IRIDIA/2021002, IRIDIA, Université Libre de
Bruxelles, Belgium, 2021.
[ bib 
http ]

[1047]

Alberto Franzin and Thomas Stützle.
A Landscapebased Analysis of Fixed Temperature and Simulated
Annealing.
Technical Report TR/IRIDIA/2021005, IRIDIA, Université Libre de
Bruxelles, Belgium, 2021.
[ bib 
http ]

[1048]

Christian Leonardo CamachoVillalón, Thomas Stützle, and Marco Dorigo.
Cuckoo Search ≡(μ+ λ)Evolution Strategy – A
Rigorous Analysis of an Algorithm That Has Been Misleading the Research
Community for More Than 10 Years and Nobody Seems to Have Noticed.
Technical Report TR/IRIDIA/2021006, IRIDIA, Université Libre de
Bruxelles, Belgium, 2021.
[ bib 
http ]

[1049]

Claudio Iacopino and Phil Palmer.
The Dynamics of Ant Colony Optimization Algorithms Applied to
Binary Chains.
Swarm Intelligence, 6(4):343–377, 2012.
[ bib ]

[1050]

Claudio Iacopino, Phil Palmer, N. Policella, A. Donati, and A. Brewer.
How Ants Can Manage Your Satellites.
Acta Futura, 9:59–72, 2014.
[ bib 
DOI ]
Keywords: ACO, Space

[1051]

Toshihide Ibaraki, Shinji Imahori, Koji Nonobe, Kensuke Sobue, Takeaki Uno, and
Mutsunori Yagiura.
An Iterated Local Search Algorithm for the Vehicle Routing
Problem with Convex Time Penalty Functions.
Discrete Applied Mathematics, 156(11):2050–2069, 2008.
[ bib ]

[1052]

Toshihide Ibaraki.
A Personal Perspective on Problem Solving by General Purpose
Solvers.
International Transactions in Operational Research,
17(3):303–315, 2010.
[ bib ]

[1053]

Jonas Ide and Anita Schöbel.
Robustness for uncertain multiobjective optimization: a survey
and analysis of different concepts.
OR Spectrum, 38(1):235–271, 2016.
[ bib 
DOI ]
In this paper, we discuss various concepts of robustness for
uncertain multiobjective optimization problems. We extend
the concepts of flimsily, highly, and lightly robust
efficiency and we collect different versions of minmax robust
efficiency and concepts based on set order relations from the
literature. Altogether, we compare and analyze ten different
concepts and point out their relations to each
other. Furthermore, we present reduction results for the
class of objectivewise uncertain multiobjective
optimization problems.

[1054]

Christian Igel, Nikolaus Hansen, and S. Roth.
Covariance Matrix Adaptation for Multiobjective Optimization.
Evolutionary Computation, 15(1):1–28, 2007.
[ bib ]

[1055]

Christian Igel, V. HeidrichMeisner, and T. Glasmachers.
Shark.
Journal of Machine Learning Research, 9:993–996, June 2008.
[ bib 
http ]

[1056]

Kokolo Ikeda, Hajime Kita, and Shigenobu Kobayashi.
Failure of Paretobased MOEAs: Does nondominated really
mean near to optimal?
In Proceedings of the 2001 Congress on Evolutionary Computation
(CEC'01), pages 957–962. IEEE Press, Piscataway, NJ, 2001.
[ bib ]

[1057]

Nesa Ilich and Slobodan P. Simonovic.
Evolutionary Algorithm for minimization of pumping cost.
Journal of Computing in Civil Engineering, ASCE,
12(4):232–240, October 1998.
[ bib ]
This paper deals with minimizing the total cost of
pumping in a liquid pipeline. Previous experience
with the most common solution procedures in pipeline
optimization is discussed along with their strengths
and weaknesses. The proposed method is an
evolutionary algorithm with two distinct features:
(1) The search is restricted to feasible region
only; and (2) it utilizes a floating point decision
variable rather than integer or binary as is the
case with most other similar approaches. A numerical
example is presented as a basis for verification of
the proposed method and its comparison with the
existing solver that utilizes the nonlinear
Newtonian search. The proposed method provides
promising improvements in terms of optimality when
compared to the widespread gradient search methods
because it does not involve evaluation of the
gradient of the objective function. It also provides
potential to improve the performance of previous
evolutionary programs because it restricts the
search to the feasible region, thus eliminating
large overhead associated with generation and
inspection of solutions that are
infeasible. Comparison of the two solutions revealed
improvement of the solution in favor of the proposed
algorithm, which ranged up to 6% depending on the
initial values of the decision variables in the
Newtonian search. The proposed method was not
sensitive to the starting value of the decision
variables.

[1058]

Janine Illian, Antti Penttinen, Helga Stoyan, and Dietrich Stoyan.
Statistical Analysis and Modelling of Spatial Point Patterns.
Wiley, 2008.
[ bib ]

[1059]

Takashi Imamichi, Mutsunori Yagiura, and Hiroshi Nagamochi.
An Iterated Local Search Algorithm Based on Nonlinear
Programming for the Irregular Strip Packing Problem.
Discrete Optimization, 6(4):345–361, 2009.
[ bib ]

[1060]

Alfred Inselberg.
The Plane with Parallel Coordinates.
The Visual Computer, 1(2):69–91, 1985.
[ bib ]

[1061]

John P. A. Ioannidis.
Why Most Published Research Findings Are False.
PLoS Medicine, 2(8):e124, 2005.
[ bib 
DOI ]

[1062]

S. Iredi, D. Merkle, and Martin Middendorf.
BiCriterion Optimization with Multi Colony Ant Algorithms.
In E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, and
D. Corne, editors, Evolutionary Multicriterion Optimization, EMO 2001,
volume 1993 of Lecture Notes in Computer Science, pages 359–372.
Springer, Heidelberg, Germany, 2001.
[ bib ]

[1063]

Manuel LópezIbáñez and Thomas Stützle.
Automatically Improving the Anytime Behaviour of Optimisation
Algorithms: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2012011/, 2012.
[ bib ]

[1064]

Stefan Irnich.
A Unified Modeling and Solution Framework for Vehicle Routing
and Local SearchBased Metaheuristics.
INFORMS Journal on Computing, 20(2):270–287, 2008.
[ bib ]

[1065]

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

[1066]

Ekhine Irurozki, Borja Calvo, and José A. Lozano.
PerMallows: An R Package for Mallows
and Generalized Mallows Models.
Journal of Statistical Software, 71, 2019.
[ bib 
DOI ]
In this paper we present the R package PerMallows, which is a
complete toolbox to work with permutations, distances and
some of the most popular probability models for permutations:
Mallows and the Generalized Mallows models. The Mallows model
is an exponential location model, considered as analogous to
the Gaussian distribution. It is based on the definition of a
distance between permutations. The Generalized Mallows model
is its bestknown extension. The package includes functions
for making inference, sampling and learning such
distributions. The distances considered in PerMallows are
Kendall's τ, Cayley, Hamming and Ulam.
Keywords: Cayley,Generalized Mallows,Hamming,Kendall's
τ,Learning,Mallows,Permutation,R,Ranking,Sampling,Ulam

[1067]

Ekhine Irurozki, Jesus Lobo, Aritz Perez, and Javier Del Ser.
Rank aggregation for nonstationary data streams.
Arxiv preprint arXiv:, 2020.
Submitted.
[ bib ]
Keywords: uborda

[1068]

Ekhine Irurozki and Manuel LópezIbáñez.
Unbalanced Mallows Models for Optimizing Expensive BlackBox
Permutation Problems.
In F. Chicano and K. Krawiec, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2021. ACM Press, New
York, NY, 2021.
[ bib 
DOI 
supplementary material ]
Expensive blackbox combinatorial optimization problems arise
in practice when the objective function is evaluated by means
of a simulator or a realworld experiment. Since each fitness
evaluation is expensive in terms of time or resources, only a
limited number of evaluations is possible, typically several
orders of magnitude smaller than in nonexpensive
problems. In this scenario, classical optimization methods
such as mixedinteger programming and local search are not
useful. In the continuous case, Bayesian optimization, in
particular using Gaussian processes, has proven very
effective under these conditions. Much less research is
available in the combinatorial case. In this paper, we
propose and analyze UMM, an estimationofdistribution (EDA)
algorithm based on a Mallows probabilistic model and
unbalanced rank aggregation (uBorda). Experimental results on
blackbox versions of LOP and PFSP show that UMM is able to
match, and sometimes surpass, the solutions obtained by CEGO,
a Bayesian optimization algorithm for combinatorial
optimization. Moreover, the computational complexity of UMM
increases linearly with both the number of function
evaluations and the permutation size.

[1069]

Hisao Ishibuchi and T. Murata.
A multiobjective genetic local search algorithm and its
application to flowshop scheduling.
IEEE Transactions on Systems, Man, and Cybernetics – Part C,
28(3):392–403, 1998.
[ bib ]

[1070]

Hisao Ishibuchi, N. Akedo, and Y. Nojima.
Behavior of Multiobjective Evolutionary Algorithms on
ManyObjective Knapsack Problems.
IEEE Transactions on Evolutionary Computation, 19(2):264–283,
2015.
[ bib 
DOI ]

[1071]

Hisao Ishibuchi, Hiroyuki Masuda, and Yusuke Nojima.
A Study on Performance Evaluation Ability of a Modified Inverted
Generational Distance Indicator.
In S. Silva and A. I. EsparciaAlcázar, editors,
Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2015, pages 695–702. ACM Press, New York, NY, 2015.
[ bib ]

[1072]

Hisao Ishibuchi, Hiroyuki Masuda, Yuki Tanigaki, and Yusuke Nojima.
Modified Distance Calculation in Generational Distance and
Inverted Generational Distance.
In A. GasparCunha, C. H. Antunes, and C. A. Coello Coello,
editors, Evolutionary Multicriterion Optimization, EMO 2015 Part I,
volume 9018 of Lecture Notes in Computer Science, pages 110–125.
Springer, Heidelberg, Germany, 2015.
[ bib ]
Proposed IGD+
Keywords: Performance metrics, multiobjective, IGD, IGD+

[1073]

Hisao Ishibuchi, Shinta Misaki, and Hideo Tanaka.
Modified simulated annealing algorithms for the flow shop
sequencing problem.
European Journal of Operational Research, 81(2):388–398, 1995.
[ bib ]

[1074]

Hisao Ishibuchi, N. Tsukamoto, and Y. Nojima.
Evolutionary manyobjective optimization: A short review.
In Proceedings of the 2008 Congress on Evolutionary Computation
(CEC 2008), pages 2419–2426, Piscataway, NJ, 2008. IEEE Press.
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[1075]

Srikanth K. Iyer and Barkha Saxena.
Improved genetic algorithm for the permutation flowshop
scheduling problem.
Computers & Operations Research, 31(4):593–606, 2004.
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[1076]

Christopher H. Jackson.
MultiState Models for Panel Data: The msm Package
for R.
Journal of Statistical Software, 38(8):1–29, 2011.
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[1077]

Richard H. F. Jackson, Paul T. Boggs, Stephen G. Nash, and Susan Powell.
Guidelines for Reporting Results of Computational Experiments.
Report of the Ad Hoc Committee.
Mathematical Programming, 49(3):413–425, 1991.
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Larry W. Jacobs and Michael J. Brusco.
A Local Search Heuristic for Large SetCovering Problems.
Naval Research Logistics, 42(7):1129–1140, 1995.
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Sophie Jacquin, Laetitia Jourdan, and ElGhazali Talbi.
Dynamic Programming Based Metaheuristic for Energy Planning
Problems.
In A. I. EsparciaAlcázar and A. M. Mora, editors,
Applications of Evolutionary Computation, volume 8602 of Lecture Notes
in Computer Science, pages 165–176. Springer, Heidelberg, Germany, 2014.
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Keywords: irace

[1080]

Daniel M Jaeggi, Geoffrey T Parks, Timoleon Kipouros, and P John Clarkson.
The development of a multiobjective Tabu Search algorithm for
continuous optimisation problems.
European Journal of Operational Research, 185(3):1192–1212,
2008.
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Antonio López Jaimes, Carlos A. Coello Coello, and Debrup Chakraborty.
Objective reduction using a feature selection technique.
In C. Ryan, editor, Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2008, pages 673–680. ACM Press, New York, NY,
2008.
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Satish Jajodia, Ioannis Minis, George Harhalakis, and JeanMarie Proth.
CLASS: computerized layout solutions using simulated
annealing.
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1992.
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Andrzej Jaszkiewicz.
Genetic local search for multiobjective combinatorial
optimization.
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Andrzej Jaszkiewicz.
ManyObjective Pareto Local Search.
European Journal of Operational Research, 271(3):1001–1013,
2018.
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Andrzej Jaszkiewicz and Jürgen Branke.
Interactive Multiobjective Evolutionary Algorithms.
In J. Branke, K. Deb, K. Miettinen, and R. Slowiński, editors,
Multiobjective Optimization: Interactive and Evolutionary Approaches,
volume 5252 of Lecture Notes in Computer Science, pages 179–193.
Springer, Heidelberg, Germany, 2008.
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Andrzej Jaszkiewicz, Hisao Ishibuchi, and Qingfu Zhang.
Multiobjective memetic algorithms.
In Handbook of Memetic Algorithms, volume 379 of Studies
in Computational Intelligence, pages 201–217. Springer, 2011.
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Andrzej Jaszkiewicz.
On the performance of multipleobjective genetic local search on
the 0/1 knapsack problem – A comparative experiment.
IEEE Transactions on Evolutionary Computation, 6(4):402–412,
2002.
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Frank Hutter and Steve Ramage.
Manual for SMAC.
University of British Columbia, 2015.
SMAC version 2.10.03.
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M. T. Jensen.
Reducing the runtime complexity of multiobjective EAs: The
NSGAII and other algorithms.
IEEE Transactions on Evolutionary Computation, 7(5):503–515,
2003.
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Mark Jerrum and Alistair Sinclair.
The Markov chain Monte Carlo method: an approach to
approximate counting and integration.
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Mark Jerrum and Gregory Sorkin.
The Metropolis algorithm for graph bisection.
Discrete Applied Mathematics, 82(1):155–175, 1998.
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Mark Jerrum.
Large cliques elude the Metropolis process.
Random Structures & Algorithms, 3(4):347–359, 1992.
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S. Jiang, Y. S. Ong, J. Zhang, and L. Feng.
Consistencies and Contradictions of Performance Metrics in
Multiobjective Optimization.
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[1094]

Yaochu Jin.
A Comprehensive Survey of Fitness Approximation in Evolutionary
Computation.
Soft Computing, 9(1):3–12, 2005.
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Yaochu Jin, Handing Wang, Tinkle Chugh, Dan Guo, and Kaisa Miettinen.
DataDriven Evolutionary Optimization: An Overview and Case
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IEEE Transactions on Evolutionary Computation, 23(3):442–458,
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Journal of Heuristics. Policies on Heuristic Search Research.
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Version visited last on June 10, 2015.
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David S. Johnson.
Optimal Two and Threestage Production Scheduling with Setup
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David S. Johnson, Cecilia R. Aragon, Lyle A. McGeoch, and Catherine Schevon.
Optimization by Simulated Annealing: An Experimental Evaluation:
Part I, Graph Partitioning.
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David S. Johnson, Cecilia R. Aragon, Lyle A. McGeoch, and Catherine Schevon.
Optimization by Simulated Annealing: An Experimental Evaluation:
Part II, Graph Coloring and Number Partitioning.
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David S. Johnson, G. Gutin, Lyle A. McGeoch, A. Yeo, W. Zhang, and
A. Zverovitch.
Experimental Analysis of Heuristics for the ATSP.
In G. Gutin and A. Punnen, editors, The Traveling Salesman
Problem and its Variations, pages 445–487. Kluwer Academic Publishers,
Dordrecht, The Netherlands, 2002.
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[1101]

Alan W. Johnson and Sheldon H. Jacobson.
On the Convergence of Generalized Hill Climbing Algorithms.
Discrete Applied Mathematics, 119(1):37–57, 2002.
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[1102]

David S. Johnson and Lyle A. McGeoch.
Experimental Analysis of Heuristics for the STSP.
In G. Gutin and A. Punnen, editors, The Traveling Salesman
Problem and its Variations, pages 369–443. Kluwer Academic Publishers,
Dordrecht, The Netherlands, 2002.
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[1103]

David S. Johnson and Lyle A. McGeoch.
The Traveling Salesman Problem: A Case Study in Local
Optimization.
In E. H. L. Aarts and J. K. Lenstra, editors, Local Search in
Combinatorial Optimization, pages 215–310. John Wiley & Sons, Chichester,
UK, 1997.
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[1104]

Mark E. Johnson, Leslie M. Moore, and Donald Ylvisaker.
Minimax and maximin distance designs.
Journal of Statistical Planning and Inference, 26(2):131–148,
1990.
[ bib ]
Keywords: Bayesian design

[1105]

David S. Johnson, Christos H. Papadimitriou, and M. Yannakakis.
How Easy is Local Search?
Journal of Computer System Science, 37(1):79–100, 1988.
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[1106]

David S. Johnson.
Local Optimization and the Traveling Salesman Problem.
In M. Paterson, editor, Automata, Languages and Programming,
17th International Colloquium, volume 443 of Lecture Notes in Computer
Science, pages 446–461. Springer, Heidelberg, Germany, 1990.
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[1107]

David S. Johnson, Lyle A. McGeoch, C. Rego, and Fred Glover.
8th DIMACS Implementation Challenge: The Traveling Salesman
Problem.
http://dimacs.rutgers.edu/archive/Challenges/TSP, 2001.
[ bib ]
Keywords: TSP Challenge, RUE, RCE, generators

[1108]

David S. Johnson.
A Theoretician's Guide to the Experimental Analysis of
Algorithms.
In M. H. Goldwasser, D. S. Johnson, and C. C. McGeoch, editors,
Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth
DIMACS Implementation Challenges, pages 215–250. American Mathematical
Society, Providence, RI, 2002.
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[1109]

Kenneth A. De Jong.
Evolutionary computation: a unified approach.
MIT Press, Cambridge, MA, 2006.
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[1110]

D. R. Jones, M. Schonlau, and W. J. Welch.
Efficient Global Optimization of Expensive BlackBox Functions.
Journal of Global Optimization, 13(4):455–492, 1998.
[ bib ]
Proposed EGO algorithm
Keywords: EGO

[1111]

Kenneth A. De Jong and William M. Spears.
A formal analysis of the role of multipoint crossover in
genetic algorithms.
Annals of Mathematics and Artificial Intelligence, 5(1):1–26,
1992.
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[1112]

D. E. Joslin and D. P. Clements.
Squeaky Wheel Optimization.
Journal of Artificial Intelligence Research, 10:353–373, 1999.
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[1113]

P. W. Jowitt and G. Germanopoulos.
Optimal pump scheduling in water supply networks.
Journal of Water Resources Planning and Management, ASCE,
118(4):406–422, 1992.
[ bib ]
The electricity cost of pumping accounts for a large
part of the total operating cost for watersupply
networks. This study presents a method based on
linear programming for determining an optimal
(minimum cost) schedule of pumping on a 24hr
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
consumerdemand profile, and the hydraulic
characteristics and operational constraints of the
network. The use of extendedperiod 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
computationtime requirements, and is therefore
suitable for realtime implementation.

[1114]

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

[1115]

Angel A. Juan, Helena R. Lourenço, Manuel Mateo, Rachel Luo, and Quim
Castellà.
Using Iterated Local Search for Solving the Flowshop Problem:
Parallelization, Parametrization, and Randomization Issues.
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21(1):103–126, 2014.
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H. Juillé and J. B. Pollack.
A SamplingBased Heuristic for Tree Search Applied to Grammar
Induction.
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[1117]

Bryant A. Julstrom.
What Have You Done for Me Lately? Adapting Operator
Probabilities in a SteadyState Genetic Algorithm.
In L. J. Eshelman, editor, ICGA, pages 81–87. Morgan Kaufmann
Publishers, San Francisco, CA, 1995.
[ bib ]

[1118]

M. Jünger, Gerhard Reinelt, and S. Thienel.
Provably Good Solutions for the Traveling Salesman Problem.
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[ bib ]

[1119]

Elena A. Kabova, Jason C. Cole, Oliver Korb, Manuel LópezIbáñez,
Adrian C. Williams, and Kenneth Shankland.
Improved performance of crystal structure solution from powder
diffraction data through parameter tuning of a simulated annealing
algorithm.
Journal of Applied Crystallography, 50(5):1411–1420, October
2017.
[ bib 
DOI ]
Significant gains in the performance of the simulated
annealing algorithm in the DASH software package have
been realized by using the irace automatic
configuration tool to optimize the values of three key
simulated annealing parameters. Specifically, the success
rate in finding the global minimum in intensity χ^{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

[1120]

Serdar Kadioglu, Yuri Malitsky, Meinolf Sellmann, and Kevin Tierney.
ISAC: InstanceSpecific Algorithm Configuration.
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Daniel Kahneman and Amos Tversky.
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[1122]

Daniel Kahneman.
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[1123]

H. Kaji, Kokolo Ikeda, and Hajime Kita.
Avoidance of constraint violation for experimentbased
evolutionary multiobjective optimization.
In Proceedings of the 2009 Congress on Evolutionary Computation
(CEC 2009), pages 2756–2763, Piscataway, NJ, 2009. IEEE Press.
[ bib 
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Keywords: evolutionary computation;constraint
violation;experimentbased evolutionary multiobjective
optimization;evolutionary algorithm;riskyconstraint
violation;Constraint optimization;Diesel
engines;Calibration;Evolutionary computation;Electric
breakdown;Optimization methods;Uncertainty;Computational
fluid dynamics;Cost function;Temperature

[1124]

Qinma Kang, Hong He, and Jun Wei.
An Effective Iterated Greedy Algorithm for Reliabilityoriented
Task Allocation in Distributed Computing Systems.
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[1125]

Korhan Karabulut.
A hybrid iterated greedy algorithm for total tardiness
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[1126]

Dervis Karaboga and Bahriye Akay.
A Survey: Algorithms Simulating Bee Swarm Intelligence.
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[1127]

Giorgos Karafotias, Agoston E. Eiben, and Mark Hoogendoorn.
Generic parameter control with reinforcement learning.
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Giorgos Karafotias, Mark Hoogendoorn, and Agoston E. Eiben.
Parameter Control in Evolutionary Algorithms: Trends and
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IEEE Transactions on Evolutionary Computation, 19(2):167–187,
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[1129]

Giorgos Karafotias, Mark Hoogendoorn, and Agoston E. Eiben.
Evaluating reward definitions for parameter control.
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[1130]

İbrahim Karahan and Murat Köksalan.
A territory defining multiobjective evolutionary algorithms and
preference incorporation.
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2010.
[ bib 
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Keywords: TDEA

[1131]

Daniel Karapetyan, Andrew J. Parkes, and Thomas Stützle.
Algorithm Configuration: Learning policies for the quick
termination of poor performers.
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[1132]

Oleksiy Karpenko, Jianming Shi, and Yang Dai.
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Giorgos Karafotias, Selmar K. Smit, and Agoston E. Eiben.
A generic approach to parameter control.
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Korhan Karabulut and Fatih M. Tasgetiren.
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Joseph R. Kasprzyk, Shanthi Nataraj, Patrick M. Reed, and Robert J. Lempert.
Many objective robust decision making for complex environmental
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Keywords: scenariobased

[1136]

Joseph R. Kasprzyk, Patrick M. Reed, Gregory W. Characklis, and Brian R.
Kirsch.
Manyobjective de Novo water supply portfolio planning under
deep uncertainty.
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Keywords: scenariobased

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K. Katayama and H. Narihisa.
Iterated Local Search Approach using Genetic Transformation to
the Traveling Salesman Problem.
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Michael D. Kazantzis, Angus R. Simpson, David Kwong, and Shyh Min Tan.
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Artem Kaznatcheev, David A. Cohen, and Peter Jeavons.
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[1141]

Liangjun Ke, Claudia Archetti, and Zuren Feng.
Ants can solve the team orienteering problem.
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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 prespecified limit. In this paper,
an ant colony optimization (ACO) approach is
proposed for the team orienteering problem. Four
methods, i.e., the sequential,
deterministicconcurrent and randomconcurrent and
simultaneous methods, are proposed to construct
candidate solutions in the framework of ACO. We
compare these methods according to the results
obtained on wellknown 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

[1142]

Liangjun Ke, Zuren Feng, Zongben Xu, Ke Shang, and Yonggang Wang.
A multiobjective ACO algorithm for rough feature selection.
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Eric Kee, Sarah Airey, and Walling Cyre.
An adaptive genetic algorithm.
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Hans Kellerer, Ulrich Pferschy, and David Pisinger.
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Springer, 2004.
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Robert E. Keller and Riccardo Poli.
Linear genetic programming of parsimonious metaheuristics.
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Robert E. Keller and Riccardo Poli.
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Graham Kendall, Ruibin Bai, Jacek Blazewicz, Patrick De Causmaecker, Michel
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J. Kennedy and Russell C. Eberhart.
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J. Kennedy, Russell C. Eberhart, and Yuhui Shi.
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Pascal Kerschke, Holger H. Hoos, Frank Neumann, and Heike Trautmann.
Automated Algorithm Selection: Survey and Perspectives.
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Pascal Kerschke and Heike Trautmann.
The Rpackage FLACCO for exploratory landscape
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Pascal Kerschke and Heike Trautmann.
Automated Algorithm Selection on Continuous BlackBox Problems
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Evolutionary Computation, 27(1):99–127, 2019.
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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 blackbox optimization
problems. Focusing on algorithm performance results of the
COCO platform of several years, we construct a representative
set of highperforming 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 BlackBox 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.

[1157]

Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz,
Heike Trautmann, and Michael T. M. Emmerich.
Towards Analyzing Multimodality of Continuous Multiobjective
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Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz,
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Keras development team.
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HARKing: Hypothesizing After the Results are Known.
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[1161]

M. Kerrisk.
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V. Khare, X. Yao, and Kalyanmoy Deb.
Performance Scaling of Multiobjective Evolutionary Algorithms.
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M. Khichane, P. Albert, and Christine Solnon.
Integration of ACO in a Constraint Programming Language.
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M. Khichane, P. Albert, and Christine Solnon.
An ACOBased Reactive Framework for Ant Colony Optimization:
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A. R. KhudaBukhsh, Lin Xu, Holger H. Hoos, and Kevin LeytonBrown.
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Philip Kilby and Tommaso Urli.
Fleet design optimisation from historical data using constraint
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Constraints, pages 1–20, 2015.
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Keywords: Frace

[1168]

YeongDae Kim.
Heuristics for Flowshop Scheduling Problems Minimizing Mean
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[1169]

Youngmin Kim, Richard Allmendinger, and Manuel LópezIbáñez.
Safe Learning and Optimization Techniques: Towards a Survey of
the State of the Art.
Arxiv preprint arXiv:2101.09505 [cs.LG], 2020.
[ bib 
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Safe learning and optimization deals with learning and
optimization problems that avoid, as much as possible, the
evaluation of nonsafe 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.

[1170]

Youngmin Kim, Richard Allmendinger, and Manuel LópezIbáñez.
Safe Learning and Optimization Techniques: Towards a Survey of
the State of the Art.
In F. Heintz, M. Milano, and B. O'Sullivan, editors, Trustworthy
AI – Integrating Learning, Optimization and Reasoning. TAILOR 2020, volume
12641 of Lecture Notes in Computer Science, pages 123–139. Springer,
Cham, Switzerland, 2020.
[ bib 
DOI ]
Safe learning and optimization deals with learning and
optimization problems that avoid, as much as possible, the
evaluation of nonsafe input points, which are solutions,
policies, or strategies that cause an irrecoverable loss
(e.g., breakage of a machine or equipment, or life
threat). Although a comprehensive survey of safe
reinforcement learning algorithms was published in 2015, a
number of new algorithms have been proposed thereafter, and
related works in active learning and in optimization were not
considered. This paper reviews those algorithms from a number
of domains including reinforcement learning, Gaussian process
regression and classification, evolutionary computing, and
active learning. We provide the fundamental concepts on which
the reviewed algorithms are based and a characterization of
the individual algorithms. We conclude by explaining how the
algorithms are connected and suggestions for future
research.

[1171]

Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi.
Bayesian Optimization with Approximate Set Kernels.
Machine Learning, 2021.
[ bib 
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We propose a practical Bayesian optimization method over
sets, to minimize a blackbox function that takes a set as a
single input. Because set inputs are permutationinvariant,
traditional Gaussian processbased Bayesian optimization
strategies which assume vector inputs can fall short. To
address this, we develop a Bayesian optimization method with
set kernel that is used to build surrogate
functions. This kernel accumulates similarity over set
elements to enforce permutationinvariance, 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 positivedefinite and is an
unbiased estimator of the true set kernel with upperbounded
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.

[1172]

J.S. Kim, J.H. Park, and D.H. Lee.
Iterated Greedy Algorithms to Minimize the Total Family Flow
Time for Jobshop Scheduling with Job Families and Sequencedependent
Setups.
Engineering Optimization, 49(10):1719–1732, 2017.
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[1173]

Diederik P Kingma and Jimmy Ba.
Adam: A method for stochastic optimization.
Arxiv preprint arXiv:1412.6980 [cs.LG], 2014.
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Published as a conference paper at the 3rd International
Conference for Learning Representations, San Diego, 2015

[1174]

Scott Kirkpatrick and G. Toulouse.
Configuration Space Analysis of Travelling Salesman Problems.
Journal de Physique, 46(8):1277–1292, 1985.
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[1175]

Scott Kirkpatrick.
Optimization by Simulated Annealing: Quantitative Studies.
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[1176]

Scott Kirkpatrick, C. D. Gelatt, and M. P. Vecchi.
Optimization by Simulated Annealing.
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Anton J. Kleywegt, Alexander Shapiro, and Tito HomemdeMello.
The Sample Average Approximation Method for Stochastic Discrete
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SIAM Journal on Optimization, 12(2):479–502, 2002.
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[1178]

Joshua D. Knowles.
A summaryattainmentsurface plotting method for visualizing the
performance of stochastic multiobjective optimizers.
In A. Abraham and M. Paprzycki, editors, Proceedings of the 5th
International Conference on Intelligent Systems Design and Applications,
pages 552–557, 2005.
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[1179]

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

[1180]

Joshua D. Knowles.
Closedloop evolutionary multiobjective optimization.
IEEE Computational Intelligence Magazine, 4:77–91, 2009.
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[1181]

Joshua D. Knowles and David Corne.
Approximating the Nondominated Front Using the Pareto Archived
Evolution Strategy.
Evolutionary Computation, 8(2):149–172, 2000.
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[1182]

Joshua D. Knowles and David Corne.
The Pareto Archived Evolution Strategy: A New Baseline
Algorithm for Multiobjective Optimisation.
In Proceedings of the 1999 Congress on Evolutionary Computation
(CEC 1999), pages 98–105. IEEE Press, Piscataway, NJ, 1999.
[ bib ]
first mention of Adaptive Grid Archiving

[1183]

Joshua D. Knowles and David Corne.
On Metrics for Comparing NonDominated Sets.
In Proceedings of the 2002 Congress on Evolutionary Computation
(CEC'02), pages 711–716. IEEE Press, Piscataway, NJ, 2002.
[ bib ]

[1184]

Joshua D. Knowles and David Corne.
Instance Generators and Test Suites for the Multiobjective
Quadratic Assignment Problem.
In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele,
editors, Evolutionary Multicriterion Optimization, EMO 2003, volume
2632 of Lecture Notes in Computer Science, pages 295–310. Springer,
Heidelberg, Germany, 2003.
[ bib ]

[1185]

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

[1186]

Joshua D. Knowles and David Corne.
Bounded Pareto Archiving: Theory and Practice.
In X. Gandibleux, M. Sevaux, K. Sörensen, and V. T'Kindt,
editors, Metaheuristics for Multiobjective Optimisation, volume 535 of
Lecture Notes in Economics and Mathematical Systems, pages 39–64.
Springer, Berlin, Germany, 2004.
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[1187]

Joshua D. Knowles and David Corne.
Memetic algorithms for multiobjective optimization: issues,
methods and prospects.
In H. W. E., S. J. E., and K. N., editors, Recent Advances in
Memetic Algorithms, volume 166 of Studies in Fuzziness and Soft
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[1188]

Joshua D. Knowles, David Corne, and Kalyanmoy Deb.
Introduction: Problem solving, EC and EMO.
In J. D. Knowles, D. Corne, K. Deb, and D. R. Chair, editors,
Multiobjective Problem Solving from Nature, Natural Computing Series, pages
1–28. Springer, 2008.
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[1189]

Joshua D. Knowles, David Corne, and Mark Fleischer.
Bounded archiving using the Lebesgue measure.
In Proceedings of the 2003 Congress on Evolutionary Computation
(CEC 2003), volume 4, pages 2490–2497. IEEE Press, Piscataway, NJ, December
2003.
[ bib ]

[1190]

Joshua D. Knowles, David Corne, and Alan P. Reynolds.
Noisy Multiobjective Optimization on a Budget of 250
Evaluations.
In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.K. Hao, and
M. Sevaux, editors, Evolutionary Multicriterion Optimization, EMO
2009, volume 5467 of Lecture Notes in Computer Science, pages 36–50.
Springer, Heidelberg, Germany, 2009.
[ bib ]

[1191]

Joshua D. Knowles, Lothar Thiele, and Eckart Zitzler.
A tutorial on the performance assessment of stochastic
multiobjective optimizers.
TIKReport 214, Computer Engineering and Networks Laboratory (TIK),
Swiss Federal Institute of Technology (ETH), Zürich, Switzerland,
February 2006.
Revised version.
[ bib 
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[1192]

Joshua D. Knowles, Richard A. Watson, and David Corne.
Reducing Local Optima in SingleObjective Problems by
Multiobjectivization.
In E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, and
D. Corne, editors, Evolutionary Multicriterion Optimization, EMO 2001,
volume 1993 of Lecture Notes in Computer Science, pages 269–283.
Springer, Heidelberg, Germany, 2001.
[ bib 
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Proposed multiobjectivization

[1193]

Joshua D. Knowles.
LocalSearch and Hybrid Evolutionary Algorithms for Pareto
Optimization.
PhD thesis, University of Reading, UK, 2002.
[ bib ]
(Examiners: Prof. K. Deb and Prof. K. Warwick)

[1194]

Gary A. Kochenberger, Fred Glover, Bahram Alidaee, and Cesar Rego.
A unified modeling and solution framework for combinatorial
optimization problems.
OR Spektrum, 26(2):237–250, 2004.
[ bib ]

[1195]

Gary A. Kochenberger, JinKao Hao, Fred Glover, Mark Lewis, Zhipeng Lü,
Haibo Wang, and Yang Wang.
The unconstrained binary quadratic programming problem: a
survey.
Journal of Combinatorial Optimization, 28(1):58–81, 2014.
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[1196]

Murat Köksalan.
Multiobjective Combinatorial Optimization: Some Approaches.
Journal of MultiCriteria Decision Analysis, 15:69–78, 2009.
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[1197]

Murat Köksalan and İbrahim Karahan.
An Interactive Territory Defining Evolutionary Algorithm:
iTDEA.
IEEE Transactions on Evolutionary Computation, 14(5):702–722,
October 2010.
[ bib 
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[1198]

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

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A. Kolen and Erwin Pesch.
Genetic Local Search in Combinatorial Optimization.
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Joshua B. Kollat and Patrick M. Reed.
A framework for visually interactive decisionmaking and design
using evolutionary multiobjective optimization (VIDEO).
Environmental Modelling & Software, 22(12):1691–1704, 2007.
[ bib ]
Keywords: glyph plot

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T. C. Koopmans and M. J. Beckmann.
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Springer, Heidelberg, Germany, 2013.
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[1292]

Hui Li and Qingfu Zhang.
Multiobjective Optimization Problems with Complicated Pareto
sets, MOEA/D and NSGAII.
IEEE Transactions on Evolutionary Computation, 13(2):284–302,
2009.
[ bib ]

[1293]

Tianjun Liao, Dogan Aydin, and Thomas Stützle.
Artificial Bee Colonies for Continuous Optimization:
Experimental Analysis and Improvements.
Swarm Intelligence, 7(4):327–356, 2013.
[ bib ]

[1294]

Tianjun Liao, Daniel Molina, Marco A. Montes de Oca, and Thomas Stützle.
A Note on the Effects of Enforcing Bound Constraints on
Algorithm Comparisons using the IEEE CEC'05 Benchmark Function Suite.
Evolutionary Computation, 22(2):351–359, 2014.
[ bib ]

[1295]

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

[1296]

Tianjun Liao, Marco A. Montes de Oca, Dogan Aydin, Thomas Stützle,
and Marco Dorigo.
An Incremental Ant Colony Algorithm with Local Search for
Continuous Optimization.
In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2011, pages 125–132.
ACM Press, New York, NY, 2011.
[ bib ]

[1297]

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

[1298]

Tianjun Liao, Marco A. Montes de Oca, and Thomas Stützle.
Tuning Parameters across Mixed Dimensional Instances: A
Performance Scalability Study of SepGCMAES.
In N. Krasnogor and P. L. Lanzi, editors, GECCO (Companion),
pages 703–706, New York, NY, 2011. ACM Press.
[ bib ]
Workshop on Scaling Behaviours of Landscapes,
Parameters and Algorithms

[1299]

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

[1300]

Tianjun Liao and Thomas Stützle.
Benchmark results for a simple hybrid algorithm on the CEC
2013 benchmark set for realparameter optimization.
In Proceedings of the 2013 Congress on Evolutionary Computation
(CEC 2013), pages 1938–1944. IEEE Press, Piscataway, NJ, 2013.
[ bib ]

[1301]

Tianjun Liao, Thomas Stützle, Marco A. Montes de Oca, and Marco Dorigo.
A Unified Ant Colony Optimization Algorithm for Continuous
Optimization.
European Journal of Operational Research, 234(3):597–609,
2014.
[ bib ]

[1302]

C.J. Liao, C.T. Tseng, and P. Luarn.
A Discrete Version of Particle Swarm Optimization for Flowshop
Scheduling Problems.
Computers & Operations Research, 34(10):3099–3111, 2007.
[ bib ]

[1303]

Tianjun Liao.
Populationbased Heuristic Algorithms for Continuous and Mixed
DiscreteContinuous Optimization Problem.
PhD thesis, IRIDIA, École polytechnique, Université Libre de
Bruxelles, Belgium, 2013.
[ bib ]

[1304]

Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Hernán E. Aguirre, and
Kiyoshi Tanaka.
Towards LandscapeAware Automatic Algorithm Configuration:
Preliminary Experiments on Neutral and Rugged Landscapes.
In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. M.
Wiecek, Y. Jin, and C. Grimme, editors, Evolutionary Multicriterion
Optimization, EMO 2017, Lecture Notes in Computer Science, pages 215–232.
Springer International Publishing, Cham, Switzerland, 2017.
[ bib ]

[1305]

Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Manuel
LópezIbáñez, Hernán E. Aguirre, and Kiyoshi Tanaka.
On Pareto Local Optimal Solutions Networks.
In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete,
and D. Whitley, editors, Parallel Problem Solving from Nature  PPSN
XV, volume 11102 of Lecture Notes in Computer Science, pages 232–244.
Springer, Cham, 2018.
[ bib 
DOI ]

[1306]

Arnaud Liefooghe, Jérémie Humeau, Salma Mesmoudi, Laetitia Jourdan, and
ElGhazali Talbi.
On dominancebased multiobjective local search: design,
implementation and experimental analysis on scheduling and traveling salesman
problems.
Journal of Heuristics, 18(2):317–352, 2012.
[ bib 
DOI ]
This paper discusses simple local search approaches for
approximating the efficient set of multiobjective
combinatorial optimization problems. We focus on algorithms
defined by a neighborhood structure and a dominance relation
that iteratively improve an archive of nondominated
solutions. Such methods are referred to as dominancebased
multiobjective local search. We first provide a concise
overview of existing algorithms, and we propose a model
trying to unify them through a finegrained
decomposition. The main problemindependent search components
of dominance relation, solution selection, neighborhood
exploration and archiving are largely discussed. Then, a
number of stateoftheart and original strategies are
experimented on solving a permutation flowshop scheduling
problem and a traveling salesman problem, both on a two and
a threeobjective formulation. Experimental results and a
statistical comparison are reported in the paper, and some
directions for future research are highlighted.

[1307]

Arnaud Liefooghe, Laetitia Jourdan, and ElGhazali Talbi.
A Software Framework Based on a Conceptual Unified Model for
Evolutionary Multiobjective Optimization: ParadisEOMOEO.
European Journal of Operational Research, 209(2):104–112,
2011.
[ bib ]

[1308]

Arnaud Liefooghe, Manuel LópezIbáñez, Luís Paquete, and
Sébastien Verel.
Dominance, Epsilon, and Hypervolume Local Optimal Sets in
Multiobjective Optimization, and How to Tell the Difference.
In H. E. Aguirre and K. Takadama, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2018, pages 324–331.
ACM Press, New York, NY, 2018.
[ bib 
DOI 
pdf ]

[1309]

Arnaud Liefooghe, Salma Mesmoudi, Jérémie Humeau, Laetitia Jourdan, and
ElGhazali Talbi.
A Study on Dominancebased Local Search Approaches for
Multiobjective Combinatorial Optimization.
In T. Stützle, M. Birattari, and H. H. Hoos, editors,
Engineering Stochastic Local Search Algorithms. Designing, Implementing and
Analyzing Effective Heuristics. SLS 2009, volume 5752 of Lecture Notes
in Computer Science, pages 120–124. Springer, Heidelberg, Germany, 2009.
[ bib ]

[1310]

Arnaud Liefooghe, Luís Paquete, Marco Simoes, and José Rui
Figueira.
Connectedness and Local Search for Bicriteria Knapsack
Problems.
In P. Merz and J.K. Hao, editors, Proceedings of EvoCOP 2011 –
11th European Conference on Evolutionary Computation in Combinatorial
Optimization, volume 6622 of Lecture Notes in Computer Science, pages
48–59. Springer, Heidelberg, Germany, 2011.
[ bib 
DOI ]

[1311]

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

[1312]

David J. Lilja.
Measuring Computer Performance: A Practitioner's Guide.
Cambridge University Press, 2000.
[ bib 
DOI ]
Measuring Computer Performance sets out the fundamental
techniques used in analyzing and understanding the
performance of computer systems. Throughout the book, the
emphasis is on practical methods of measurement, simulation,
and analytical modeling. The author discusses performance
metrics and provides detailed coverage of the strategies used
in benchmark programmes. He gives intuitive explanations of
the key statistical tools needed to interpret measured
performance data. He also describes the general 'design of
experiments' technique, and shows how the maximum amount of
information can be obtained for the minimum effort. The book
closes with a chapter on the technique of queueing
analysis. Appendices listing common probability distributions
and statistical tables are included, along with a glossary of
important technical terms. This practicallyoriented book
will be of great interest to anyone who wants a detailed, yet
intuitive, understanding of computer systems performance
analysis.

[1313]

Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, and Torsten Schaub.
AutoFolio: Algorithm Configuration for Algorithm Selection.
In B. Bonet and S. Koenig, editors, Proceedings of the AAAI
Conference on Artificial Intelligence. AAAI Press, 2015.
[ bib ]

[1314]

Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, and Torsten Schaub.
AutoFolio: An Automatically Configured Algorithm Selector.
Journal of Artificial Intelligence Research, 53:745–778, 2015.
[ bib ]

[1315]

S. Lin and B. W. Kernighan.
An Effective Heuristic Algorithm for the Traveling Salesman
Problem.
Operations Research, 21(2):498–516, 1973.
[ bib ]

[1316]

W. Ling and H. Luo.
An Adaptive Parameter Control Strategy for Ant Colony
Optimization.
In CIS'07: Proceedings of the 2007 International Conference on
Computational Intelligence and Security, pages 142–146, Washington, DC,
2007. IEEE Computer Society.
[ bib ]

[1317]

Marius Thomas Lindauer, Jan N. Van Rijn, and Lars Kotthoff.
The algorithm selection competitions 2015 and 2017.
Artificial Intelligence, 272:86–100, 2019.
[ bib ]

[1318]

Andrei Lissovoi and Carsten Witt.
Runtime Analysis of Ant Colony Optimization on Dynamic Shortest
Path Problems.
Theoretical Computer Science, 561(Part A):73–85, 2015.
[ bib 
DOI ]
A simple {ACO} algorithm called λMMAS for dynamic
variants of the singledestination shortest paths problem is
studied by rigorous runtime analyses. Building upon previous
results for the special case of 1MMAS, it is studied to what
extent an enlarged colony using λ ants per vertex
helps in tracking an oscillating optimum. It is shown that
easy cases of oscillations can be tracked by a constant
number of ants. However, the paper also identifies more
involved oscillations that with overwhelming probability
cannot be tracked with any polynomialsize colony. Finally,
parameters of dynamic shortestpath problems which make the
optimum difficult to track are discussed. Experiments
illustrate theoretical findings and conjectures.

[1319]

J. D. C. Little, K. G. Murty, D. W. Sweeney, and C. Karel.
An Algorithm for the Traveling Salesman Problem.
Operations Research, 11:972–989, 1963.
[ bib ]

[1320]

Shusen Liu, Dan Maljovec, Bei Wang, PeerTimo Bremer, and Valerio Pascucci.
Visualizing HighDimensional Data: Advances in the Past Decade.
IEEE Transactions on Visualization and Computer Graphics,
23(3), 2017.
[ bib 
DOI ]

[1321]

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

[1322]

Innovation 24.
LocalSolver.
http://www.localsolver.com/product.html, 2016.
Last visited, August 15, 2016.
[ bib ]

[1323]

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

[1324]

Andrea Lodi, Silvano Martello, and Daniele Vigo.
TSpack: a unified tabu search code for multidimensional bin
packing problems.
Annals of Operations Research, 131(14):203–213, 2004.
[ bib 
DOI ]

[1325]

Andrea Lodi and Andrea Tramontani.
Performance Variability in MixedInteger Programming.
In H. Topaluglu, editor, Theory Driven by Influential
Applications, pages 1–12. INFORMS Journal on Computing, 2013.
[ bib ]

[1326]

Andrea Lodi, Silvano Martello, and Daniele Vigo.
Two and ThreeDimensional Bin Packing – Source Code of
TSpack.
http://or.dei.unibo.it/research_pages/ORcodes/TSpack.html,
2004.
[ bib ]

[1327]

PoLing Loh and Sebastian Nowozin.
Faster Hoeffding Racing: Bernstein Races via Jackknife
Estimates.
In S. Jain, R. Munos, F. Stephan, and T. Zeugmann, editors,
Proceedings of Algorithmic Learning Theory, volume 8139 of Lecture
Notes in Computer Science, pages 203–217, Berlin, Germany, 2013. Springer.
[ bib 
DOI ]

[1328]

Manuel LópezIbáñez.
High Performance Ant Colony Optimisation of the Pump Scheduling
Problem.
In P. Alberigo, G. Erbacci, F. Garofalo, and S. Monfardini, editors,
Science and Sumpercomputing in Europe, pages 371–375. CINECA, 2007.
[ bib ]

[1329]

Manuel LópezIbáñez and Christian Blum.
BeamACO Based on Stochastic Sampling: A Case Study on the
TSP with Time Windows.
Technical Report LSI0828, Department LSI, Universitat
Politècnica de Catalunya, 2008.
Extended version published in Computers & Operations
Research [1332].
[ bib ]

[1330]

Manuel LópezIbáñez, Christian Blum, Dhananjay Thiruvady,
Andreas T. Ernst, and Bernd Meyer.
BeamACO based on stochastic sampling for makespan
optimization concerning the TSP with time windows.
In C. Cotta and P. Cowling, editors, Proceedings of EvoCOP 2009
– 9th European Conference on Evolutionary Computation in Combinatorial
Optimization, volume 5482 of Lecture Notes in Computer Science, pages
97–108. Springer, Heidelberg, Germany, 2009.
[ bib 
DOI 
pdf ]

[1331]

Manuel LópezIbáñez and Christian Blum.
BeamACO Based on Stochastic Sampling: A Case Study on the
TSP with Time Windows.
In T. Stützle, editor, Learning and Intelligent
Optimization, Third International Conference, LION 3, volume 5851 of
Lecture Notes in Computer Science, pages 59–73. Springer, Heidelberg,
Germany, 2009.
[ bib 
DOI ]

[1332]

Manuel LópezIbáñez and Christian Blum.
BeamACO for the travelling salesman problem with time
windows.
Computers & Operations Research, 37(9):1570–1583, 2010.
[ bib 
DOI ]
The travelling salesman problem with time windows is
a difficult optimization problem that arises, for
example, in logistics. This paper deals with the
minimization of the travelcost. For solving this
problem, this paper proposes a BeamACO algorithm,
which is a hybrid method combining ant colony
optimization with beam search. In general, BeamACO
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
BeamACO algorithm is currently a stateoftheart
technique for the travelling salesman problem with
time windows when travelcost optimization is
concerned.
Keywords: Ant colony optimization, Travelling salesman problem with
time windows, Hybridization

[1333]

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

[1334]

Manuel LópezIbáñez, Jürgen Branke, and Luís Paquete.
Reproducibility in Evolutionary Computation.
Arxiv preprint arXiv:20102.03380 [cs.AI], 2021.
[ bib 
http ]
Experimental studies are prevalent in Evolutionary
Computation (EC), and concerns about the reproducibility and
replicability of such studies have increased in recent times,
reflecting similar concerns in other scientific fields. In
this article, we suggest a classification of different types
of reproducibility that refines the badge system of the
Association of Computing Machinery (ACM) adopted by TELO. We
discuss, within the context of EC, the different types of
reproducibility as well as the concepts of artifact and
measurement, which are crucial for claiming
reproducibility. We identify cultural and technical obstacles
to reproducibility in the EC field. Finally, we provide
guidelines and suggest tools that may help to overcome some
of these reproducibility obstacles.
Keywords: Evolutionary Computation, Reproducibility, Empirical study,
Benchmarking

[1335]

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

[1336]

Manuel LópezIbáñez, Jérémie DuboisLacoste, Leslie
Pérez Cáceres, Thomas Stützle, and Mauro Birattari.
The irace Package: Iterated Racing for Automatic
Algorithm Configuration (Supplementary Material).
http://iridia.ulb.ac.be/supp/IridiaSupp2016003, 2016.
[ bib ]

[1337]

Manuel LópezIbáñez, Jérémie DuboisLacoste, Thomas
Stützle, and Mauro Birattari.
The irace package, Iterated Race for Automatic
Algorithm Configuration.
Technical Report TR/IRIDIA/2011004, IRIDIA, Université Libre de
Bruxelles, Belgium, 2011.
Published in Operations Research
Perspectives [1335].
[ bib 
http ]

[1338]

Manuel LópezIbáñez, MarieEléonore Kessaci, and Thomas
Stützle.
Automatic Design of Hybrid Metaheuristics from Algorithmic
Components.
Submitted, 2017.
[ bib ]

[1339]

Manuel LópezIbáñez and Joshua D. Knowles.
Machine Decision Makers as a Laboratory for Interactive EMO.
In A. GasparCunha, C. H. Antunes, and C. A. Coello Coello,
editors, Evolutionary Multicriterion Optimization, EMO 2015 Part II,
volume 9019 of Lecture Notes in Computer Science, pages 295–309.
Springer, Heidelberg, Germany, 2015.
[ bib 
DOI 
pdf ]
A key challenge, perhaps the central challenge, of
multiobjective optimization is how to deal with candidate
solutions that are ultimately evaluated by the hidden or
unknown preferences of a human decision maker (DM) who
understands and cares about the optimization problem.
Alternative ways of addressing this challenge exist but
perhaps the favoured one currently is the interactive
approach (proposed in various forms). Here, an evolutionary
multiobjective optimization algorithm (EMOA) is controlled
by a series of interactions with the DM so that preferences
can be elicited and the direction of search controlled. MCDM
has a key role to play in designing and evaluating these
approaches, particularly in testing them with real DMs, but
so far quantitative assessment of interactive EMOAs has been
limited. In this paper, we propose a conceptual framework
for this problem of quantitative assessment, based on the
definition of machine decision makers (machine DMs), made
somewhat realistic by the incorporation of various
nonidealities. The machine DM proposed here draws from
earlier models of DM biases and inconsistencies in the MCDM
literature. As a practical illustration of our approach, we
use the proposed machine DM to study the performance of an
interactive EMOA, and discuss how this framework could help
in the evaluation and development of better interactive
EMOAs.

[1340]

Manuel LópezIbáñez, Joshua D. Knowles, and Marco Laumanns.
On Sequential Online Archiving of Objective Vectors.
In R. H. C. Takahashi et al., editors, Evolutionary
Multicriterion Optimization, EMO 2011, volume 6576 of Lecture Notes in
Computer Science, pages 46–60. Springer, Heidelberg, Germany, 2011.
[ bib 
DOI ]
In this paper, we examine the problem of maintaining
an approximation of the set of nondominated points
visited during a multiobjective optimization, a
problem commonly known as archiving. Most of the
currently available archiving algorithms are
reviewed, and what is known about their convergence
and approximation properties is summarized. The main
scenario considered is the restricted case where the
archive must be updated online as points are
generated one by one, and at most a fixed number of
points are to be stored in the archive at any one
time. In this scenario, the bettermonotonicity of
an archiving algorithm is proposed as a weaker, but
more practical, property than negative efficiency
preservation. This paper shows that
hypervolumebased archivers and a recently proposed
multilevel grid archiver have this property. On the
other hand, the archiving methods used by SPEA2 and
NSGAII do not, and they may betterdeteriorate with
time. The bettermonotonicity property has meaning
on any input sequence of points. We also classify
archivers according to limit properties,
i.e. convergence and approximation properties of the
archiver in the limit of infinite (input) samples
from a finite space with strictly positive
generation probabilities for all points. This paper
establishes a number of research questions, and
provides the initial framework and analysis for
answering them.
Revised version available at http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011001.pdf

[1341]

Manuel LópezIbáñez, Tianjun Liao, and Thomas Stützle.
On the anytime behavior of IPOPCMAES.
In C. A. Coello Coello et al., editors, Parallel Problem
Solving from Nature, PPSN XII, volume 7491 of Lecture Notes in Computer
Science, pages 357–366. Springer, Heidelberg, Germany, 2012.
[ bib 
DOI ]

[1342]

Manuel LópezIbáñez, Tianjun Liao, and Thomas Stützle.
On the anytime behavior of IPOPCMAES: Supplementary
material.
http://iridia.ulb.ac.be/supp/IridiaSupp2012010/, 2012.
[ bib ]

[1343]

Manuel LópezIbáñez, Arnaud Liefooghe, and Sébastien Verel.
Local Optimal Sets and Bounded Archiving on Multiobjective
NKLandscapes with Correlated Objectives.
In T. BartzBeielstein, J. Branke, B. Filipič, and J. Smith,
editors, PPSN 2014, volume 8672 of Lecture Notes in Computer
Science, pages 621–630. Springer, Heidelberg, Germany, 2014.
[ bib 
DOI 
pdf ]

[1344]

Manuel LópezIbáñez, Franco Mascia, MarieEléonore Marmion, and
Thomas Stützle.
Automatic Design of a Hybrid Iterated Local Search for the
MultiMode ResourceConstrained MultiProject Scheduling Problem.
In G. Kendall, G. V. Berghe, and B. McCollum, editors,
Multidisciplinary International Conference on Scheduling: Theory and
Applications (MISTA 2013), pages 1–6, Gent, Belgium, 2013.
[ bib 
pdf ]
https://hal.inria.fr/hal01094681

[1345]

Manuel LópezIbáñez, Luís Paquete, and Thomas Stützle.
On the Design of ACO for the Biobjective Quadratic Assignment
Problem.
In M. Dorigo et al., editors, Ant Colony Optimization and Swarm
Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of
Lecture Notes in Computer Science, pages 214–225. Springer, Heidelberg,
Germany, 2004.
[ bib 
DOI ]

[1346]

Manuel LópezIbáñez, Luís Paquete, and Thomas Stützle.
Hybrid Populationbased Algorithms for the Biobjective
Quadratic Assignment Problem.
Technical Report AIDA–04–11, FG Intellektik, FB Informatik, TU
Darmstadt, December 2004.
Published in Journal of Mathematical Modelling and
Algorithms [1347].
[ bib ]

[1347]

Manuel LópezIbáñez, Luís Paquete, and Thomas Stützle.
Hybrid Populationbased Algorithms for the Biobjective
Quadratic Assignment Problem.
Journal of Mathematical Modelling and Algorithms,
5(1):111–137, 2006.
[ bib 
DOI 
pdf ]
We present variants of an ant colony optimization
(MOACO) algorithm and of an evolutionary algorithm
(SPEA2) for tackling multiobjective 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 biobjective
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.

[1348]

Manuel LópezIbáñez, Luís Paquete, and Thomas Stützle.
Exploratory Analysis of Stochastic Local Search Algorithms in
Biobjective Optimization.
In T. BartzBeielstein, M. Chiarandini, L. Paquete, and M. Preuss,
editors, Experimental Methods for the Analysis of Optimization
Algorithms, pages 209–222. Springer, Berlin, Germany, 2010.
[ bib 
DOI ]
This chapter introduces two Perl programs that
implement graphical tools for exploring the
performance of stochastic local search algorithms
for biobjective optimization problems. These tools
are based on the concept of the empirical attainment
function (EAF), which describes the probabilistic
distribution of the outcomes obtained by a
stochastic algorithm in the objective space. In
particular, we consider the visualization of
attainment surfaces and differences between the
firstorder EAFs of the outcomes of two
algorithms. This visualization allows us to identify
certain algorithmic behaviors in a graphical way.
We explain the use of these visualization tools and
illustrate them with examples arising from
practice.

[1349]

Manuel LópezIbáñez, Luís Paquete, and Thomas Stützle.
EAF Graphical Tools.
http://lopezibanez.eu/eaftools, 2010.
These tools are described in the book chapter “Exploratory
analysis of stochastic local search algorithms in biobjective
optimization” [1348].
[ bib ]
Please cite the book chapter, not this.

[1350]

Manuel LópezIbáñez, Leslie Pérez Cáceres, Jérémie
DuboisLacoste, Thomas Stützle, and Mauro Birattari.
The irace package: User Guide.
Technical Report TR/IRIDIA/2016004, IRIDIA, Université Libre de
Bruxelles, Belgium, 2016.
[ bib 
http ]

[1351]

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

[1352]

Manuel LópezIbáñez, T. Devi Prasad, and Ben Paechter.
Parallel Optimisation Of Pump Schedules With A ThreadSafe
Variant Of EPANET Toolkit.
In J. E. van Zyl, A. A. Ilemobade, and H. E. Jacobs, editors,
Proceedings of the 10th Annual Water Distribution Systems Analysis Conference
(WDSA 2008). ASCE, August 2008.
[ bib 
DOI 
pdf ]

[1353]

Manuel LópezIbáñez, T. Devi Prasad, and Ben Paechter.
Ant Colony Optimisation for the Optimal Control of Pumps in
Water Distribution Networks.
Journal of Water Resources Planning and Management, ASCE,
134(4):337–346, 2008.
[ bib 
DOI 
http 
pdf ]
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
metaheuristic 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 realworld network. Results are
compared with those obtained using a simple genetic algorithm based
on binary representation and a hybrid genetic algorithm that uses
levelbased triggers.

[1354]

Manuel LópezIbáñez, T. Devi Prasad, and Ben Paechter.
Representations and Evolutionary Operators for the Scheduling of
Pump Operations in Water Distribution Networks.
Evolutionary Computation, 19(3):429–467, 2011.
[ bib 
DOI ]
Reducing the energy consumption of water
distribution networks has never had more
significance. The greatest energy savings can be
obtained by carefully scheduling the operations of
pumps. Schedules can be defined either implicitly,
in terms of other elements of the network such as
tank levels, or explicitly by specifying the time
during which each pump is on/off. The traditional
representation of explicit schedules is a string of
binary values with each bit representing pump on/off
status during a particular time interval. In this
paper, we formally define and analyze two new
explicit representations based on timecontrolled
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
mostused representations in pump scheduling,
namely, binary representation and levelcontrolled
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 stateoftheart Hybrid Genetic
Algorithm for pump scheduling using levelcontrolled
triggers.

[1355]

Manuel LópezIbáñez, T. Devi Prasad, and Ben Paechter.
Solving Optimal Pump Control Problem using MaxMin Ant System.
In D. Thierens et al., editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2007, volume 1, page 176. ACM
Press, New York, NY, 2007.
[ bib 
DOI 
pdf ]

[1356]

Manuel LópezIbáñez, T. Devi Prasad, and Ben Paechter.
Multiobjective Optimisation of the Pump Scheduling Problem
using SPEA2.
In Proceedings of the 2005 Congress on Evolutionary Computation
(CEC 2005), volume 1, pages 435–442. IEEE Press, Piscataway, NJ, September
2005.
[ bib 
DOI ]

[1357]

Manuel LópezIbáñez, T. Devi Prasad, and Ben Paechter.
Optimal Pump Scheduling: Representation and Multiple
Objectives.
In D. A. Savic, G. A. Walters, R. King, and S. ThiamKhu, editors,
Proceedings of the Eighth International Conference on Computing and
Control for the Water Industry (CCWI 2005), volume 1, pages 117–122,
University of Exeter, UK, September 2005.
[ bib 
pdf ]

[1358]

Manuel LópezIbáñez and Thomas Stützle.
An Analysis of Algorithmic Components for Multiobjective Ant
Colony Optimization: A Case Study on the Biobjective TSP.
In P. Collet, N. Monmarché, P. Legrand, M. Schoenauer, and
E. Lutton, editors, Artificial Evolution: 9th International Conference,
Evolution Artificielle, EA, 2009, volume 5975 of Lecture Notes in
Computer Science, pages 134–145. Springer, Heidelberg, Germany, 2010.
[ bib 
DOI ]

[1359]

Manuel LópezIbáñez and Thomas Stützle.
Automatic Configuration of MultiObjective ACO Algorithms.
In M. Dorigo et al., editors, Swarm Intelligence, 7th
International Conference, ANTS 2010, volume 6234 of Lecture Notes in
Computer Science, pages 95–106. Springer, Heidelberg, Germany, 2010.
[ bib 
DOI ]
In the last few years a significant number of ant
colony optimization (ACO) algorithms have been
proposed for tackling multiobjective optimization
problems. In this paper, we propose a software
framework that allows to instantiate the most
prominent multiobjective ACO (MOACO)
algorithms. More importantly, the flexibility of
this MOACO framework allows the application of
automatic algorithm configuration techniques. The
experimental results presented in this paper show
that such an automatic configuration of MOACO
algorithms is highly desirable, given that our
automatically configured algorithms clearly
outperform the best performing MOACO algorithms that
have been proposed in the literature. As far as we
are aware, this paper is also the first to apply
automatic algorithm configuration techniques to
multiobjective stochastic local search algorithms.

[1360]

Manuel LópezIbáñez and Thomas Stützle.
The impact of design choices of multiobjective ant colony
optimization algorithms on performance: An experimental study on the
biobjective TSP.
In M. Pelikan and J. Branke, editors, Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2010, pages 71–78. ACM
Press, New York, NY, 2010.
[ bib 
DOI ]
Over the last few years, there have been a number of
proposals of ant colony optimization (ACO)
algorithms for tackling multiobjective combinatorial
optimization problems. These proposals adapt ACO
concepts in various ways, for example, some use
multiple pheromone matrices and multiple heuristic
matrices and others use multiple ant colonies.
In
this article, we carefully examine several of the
most prominent of these proposals. In particular, we
identify commonalities among the approaches by
recasting the original formulation of the algorithms
in different terms. For example, several proposals
described in terms of multiple colonies can be cast
equivalently using a single ant colony, where ants
use different weights for aggregating the pheromone
and/or the heuristic information. We study
algorithmic choices for the various proposals and we
identify previously undetected tradeoffs in their
performance.

[1361]

Manuel LópezIbáñez and Thomas Stützle.
The impact of design choices of multiobjective ant colony
optimization algorithms on performance: An experimental study on the
biobjective TSP.
http://iridia.ulb.ac.be/supp/IridiaSupp2010003/, 2010.
Supplementary material of [1360].
[ bib ]

[1362]

Manuel LópezIbáñez and Thomas Stützle.
The Automatic Design of MultiObjective Ant Colony Optimization
Algorithms: Supplementary material, 2011.
[ bib 
http ]

[1363]

Manuel LópezIbáñez and Thomas Stützle.
An experimental analysis of design choices of multiobjective
ant colony optimization algorithms: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2012006/, 2012.
[ bib ]

[1364]

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

[1365]

Manuel LópezIbáñez and Thomas Stützle.
The Automatic Design of MultiObjective Ant Colony Optimization
Algorithms.
IEEE Transactions on Evolutionary Computation, 16(6):861–875,
2012.
[ bib 
DOI ]
Multiobjective 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 multiobjective ACO (MOACO) algorithms exhibit
different design choices for dealing with the particularities of
the multiobjective 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 multiobjective algorithms.

[1366]

Manuel LópezIbáñez and Thomas Stützle.
Automatically Improving the Anytime Behaviour of Optimisation
Algorithms.
European Journal of Operational Research, 235(3):569–582,
2014.
[ bib 
DOI 
pdf 
supplementary material ]
Optimisation algorithms with good anytime behaviour try to
return as highquality 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 tradeoff curve
between computation time and solution quality. Yet, the
tradeoff curve may be modelled also as a set of mutually
nondominated, biobjective 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
decisionmaker'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 MAXMIN Ant
System and (ii) the tuning of the anytime behaviour of SCIP,
an opensource mixed integer programming solver with more
than 200 parameters.

[1367]

Manuel LópezIbáñez, Thomas Stützle, and Marco Dorigo.
Ant Colony Optimization: A ComponentWise Overview.
In R. Martí, P. M. Pardalos, and M. G. C. Resende, editors,
Handbook of Heuristics, pages 371–407. Springer International Publishing,
2018.
[ bib 
DOI 
supplementary material ]

[1368]

Eunice LĂłpezCamacho, Hugo TerashimaMarin, Peter Ross, and Gabriela Ochoa.
A unified hyperheuristic framework for solving bin packing
problems.
Expert Systems with Applications, 41(15):6876–6889, 2014.
[ bib 
DOI ]

[1369]

Manuel LópezIbáñez.
Multiobjective Ant Colony Optimization.
Diploma thesis, Intellectics Group, Computer Science Department,
Technische Universität Darmstadt, Germany, 2004.
[ bib 
pdf ]

[1370]

Manuel LópezIbáñez.
Operational Optimisation of Water Distribution Networks.
PhD thesis, School of Engineering and the Built Environment,
Edinburgh Napier University, UK, 2009.
[ bib 
http ]

[1371]

Ilya Loshchilov, Marc Schoenauer, and Michèle Sebag.
Alternative Restart Strategies for CMAES.
In C. A. Coello Coello et al., editors, Parallel Problem
Solving from Nature, PPSN XII, volume 7491 of Lecture Notes in Computer
Science, pages 296–305. Springer, Heidelberg, Germany, 2012.
[ bib 
DOI ]

[1372]

A. V. Lotov and Kaisa Miettinen.
Visualizing the Pareto Frontier.
In J. Branke, K. Deb, K. Miettinen, and R. Slowiński, editors,
Multiobjective Optimization: Interactive and Evolutionary Approaches,
volume 5252 of Lecture Notes in Computer Science, pages 213–243.
Springer, Heidelberg, Germany, 2008.
[ bib ]

[1373]

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

[1374]

Helena R. Lourenço, Olivier Martin, and Thomas Stützle.
Iterated Local Search.
In F. Glover and G. Kochenberger, editors, Handbook of
Metaheuristics, pages 321–353. Kluwer Academic Publishers, Norwell, MA,
2002.
[ bib 
DOI ]

[1375]

Helena R. Lourenço, Olivier Martin, and Thomas Stützle.
Iterated Local Search: Framework and Applications.
In M. Gendreau and J.Y. Potvin, editors, Handbook of
Metaheuristics, volume 146 of International Series in Operations
Research & Management Science, chapter 9, pages 363–397. Springer, New
York, NY, 2nd edition, 2010.
[ bib 
DOI ]

[1376]

Helena R. Lourenço, Olivier Martin, and Thomas Stützle.
Iterated Local Search: Framework and Applications.
In M. Gendreau and J.Y. Potvin, editors, Handbook of
Metaheuristics, volume 272 of International Series in Operations
Research & Management Science, chapter 5, pages 129–168. Springer, 2019.
[ bib 
DOI ]

[1377]

Helena R. Lourenço.
JobShop Scheduling: Computational Study of Local Search and
LargeStep Optimization Methods.
European Journal of Operational Research, 83(2):347–364, 1995.
[ bib ]

[1378]

Antonio Lova and Pilar Tormos.
Analysis of Scheduling Schemes and Heuristic Rules Performance
in ResourceConstrained Multiproject Scheduling.
Annals of Operations Research, 102(14):263–286, February
2001.
[ bib 
DOI ]
Frequently, the availability of resources assigned
to a project is limited and not sufficient to
execute all the concurrent activities. In this
situation, decision making about their schedule is
necessary. Many times this schedule supposes an
increase in the project completion
time. Additionally, companies commonly manage
various projects simultaneously, sharing a pool of
renewable resources. Given these resource
constraints, we often can only apply heuristic
methods to solve the scheduling problem. In this
work the effect of the schedule generation schemes 
serial or parallel  and priority rules  MINLFT,
MINSLK, MAXTWK, SASP or FCFS  with two
approaches  multiproject and singleproject  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 multiproject 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 twophase approach 
that outperform classical ones are proposed to
minimise mean project delay with a multiproject
approach. Finally, the best heuristics analysed are
evaluated together with a representative sample of
commercial project management software.
Keywords: Combinatorics, heuristic based on priority rules,
Multiproject scheduling, Operation
Research/Decision Theory, Project management,
project management software, Resource allocation,
Theory of Computation

[1379]

Antonio Lova, Pilar Tormos, Mariamar Cervantes, and Federico Barber.
An efficient hybrid genetic algorithm for scheduling projects
with resource constraints and multiple execution modes.
International Journal of Production Economics, 117(2):302–316,
2009.
[ bib 
DOI ]
Multimode 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 (MMHGA) to
solve it. Its main contributions are the mode
assignment procedure, the fitness function and the
use of a very efficient improving method. Its
performance is demonstrated by extensive
computational results obtained on a set of standard
instances and against the best currently available
algorithms.
Keywords: genetic algorithm, multimode resourceconstrained
project scheduling

[1380]

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

[1381]

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

[1382]

Zhipeng Lü, Fred Glover, and JinKao Hao.
A hybrid metaheuristic approach to solving the UBQP problem.
European Journal of Operational Research, 207(3):1254–1262,
2010.
[ bib 
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[1383]

M. Lundy and A. Mees.
Convergence of an Annealing Algorithm.
Mathematical Programming, 34(1):111–124, 1986.
[ bib ]

[1384]

Thibaut Lust and Jacques Teghem.
Twophase Pareto local search for the biobjective traveling
salesman problem.
Journal of Heuristics, 16(3):475–510, 2010.
[ bib 
DOI ]
In this work, we present a method, called TwoPhase
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
stateoftheart algorithms. Two other points are introduced:
the notion of ideal set and a simple way to produce
nearefficient solutions of multiobjective problems, by using
an efficient singleobjective solver with a data perturbation
technique.

[1385]

Thibaut Lust and Jacques Teghem.
The multiobjective traveling salesman problem: A survey and a
new approach.
In C. A. Coello Coello, C. Dhaenens, and L. Jourdan, editors,
Advances in MultiObjective Nature Inspired Computing, volume 272 of
Studies in Computational Intelligence, pages 119–141. Springer, 2010.
[ bib ]

[1386]

Thibaut Lust and Jacques Teghem.
The multiobjective multidimensional knapsack problem: a survey
and a new approach.
Arxiv preprint arXiv:1007.4063, 2010.
[ bib ]
Published as [1387]

[1387]

Thibaut Lust and Jacques Teghem.
The multiobjective multidimensional knapsack problem: a survey
and a new approach.
International Transactions in Operational Research,
19(4):495–520, 2012.
[ bib 
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[1388]

Thibaut Lust and Andrzej Jaszkiewicz.
Speedup techniques for solving largescale biobjective TSP.
Computers & Operations Research, 37(3):521–533, 2010.
[ bib 
DOI ]
Keywords: Multiobjective combinatorial optimization, Hybrid
metaheuristics, TSP, Local search, Speedup techniques

[1389]

C. von Lücken, Benjamín Barán, and Carlos Brizuela.
A survey on multiobjective evolutionary algorithms for
manyobjective problems.
Computational Optimization and Applications, 58(3):707–756,
2014.
[ bib ]

[1390]

Qingfu Zhang.
MOEA/D homepage.
https://dces.essex.ac.uk/staff/zhang/webofmoead.htm, 2007.
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[1391]

Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew
Hausknecht, and Michael Bowling.
Revisiting the Arcade Learning Environment: Evaluation
Protocols and Open Problems for General Agents.
Journal of Artificial Intelligence Research, 61(1):523–562,
January 2018.
[ bib ]
The Arcade Learning Environment (ALE) is an evaluation
platform that poses the challenge of building AI agents with
general competency across dozens of Atari 2600 games. It
supports a variety of different problem settings and it has
been receiving increasing attention from the scientific
community, leading to some highpro_le success stories such
as the much publicized Deep QNetworks (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
stateoftheart in various problems and highlighting
problems that remain open.

[1392]

Gunther Mäckle, Dragan A. Savic, and Godfrey A. Walters.
Application of Genetic Algorithms to Pump Scheduling for Water
Supply.
In Genetic Algorithms in Engineering Systems: Innovations and
Applications, GALESIA'95, volume 414, pages 400–405, Sheffield, UK,
September 1995. IEE Conference Publication.
[ bib 
http ]
A simple Genetic Algorithm has been applied to the
scheduling of multiple pumping units in a water
supply system with the objective of minimising the
overall cost of the pumping operation, taking
advantage of storage capacity in the system and the
availability of off peak electricity tariffs. A
simple example shows that the method is easy to
apply and has produced encouraging preliminary
results

[1393]

Nateri K. Madavan.
Multiobjective optimization using a Pareto differential
evolution approach.
In D. B. Fogel et al., editors, Proceedings of the 2002 World
Congress on Computational Intelligence (WCCI 2002), pages 1145–1150,
Piscataway, NJ, 2002. IEEE Press.
[ bib ]

[1394]

Sam Madden.
From Databases to Big Data.
IEEE Internet Computing, 16(3), 2012.
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[1395]

M. Mahdavi, M. Fesanghary, and E. Damangir.
An improved harmony search algorithm for solving optimization
problems.
Applied Mathematics and Computation, 188(2):1567–1579, 2007.
[ bib 
DOI ]
This paper develops an Improved harmony search (IHS)
algorithm for solving optimization problems. IHS employs a
novel method for generating new solution vectors that
enhances accuracy and convergence rate of harmony search (HS)
algorithm. In this paper the impacts of constant parameters
on harmony search algorithm are discussed and a strategy for
tuning these parameters is presented. The IHS algorithm has
been successfully applied to various benchmarking and
standard engineering optimization problems. Numerical results
reveal that the proposed algorithm can find better solutions
when compared to HS and other heuristic or deterministic
methods and is a powerful search algorithm for various
engineering optimization problems.
Keywords: Global optimization, Heuristics, Harmony search algorithm,
Mathematical programming

[1396]

Guilherme B. Mainieri and Débora P. Ronconi.
New heuristics for total tardiness minimization in a flexible
flowshop.
Optimization Letters, pages 1–20, 2012.
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D. R. Broad, Graeme C. Dandy, and Holger R. Maier.
A Metamodeling Approach to Water Distribution System
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Holger R. Maier, Angus R. Simpson, Aaron C. Zecchin, Wai Kuan Foong, Kuang Yeow
Phang, Hsin Yeow Seah, and Chan Lim Tan.
Ant Colony Optimization for Design of Water Distribution
Systems.
Journal of Water Resources Planning and Management, ASCE,
129(3):200–209, May / June 2003.
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Yuri Malitsky and Meinolf Sellmann.
Instancespecific algorithm configuration as a method for
nonmodelbased portfolio generation.
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Integration of AI and OR Techniques in Contraint Programming for
Combinatorial Optimization Problems, volume 7298 of Lecture Notes in
Computer Science, pages 244–259. Springer, Heidelberg, Germany, 2012.
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Algorithm for mixing problems in water systems.
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Yuri Malitsky, Deepak Mehta, Barry O'Sullivan, and Helmut Simonis.
Tuning parameters of large neighborhood search for the machine
reassignment problem.
In C. Gomes and M. Sellmann, editors, Integration of AI and OR
Techniques in Constraint Programming for Combinatorial Optimization Problems,
CPAIOR 2010, volume 7874 of Lecture Notes in Computer Science, pages
176–192. Springer, Heidelberg, Germany, 2013.
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Vittorio Maniezzo.
Exact and Approximate Nondeterministic TreeSearch Procedures
for the Quadratic Assignment Problem.
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Vittorio Maniezzo, M. Boschetti, and M. Jelasity.
An Ant Approach to Membership Overlay Design.
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Vittorio Maniezzo and A. Carbonaro.
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The Ant System Applied to the Quadratic Assignment Problem.
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An AntBased Framework for Very Strongly Constrained Problems.
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[1412]

MarieEléonore Marmion, Clarisse Dhaenens, Laetitia Jourdan, Arnaud
Liefooghe, and Sébastien Verel.
NILS: A NeutralityBased Iterated Local Search and Its
Application to Flowshop Scheduling.
In P. Merz and J.K. Hao, editors, Proceedings of EvoCOP 2011 –
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[1413]

MarieEléonore Marmion, Franco Mascia, Manuel LópezIbáñez, and
Thomas Stützle.
Automatic Design of Hybrid Stochastic Local Search Algorithms.
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Oded Maron.
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A certifying algorithm is an algorithm that produces, with
each output, a certificate or witness (easytoverify 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 noncertifying 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
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The scheduling of pumps for clean water distribution
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variables. The scheduling method described in this
paper typically produces costs within 1% of a
linear programbased solution, and can incorporate
realistic nonlinear costs that may be hard to
incorporate in linear programming
formulations. These costs include pump switching and
maximum demand charges. A simplified model is
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James McDermott.
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Although experimental studies have been widely applied to the
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unfortunate, since much can be done to improve the quality of
the data obtained; often, much improvement may be needed for
the data to be useful. This paper gives a tutorial discussion
of two aspects of good experimental technique: the use of
variance reduction techniques and simulation speedups in
algorithm studies. In an illustrative study, application of
variance reduction techniques produces a decrease in variance
by a factor 1000 in one case, giving a dramatic improvement
in the precision of experimental results. Furthermore, the
complexity of the simulation program is improved from
Θ(m n/H_{n}) to Θ(m + n logn) (where m is
typically much larger than n), giving a much faster
simulation program and therefore more data per unit of
computation time. The general application of variance
reduction techniques is also discussed for a variety of
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selforganizing sequential search, statistical analysis of
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Russell McKenna, Valentin Bertsch, Kai Mainzer, and Wolf Fichtner.
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Kaisa Miettinen.
Nonlinear Multiobjective Optimization.
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Nonlinear Multiobjective Optimization provides an extensive,
uptodate, selfcontained and consistent survey and review
of the literature and of the state of the art on nonlinear
(deterministic) multiobjective optimization, its methods, its
theory and its background. This book is intended for both
researchers and students in the areas of (applied)
mathematics, engineering, economics, operations research and
management science; it is meant for both professionals and
practitioners in many different fields of application. The
intention is to provide a consistent summary that may help in
selecting an appropriate method for the problem to be
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We give an overview of interactive methods developed
for solving nonlinear multiobjective optimization
problems. In interactive methods, a decision maker
plays an important part and the idea is to support
her/him in the search for the most preferred
solution. In interactive methods, steps of an
iterative solution algorithm are repeated and the
decision maker progressively provides preference
information so that the most preferred solution can
be found. We identify three types of specifying
preference information in interactive methods and
give some examples of methods representing each
type. The types are methods based on tradeoff
information, reference points and classification of
objective functions.

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as hindsight bias, make it hard to avoid this mistake. An
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that result from postdictions. A variety of practical
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In this research, we proposed to build an automated framework
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an artificial DM constructed from two distinct parts: the
steady part and the current context. With the steady part the
artificial DM tries to maintain the search towards its
preferences, while at the same time the current context
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This paper describes the methodology and application
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hydraulics. The objective is to minimize the total
cost of operating the pumping units and the chlorine
booster operation and design for a selected
operational time horizon, while delivering the
consumers required water quantities, at acceptable
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decision variables, for each of the time steps that
encompass the total operational time horizon,
include: the scheduling of the pumping units,
settings of the water distribution system control
valves, and the mass injection rates at each of the
booster chlorination stations. The constraints are
domain heads and chlorine concentrations at the
consumer nodes, maximum injection rates at the
chlorine injection stations, maximum allowable
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Automatic Design of Hybrid Stochastic Local Search Algorithms
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Sinno Jialin Pan and Qiang Yang.
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Luís Paquete.
Algoritmos Evolutivos Multiobjectivo para Afectação de
Recursos e sua Aplicação à Geração de Horários em
Universidades (Multiobjective Evolutionary Algorithms for Resource
Allocation and their Application to University Timetabling).
Master's thesis, University of Algarve, 2001.
In Portuguese.
[ bib ]
The aim of this study is the application of
multiobjective evolutionary algorithms to resource
allocation problems, such as university examination
timetabling and course timetabling
problems. Usually, these problems are characterized
by multiple conflicting objectives. A multiobjective
formalization of these problems is presented, based
on goals and priorities. Various aspects of
evolutionary algorithms are proposed and studied for
these problems, particulary, selection methods and
types and parameters of mutation operator. The
choice of both representation and operators is made
so as not to favour excessively certain objectives
with respect to others at the level of the
exploration mechanism. A comparative study of
performance is presented for the proposed algorithms
by means of statistical inference, based on real
problems of the University of Algarve. The notion of
attainment functions is used as a base for the
assessment of performance of multiobjective
evolutionary algorithms. Finally, the evolution of
the solution cost during the runs is analysed by
means of attainment functions, as well.

[1647]

Luís Paquete.
Stochastic Local Search Algorithms for Multiobjective
Combinatorial Optimization: Methods and Analysis.
PhD thesis, FB Informatik, TU Darmstadt, Germany, 2005.
[ bib ]

[1648]

Luís Paquete, Marco Chiarandini, and Thomas Stützle.
Pareto Local Optimum Sets in the Biobjective Traveling
Salesman Problem: An Experimental Study.
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editors, Metaheuristics for Multiobjective Optimisation, volume 535 of
Lecture Notes in Economics and Mathematical Systems, pages 177–199.
Springer, Berlin, Germany, 2004.
[ bib 
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In this article, we study Pareto local optimum sets for the
biobjective Traveling Salesman Problem applying
straightforward extensions of local search algorithms for the
single objective case. The performance of the local search
algorithms is illustrated by experimental results obtained
for well known benchmark instances and comparisons to methods
from literature. In fact, a 3opt local search is able to
compete with the best performing metaheuristics in terms of
solution quality. Finally, we also present an empirical study
of the features of the solutions found by 3opt on a set of
randomly generated instances. The results indicate the
existence of several clusters of nearoptimal solutions that
are separated by only a few edges.
Keywords: Pareto local search, PLS

[1649]

Luís Paquete, Carlos M. Fonseca, and Manuel LópezIbáñez.
An optimal algorithm for a special case of Klee's measure
problem in three dimensions.
Technical Report CSIRTI01/2006, CSI, Universidade do Algarve,
2006.
Superseded by paper in IEEE Transactions on Evolutionary
Computation [194].
[ bib ]
The measure of the region dominated by (the maxima
of) a set of n points in the positive dorthant
has been proposed as an indicator of performance in
multiobjective optimization, known as the
hypervolume indicator, and the problem of computing
it efficiently is attracting increasing
attention. In this report, this problem is
formulated as a special case of Klee's measure
problem in d dimensions, which immediately
establishes O(n^{d/2}logn) as a, possibly
conservative, upper bound on the required
computation time. Then, an O(n log n) algorithm
for the 3dimensional version of this special case
is constructed, based on an existing dimensionsweep
algorithm for the related maxima problem. Finally,
O(n log n) is shown to remain a lower bound on the
time required by the hypervolume indicator for
d>1, which attests the optimality of the algorithm
proposed.
Proof of Theorem 3.1 is incorrect

[1650]

Luís Paquete, Tommaso Schiavinotto, and Thomas Stützle.
On Local Optima in Multiobjective Combinatorial Optimization
Problems.
Annals of Operations Research, 156:83–97, 2007.
[ bib 
DOI ]
In this article, local optimality in multiobjective
combinatorial optimization is used as a baseline for
the design and analysis of two iterative improvement
algorithms. Both algorithms search in a neighborhood
that is defined on a collection of sets of feasible
solutions and their acceptance criterion is based on
outperformance relations. Proofs of the soundness
and completeness of these algorithms are given.
Keywords: Pareto local search, PLS

[1651]

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

[1652]

Luís Paquete and Thomas Stützle.
Clusters of nondominated solutions in multiobjective
combinatorial optimization: An experimental analysis.
In V. Barichard, M. Ehrgott, X. Gandibleux, and V. T'Kindt, editors,
Multiobjective Programming and Goal Programming: Theoretical Results and
Practical Applications, volume 618 of Lecture Notes in Economics and
Mathematical Systems, pages 69–77. Springer, Berlin, 2009.
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[1653]

Luís Paquete and Thomas Stützle.
Design and analysis of stochastic local search for the
multiobjective traveling salesman problem.
Computers & Operations Research, 36(9):2619–2631, 2009.
[ bib 
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[1654]

Luís Paquete and Thomas Stützle.
An Experimental Investigation of Iterated Local Search for
Coloring Graphs.
In S. Cagnoni et al., editors, Applications of Evolutionary
Computing, Proceedings of EvoWorkshops 2002, volume 2279 of Lecture
Notes in Computer Science, pages 122–131. Springer, Heidelberg, Germany,
2002.
[ bib ]

[1655]

Luís Paquete and Thomas Stützle.
A TwoPhase Local Search for the Biobjective Traveling Salesman
Problem.
In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele,
editors, Evolutionary Multicriterion Optimization, EMO 2003, volume
2632 of Lecture Notes in Computer Science, pages 479–493. Springer,
Heidelberg, Germany, 2003.
[ bib ]

[1656]

Luís Paquete, Thomas Stützle, and Manuel LópezIbáñez.
On the design and analysis of SLS algorithms for
multiobjective combinatorial optimization problems.
Technical Report TR/IRIDIA/2005029, IRIDIA, Université Libre de
Bruxelles, Belgium, 2005.
[ bib 
http ]
Effective Stochastic Local Search (SLS) algorithms
can be seen as being composed of several algorithmic
components, each of which plays some specific role
with respect to overall performance. In this
article, we explore the application of experimental
design techniques to analyze the effect of different
choices for these algorithmic components on SLS
algorithms applied to Multiobjective Combinatorial
Optimization Problems that are solved in terms of
Pareto optimality. This analysis is done using the
example application of SLS algorithms to the
biobjective Quadratic Assignment Problem and we show
also that the same choices for algorithmic
components can lead to different behavior in
dependence of various instance features, such as the
structure of input data and the correlation between
objectives.

[1657]

Luís Paquete, Thomas Stützle, and Manuel LópezIbáñez.
Towards the Empirical Analysis of SLS Algorithms for
Multiobjective Combinatorial Optimization Problems through Experimental
Design.
In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F.
Hartl, and M. Reimann, editors, 6th Metaheuristics International
Conference (MIC 2005), pages 739–746, Vienna, Austria, 2005.
[ bib 
pdf ]
Stochastic Local Search (SLS) algorithms for
Multiobjective Combinatorial Optimization Problems
(MCOPs) typically involve the selection and
parameterization of many algorithm components whose
role with respect to their overall performance and
relation to certain instance features is often not
clear. In this abstract, we use a modular approach
for the design of SLS algorithms for MCOPs defined
in terms of Pareto optimality and we present an
extensive analysis of SLS algorithms through
experimental design techniques, where each algorithm
component is considered a factor. The experimental
analysis is based on a sound experimental
methodology for analyzing the output of algorithms
for MCOPs. We show that different choices for
algorithm components can lead to different behavior
in dependence of various instance features.

[1658]

Luís Paquete, Thomas Stützle, and Manuel LópezIbáñez.
Using experimental design to analyze stochastic local search
algorithms for multiobjective problems.
In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F.
Hartl, and M. Reimann, editors, Metaheuristics: Progress in Complex
Systems Optimization, volume 39 of Operations Research / Computer
Science Interfaces, pages 325–344. Springer, New York, NY, 2007.
[ bib 
DOI ]
Stochastic Local Search (SLS) algorithms can be seen
as being composed of several algorithmic components,
each playing some specific role with respect to
overall performance. This article explores the
application of experimental design techniques to
analyze the effect of components of SLS algorithms
for Multiobjective Combinatorial Optimization
problems, in particular for the Biobjective
Quadratic Assignment Problem. The analysis shows
that there exists a strong dependence between the
choices for these components and various instance
features, such as the structure of the input data
and the correlation between the objectives.
PostConference Proceedings of the 6th
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Automatic algorithm configuration techniques have proved to
be successful in finding performanceoptimizing parameter
settings of many searchbased decision and optimization
algorithms. A recurrent, important step in software
development is the compilation of source code written in some
programming language into machineexecutable code. The
generation of performanceoptimized machine code itself is a
difficult task that can be parametrized in many different
possible ways. While modern compilers usually offer different
levels of optimization as possible defaults, they have a
larger number of other flags and numerical parameters that
impact properties of the generated machinecode. While the
generation of performanceoptimized machine code has received
large attention and is dealt with in the research area of
autotuning, the usage of standard automatic algorithm
configuration software has not been explored, even though, as
we show in this article, the performance of the compiled code
has significant stochasticity, just as standard optimization
algorithms. As a practical case study, we consider the
configuration of the wellknown GNU compiler collection (GCC)
for minimizing the runtime of machine code for various
heuristic search methods. Our experimental results show that,
depending on the specific code to be optimized, improvements
of up to 40% of execution time when compared to the O2
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Leslie Pérez Cáceres, Federico Pagnozzi, Alberto Franzin, and Thomas
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Automatic Algorithm Configuration: Analysis, Improvements and
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PhD thesis, IRIDIA, École polytechnique, Université Libre de
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Michael L. Pinedo.
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In this chapter we discuss the ant colony
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application to static and dynamic constraint
satisfaction optimization problems, in particular
the static and dynamic maximum satisfiability
problems (MAXSAT). In the first part of the
chapter we give an introduction to metaheuristics
in general and ant colony optimization in
particular, followed by an introduction to
constraint satisfaction and static and dynamic
constraint satisfaction optimization problems.
Then, we describe how to apply the ACO algorithm
to the problems, and do an analysis of the results
obtained for several benchmarks. The adapted ant
colony optimization accomplishes very well the task
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David Pisinger and Stefan Ropke.
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Rapeepan Pitakaso, Christian Almeder, Karl F. Doerner, and Richard F. Hartl.
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In this paper, we present an antbased algorithm
for solving unconstrained multilevel lotsizing
problems called ant system for multilevel
lotsizing algorithm (ASMLLS). We apply a hybrid
approach where we use ant colony optimization in
order to find a good lotsizing sequence, i.e. a
sequence of the different items in the product
structure in which we apply a modified
WagnerWhitin algorithm for each item
separately. Based on the setup costs each ant
generates a sequence of items. Afterwards a simple
singlestage lotsizing rule is applied with
modified setup costs. This modification of the setup
costs depends on the position of the item in the
lotsizing sequence, on the items which have been
lotsized before, and on two further parameters,
which are tried to be improved by a systematic
search. For smallsized problems ASMLLS is among
the best algorithms, but for most medium and
largesized problems it outperforms all other
approaches regarding solution quality as well as
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Keywords: Ant colony optimization, Material requirements
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Marco Pranzo and D. Pacciarelli.
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Heatmap visualization of population based multi objective
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The problem of connecting a set of client nodes
with known demands to a root node through a minimum
cost tree network, subject to capacity constraints
on all links is known as the capacitated minimum
spanning tree (CMST) problem. As the problem is
NPhard, we propose a hybrid ant colony
optimization (ACO) algorithm to tackle it
heuristically. The algorithm exploits two important
problem characteristics: (i) the CMST problem is
closely related to the capacitated vehicle routing
problem (CVRP), and (ii) given a clustering of
client nodes that satisfies capacity constraints,
the solution is to find a MST for each cluster,
which can be done exactly in polynomial time. Our
ACO exploits these two characteristics of the
CMST by a solution construction originally
developed for the CVRP. Given the CVRP solution,
we then apply an implementation of Prim's algorithm
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This study presents a novel evidential reasoning (ER)
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features embedded in Twitter posts (tweets) can predict the
number of retweets achieved during an electoral campaign. The
tweets posted by the two most voted candidates during the
official campaign for the 2017 Ecuadorian Presidential
election were used for this research. For each tweet, five
features including type of tweet, emotion, URL, hashtag, and
date are identified and coded to predict if tweets are of
either high or low impact. The main contributions of the new
proposed model include its suitability to analyse tweet
datasets based on likelihood analysis of data. The model is
interpretable, and the prediction process relies only on the
use of available data. The experimental results show that
MAKERRIMER performed better, in terms of misclassification
error, when compared against other predictive machine
learning approaches. In addition, the model allows observing
which features of the candidates' tweets are linked to high
and low impact. Tweets containing allusions to the contender
candidate, either with positive or negative connotations,
without hashtags, and written towards the end of the
campaign, were persistently those with the highest
impact. URLs, on the other hand, is the only variable that
performs differently for the two candidates in terms of
achieving high impact. MAKERRIMER can provide campaigners of
political parties or candidates with a tool to measure how
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be useful to tailor Twitter content during electoral
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Keywords: Evidential reasoning rule,Belief rulebased inference,Maximum
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Pramod J. Sadalage and Martin Fowler.
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Bhupinder Singh Saini, Manuel LópezIbáñez, and Kaisa Miettinen.
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pdf ]
A typical scenario when solving industrial single or
multiobjective optimization problems is that no explicit
formulation of the problem is available. Instead, a dataset
containing vectors of decision variables together with their
objective function value(s) is given and a surrogate model
(or metamodel) is build from the data and used for
optimization and decisionmaking. This datadriven
optimization process strongly depends on the ability of the
surrogate model to predict the objective value of decision
variables not present in the original dataset. Therefore, the
choice of surrogate modelling technique is crucial. While
many surrogate modelling techniques have been discussed in
the literature, there is no standard procedure that will
select the best technique for a given problem. In this work,
we propose the automatic selection of a surrogate modelling
technique based on exploratory landscape features of the
optimization problem that underlies the given dataset. The
overall idea is to learn offline from a large pool of
benchmark problems, on which we can evaluate a large number
of surrogate modelling techniques. When given a new dataset,
features are used to select the most appropriate surrogate
modelling technique. The preliminary experiments reported
here suggest that the proposed automatic selector is able to
identify highaccuracy surrogate models as long as an
appropriate classifier is used for selection.

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Yoshitaka Sakurai, Kouhei Takada, Takashi Kawabe, and Setsuo Tsuruta.
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Marcela Samà, Paola Pellegrini, Andrea D'Ariano, Joaquin Rodriguez, and Dario
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J. J. SánchezMedina, M. J. GalánMoreno, and E. RubioRoyo.
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Keywords: cellular automata;genetic algorithms;road traffic;road
vehicles;traffic engineering computing;Beowulf cluster;La
Almozara district;Saragossa;cellular automata;cluster
computing;genetic algorithm;multipleinstruction multiple
data;traffic light programming;traffic
microsimulation;traffic signal optimization;urban traffic
congestion;Cellular automata (CA);genetic algorithms
(GAs);intelligent transportation
systems;microsimulation;traffic congestion;traffic modeling

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the checking of which normally requires O(N)
time. Our method reduces this checking effort to
O(1) time. We also consider the problem of finding
initial solutions. A complexity result is given and
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Dragan A. Savic, Godfrey A. Walters, and Martin Schwab.
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selecting and visiting the points of interests
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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 probabilitybased approaches,
such as stochastic programming, do not address these
problems; as they require a correctlydefined complete sample
space, strong assumptions (e.g. normality), or both. The
proposed method extends the concept of twostage stochastic
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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 scenariobased
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performance under different future conditions as an
alternative to expectation. To the best of our knowledge,
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this paper also proposes an extension of the goal programming
paradigm to deal with deep uncertainty. Furthermore, we will
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Babooshka Shavazipour, Jonas Stray, and T. J. Stewart.
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In this paper, the strategic planning of sugarbioethanol
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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 energyrelated problems. This study is
the first try to fills this gap. Particularly, the
sustainability of the whole infrastructure of the
sugarbioethanol SCs is analysed in such a way that the final
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of South African sugarcane industry is utilised to study and
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