IRIDIA BibTeX Repository

[1]

Emile H. L. Aarts, Jan H. M. Korst, and Wil Michiels.
Simulated Annealing.
In Search Methodologies, pages 187–210. Springer, 2005.
[ bib ]

[2]

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 ]

[3]

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 ]

[4]

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 ]

[5]

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 ]

[6]

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

[7]

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

[8]

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

[9]

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

[10]

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

[11]

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

[12]

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

[13]

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

[14]

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 ]

[15]

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 ]

[16]

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

[17]

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 ]

[18]

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

[19]

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 ]

[20]

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

[21]

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 ]

[22]

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

[23]

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 ]

[24]

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 ]

[25]

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 ]

[26]

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 ]

[27]

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 ]

[28]

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 ]

[29]

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 ]

[30]

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 ]

[31]

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 ]

[32]

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

[33]

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

[34]

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

[35]

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 ]

[36]

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 ]

[37]

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 ]

[38]

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

[39]

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 ]

[40]

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

[41]

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 ]

[42]

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 ]

[43]

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

[44]

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

[45]

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 ]

[46]

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

[47]

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 ]

[48]

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. ACM New York, NY, USA, 2014.
[ bib 
DOI ]

[49]

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 ]
Keywords: GGA++

[50]

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

[51]

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

[52]

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 ]

[53]

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 ]

[54]

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 ]

[55]

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 ]

[56]

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.
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DOI ]

[57]

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

[58]

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 ]

[59]

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 ]

[60]

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 ]

[61]

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 ]

[62]

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 ]

[63]

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

[64]

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.
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DOI ]

[65]

J. E. 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 ]

[66]

J. E. 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

[67]

J.E.C. Arroyo and J.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 ]

[68]

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 ]

[69]

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

[70]

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

[71]

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.

[72]

Alper Atamtürk.
On the facets of the mixed–integer knapsack polyhedron.
Mathematical Programming, 98(1):145–175, 2003.
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[73]

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 ]

[74]

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.
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[75]

Charles Audet, CongKien Dang, and Dominique Orban.
Optimization of Algorithms with OPAL.
Mathematical Programming Computation, 6(3):233–254, 2014.
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[76]

P. Audze and Vilnis Eglãjs.
New approach to the design of multifactor experiments.
Problems of Dynamics and Strengths, 35:104–107, 1977.
(in Russian).
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[77]

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

[78]

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

[79]

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 ]

[80]

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 ]

[81]

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 ]

[82]

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 ]

[83]

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,
Learning in Multiobjective Optimization (Dagstuhl Seminar 12041),
volume 2(1) of Dagstuhl Reports, pages 92–93. Schloss
Dagstuhl–LeibnizZentrum für Informatik, Germany, 2012.
[ bib 
DOI ]

[84]

Anne Auger and Nikolaus Hansen.
A restart CMA evolution strategy with increasing population
size.
In Proceedings of the 2005 Congress on Evolutionary Computation
(CEC 2005), pages 1769–1776. IEEE Press, Piscataway, NJ, September 2005.
[ bib 
DOI ]
Keywords: IPOPCMAES

[85]

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

[86]

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

[87]

Dogan Aydin, Gürcan Yavuz, Serdar Özyön, Celal Yasar, and
Thomas Stützle.
Artificial Bee Colony Framework to Nonconvex Economic Dispatch
Problem with Valve Point Effects: A Case Study.
In P. A. N. Bosman, editor, GECCO'17 Companion, pages
1311–1318, New York, NY, 2017. ACM Press.
[ bib ]

[88]

Dogan Aydin, Gürcan Yavuz, and Thomas Stützle.
ABCX: A Generalized, Automatically Configurable Artificial
Bee Colony Framework.
Swarm Intelligence, 11(1):1–38, 2017.
[ bib ]

[89]

Mahdi Aziz and MohammadH. Tayarani N.
An adaptive memetic Particle Swarm Optimization algorithm for
finding largescale Latin hypercube designs.
Engineering Applications of Artificial Intelligence,
36:222–237, 2014.
[ bib 
DOI ]
Keywords: Frace

[90]

Ilya Loshchilov and T. Glasmachers.
Black Box Optimization Competition, 2017.
[ bib 
http ]

[91]

Anne Auger, Dimo Brockhoff, Nikolaus Hansen, Dejan Tusar, Tea Tušar, and
Tobias Wagner.
GECCO Workshop on RealParameter BlackBox Optimization
Benchmarking (BBOB 2016): Focus on multiobjective problems.
https://numbbo.github.io/workshops/BBOB2016/, 2016.
[ bib ]

[92]

S. Bleuler, Marco Laumanns, Lothar Thiele, and Eckart Zitzler.
PISA – A Platform and Programming Language Independent
Interface for Search Algorithms.
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 494–508. Springer,
Heidelberg, Germany, 2003.
[ bib ]

[93]

Domagoj Babić and Alan J. Hu.
Structural Abstraction of Software Verification Conditions.
In Computer Aided Verification: 19th International Conference,
CAV 2007, pages 366–378, 2007.
[ bib ]
Spearswv instances,
http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/benchmark_instances/SpearSWV/SWVscrambledfirst302.tar.gz,
http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/benchmark_instances/SpearSWV/SWVscrambledlast302.tar.gz

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Hossein Baharmand, Tina Comes, and Matthieu Lauras.
Biobjective multilayer location–allocation model for the
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Transportation Research Part E: Logistics and Transportation
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Locating distribution centers is critical for humanitarians
in the immediate aftermath of a suddenonset disaster. A
major challenge lies in balancing the complexity and
uncertainty of the problem with time and resource
constraints. To address this problem, we propose a
locationallocation model that divides the topography of
affected areas into multiple layers; considers constrained
number and capacity of facilities and fleets; and allows
decisionmakers to explore tradeoffs between response time
and logistics costs. To illustrate our theoretical work, we
apply the model to a real dataset from the 2015 Nepal
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Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
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fails for the quadratic assignment problem.
In M. Dorigo et al., editors, Ant Colony Optimization and Swarm
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Prasanna Balaprakash, Mauro Birattari, and Thomas Stützle.
Improvement Strategies for the FRace Algorithm: Sampling
Design and Iterative Refinement.
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G. Rudolph, and M. Sampels, editors, Hybrid Metaheuristics, volume 4771
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[102]

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, Zhi Yuan, and Marco
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Egon Balas and Neil Simonetti.
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Consider the following restricted (symmetric or
asymmetric) travelingsalesman problem (TSP):
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
procedure, based on contracting some arcs of the
tour produced by a first application of the
algorithm, then reapplying the algorithm to the
shrunken graph with the same k.
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Egon Balas and A. Vazacopoulos.
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Steven C. Bankes.
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uncertain systems.
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Agentbased models (ABM) are examples of complex adaptive
systems, which can be characterized as those systems for
which no model less complex than the system itself can
accurately predict in detail how the system will behave at
future times. Consequently, the standard tools of policy
analysis, based as they are on devising policies that perform
well on some best estimate model of the system, cannot be
reliably used for ABM. This paper argues that policy analysis
by using ABM requires an alternative approach to decision
theory. The general characteristics of such an approach are
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P. Baptiste and L. K. Hguny.
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Thomas BartzBeielstein.
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Thomas BartzBeielstein, Oliver Flasch, Patrick Koch, and Wolfgang Konen.
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Erin Bartholomew and Jan H. Kwakkel.
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Thomas BartzBeielstein, C. Lasarczyk, and Mike Preuss.
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Peer reviews are a unique governance tool that use expertise
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these, a systematic literature review (SLR) of academic and
nonacademic literature was conducted on city resilience peer
reviews. Thirtythree attributes of resilience are
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reviews, to drive meaningful change. Moreover, it situates
the challenges associated with resilience building tools
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Grid search and manual search are the most widely
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theoretically that randomly chosen trials are more
efficient for hyperparameter optimization than
trials on a grid. Empirical evidence comes from a
comparison with a large previous study that used
grid search and manual search to configure neural
networks and deep belief networks. Compared with
neural networks configured by a pure grid search, we
find that random search over the same domain is able
to find models that are as good or better within a
small fraction of the computation time. Granting
random search the same computational budget, random
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a larger, less promising configuration
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32dimensional configuration space found
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from hyperparameters to validation set performance
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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
in large hierarchical models will place an
increasing burden on techniques for hyperparameter
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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
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algorithm for finding the maxima of a point set.

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SMSEMOA: Multiobjective selection based on dominated
hypervolume.
European Journal of Operational Research, 181(3):1653–1669,
2007.
[ bib 
DOI ]

[174]

Nicola Beume and Günther Rudolph.
Faster SMetric Calculation by Considering Dominated
Hypervolume as Klee's Measure Problem.
In B. Kovalerchuk, editor, Proceedings of the Second IASTED
Conference on Computational Intelligence, pages 231–236. ACTA Press,
Anaheim, 2006.
[ bib ]

[175]

HansGeorg Beyer and HansPaul Schwefel.
Evolution Strategies: A Comprehensive Introduction.
Natural Computing, 1:3–52, 2002.
[ bib ]

[176]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic Generation of Multiobjective ACO Algorithms for the
Biobjective Knapsack.
In M. Dorigo et al., editors, Swarm Intelligence, 8th
International Conference, ANTS 2012, volume 7461 of Lecture Notes in
Computer Science, pages 37–48. Springer, Heidelberg, Germany, 2012.
[ bib 
DOI 
pdf 
supplementary material ]

[177]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic Generation of MOACO Algorithms for the Biobjective
Bidimensional Knapsack Problem: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2012008/, 2012.
[ bib ]

[178]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
An Analysis of Local Search for the Biobjective Bidimensional
Knapsack: Supplementary material.
http://iridia.ulb.ac.be/supp/IridiaSupp2012016/, 2013.
[ bib ]

[179]

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.
[ bib ]

[180]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
An Analysis of Local Search for the Biobjective Bidimensional
Knapsack Problem.
In M. Middendorf and C. Blum, editors, Proceedings of EvoCOP
2013 – 13th European Conference on Evolutionary Computation in Combinatorial
Optimization, volume 7832 of Lecture Notes in Computer Science, pages
85–96. Springer, Heidelberg, Germany, 2013.
[ bib 
DOI ]

[181]

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 ]

[182]

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
International Conference, LION 8, volume 8426 of Lecture Notes in
Computer Science, pages 57–172. Springer, Heidelberg, Germany, 2014.
[ bib 
DOI 
supplementary material ]

[183]

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.
[ bib 
DOI ]

[184]

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 ]

[185]

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 ]

[186]

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.
[ bib ]

[187]

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.
[ bib 
DOI ]

[188]

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 
DOI ]

[189]

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 
DOI 
pdf 
supplementary material ]

[190]

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 ]

[191]

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 ]

[192]

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 ]

[193]

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.

[194]

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 ]

[195]

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 ]

[196]

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 ]

[197]

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 ]

[198]

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 
DOI 
pdf 
supplementary material ]

[199]

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 ]

[200]

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.

[201]

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 ]

[202]

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 ]

[203]

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.
[ bib ]

[204]

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.
[ bib ]

[205]

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.
[ bib ]

[206]

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, AAAI Conference on
Artificial Intelligence. AAAI Press, February 2017.
[ bib 
http ]

[207]

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.
[ bib ]

[208]

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

[209]

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

[210]

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.
[ bib ]

[211]

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.
[ bib ]

[212]

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 ]

[213]

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 ]

[214]

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 ]

[215]

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 ]

[216]

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

[217]

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 ]
Keywords: Frace, iterated Frace, irace, tuning

[218]

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 ]

[219]

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 ]

[220]

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

[221]

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

[222]

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 
http ]
Keywords: PaGMO

[223]

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

[224]

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 ]

[225]

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.
[ bib 
http ]

[226]

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

[227]

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

[228]

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 
DOI ]

[229]

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 ]

[230]

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.
[ bib 
http ]

[231]

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 ]

[232]

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 ]

[233]

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 ]

[234]

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.
[ bib 
DOI ]

[235]

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.
[ bib 
DOI ]

[236]

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 ]

[237]

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 ]

[238]

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

[239]

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 ]

[240]

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 [241].
[ bib ]

[241]

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.

[242]

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 
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Keywords: irace

[243]

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 ]

[244]

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.
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[245]

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.
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[246]

Christian Blum and Manuel LópezIbáñez.
Ant Colony Optimization.
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CRC Press, second edition, 2011.
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[247]

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.
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[248]

C. Blum and D. Merkle, editors.
Swarm Intelligence–Introduction and Applications.
Natural Computing Series. Springer Verlag, Berlin, Germany, 2008.
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[249]

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 
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Keywords: irace

[250]

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.
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[251]

Christian Blum and Günther R. Raidl.
Hybrid Metaheuristics—Powerful Tools for Optimization.
Artificial Intelligence: Foundations, Theory, and Algorithms.
Springer, Springer, Berlin, Germany, 2016.
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[252]

Christian Blum and Andrea Roli.
Metaheuristics in Combinatorial Optimization: Overview and
Conceptual Comparison.
ACM Computing Surveys, 35(3):268–308, 2003.
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[253]

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 ]

[254]

Christian Blum and M. Sampels.
An Ant Colony Optimization Algorithm for Shop Scheduling
Problems.
Journal of Mathematical Modelling and Algorithms,
3(3):285–308, 2004.
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[255]

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.
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[256]

Christian Blum, M. Yábar Vallès, and María J. Blesa.
An ant colony optimization algorithm for DNA sequencing by
hybridization.
Computers & Operations Research, 35(11):3620–3635, 2008.
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[257]

K. D. Boese.
Models for Iterative Global Optimization.
PhD thesis, University of California, Computer Science Department,
Los Angeles, CA, 1996.
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Marko Bohanec.
Decision making: a computerscience and informationtechnology
viewpoint.
Interdisciplinary Description of Complex Systems, 7(2):22–37,
2009.
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Ihor O. Bohachevsky, Mark E. Johnson, and Myron L. Stein.
Generalized Simulated Annealing for Function Optimization.
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[260]

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, 2 edition, 2005.
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[262]

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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[263]

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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Allan Borodin and Ran ElYaniv.
Online computation and competitive analysis.
Cambridge University Press, New York, NY, 1998.
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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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Hozefa M. Botee and Eric Bonabeau.
Evolving Ant Colony Optimization.
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Marco Botte and Anita Schöbel.
Dominance for multiobjective robust optimization concepts.
European Journal of Operational Research, 273(2):430–440,
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[268]

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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[269]

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

[271]

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
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Water Works Association.
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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.
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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

[278]

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

[279]

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,
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[280]

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.
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Jürgen Branke, T. Kaussler, and H. Schmeck.
Guidance in evolutionary multiobjective optimization.
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[282]

JeanPierre Brans and Bertrand Mareschal.
PROMETHEEGAIA. Une méthode d'aide à la décision
en présence de critères multiples.
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JeanPierre Brans and Bertrand Mareschal.
PROMETHEE Methods.
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[284]

Jürgen Branke, S. Nguyen, C. W. Pickardt, and M. Zhang.
Automated Design of Production Scheduling Heuristics: A Review.
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Jürgen Branke, C. Schmidt, and H. Schmeck.
Efficient fitness estimation in noisy environments.
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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.
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[287]

Jürgen Branke, Salvatore Corrente, Salvatore Greco, Roman Slowiński,
and P. Zielniewicz.
Using Choquet integral as preference model in interactive
evolutionary multiobjective optimization.
Technical report, WBS, University of Warwick, 2014.
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[288]

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,
2016.
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[289]

Jürgen Branke and Jawad Elomari.
Simultaneous tuning of metaheuristic parameters for various
computing budgets.
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ACM Press, New York, NY, 2011.
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Keywords: metaoptimization, offline parameter optimization

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Jürgen Branke and Jawad Elomari.
Racing with a Fixed Budget and a SelfAdaptive Significance
Level.
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Intelligent Optimization, 7th International Conference, LION 7, volume 7997
of Lecture Notes in Computer Science. Springer, Heidelberg, Germany,
2013.
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[291]

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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[292]

Jürgen Branke, Salvatore Greco, Roman Slowiński, and Piotr
Zielniewicz.
Learning Value Functions in Interactive Evolutionary
Multiobjective Optimization.
IEEE Transactions on Evolutionary Computation, 19(1):88–102,
2015.
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[293]

Yaochu Jin and Jürgen Branke.
Evolutionary Optimization in Uncertain Environments—A Survey.
IEEE Transactions on Evolutionary Computation, 9(5):303–317,
2005.
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Mátyás Brendel and Marc Schoenauer.
LearnandOptimize: A Parameter Tuning Framework for
Evolutionary AI Planning.
In J.K. Hao, P. Legrand, P. Collet, N. Monmarché, E. Lutton, and
M. Schoenauer, editors, Artificial Evolution: 10th International
Conference, Evolution Artificielle, EA, 2011, volume 7401 of Lecture
Notes in Computer Science, pages 145–155. Springer, Heidelberg, Germany,
2012.
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Mátyás Brendel and Marc Schoenauer.
Instancebased Parameter Tuning for Evolutionary AI Planning.
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pages 591–598, New York, NY, 2011. ACM Press.
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[296]

Leo Breiman.
Random Forests.
Machine Learning, 45(1):5–32, 2001.
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[297]

Karl Bringmann and Tobias Friedrich.
Approximating the Least Hypervolume Contributor: NPHard in
General, But Fast in Practice.
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 6–20.
Springer, Heidelberg, Germany, 2009.
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[298]

Karl Bringmann and Tobias Friedrich.
The Maximum Hypervolume Set Yields Nearoptimal Approximation.
In M. Pelikan and J. Branke, editors, Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2010, pages 511–518. ACM
Press, New York, NY, 2010.
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[299]

Karl Bringmann and Tobias Friedrich.
Convergence of HypervolumeBased Archiving Algorithms I:
Effectiveness.
In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2011, pages 745–752.
ACM Press, New York, NY, 2011.
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Karl Bringmann and Tobias Friedrich.
Convergence of HypervolumeBased Archiving Algorithms II:
Competitiveness.
In T. Soule and J. H. Moore, editors, Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2012, pages 457–464. ACM
Press, New York, NY, 2012.
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[301]

Karl Bringmann and Tobias Friedrich.
Don't be greedy when calculating hypervolume contributions.
In I. I. Garibay, T. Jansen, R. P. Wiegand, and A. S. Wu, editors,
Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic
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Karl Bringmann, Tobias Friedrich, Frank Neumann, and Markus Wagner.
Approximationguided Evolutionary Multiobjective Optimization.
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International Joint Conference on Artificial Intelligence (IJCAI11), pages
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Dimo Brockhoff, Johannes Bader, Lothar Thiele, and Eckart Zitzler.
Directed Multiobjective Optimization Based on the Weighted
Hypervolume Indicator.
Journal of MultiCriteria Decision Analysis, 20(56):291–317,
2013.
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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

[304]

Dimo Brockhoff, Roberto Calandra, Manuel LópezIbáñez, Frank
Neumann, and Selvakumar Ulaganathan.
Metamodeling for (interactive) multiobjective optimization
(WG5).
In K. Klamroth, J. D. Knowles, G. Rudolph, and M. M. Wiecek, editors,
Personalized Multiobjective Optimization: An Analytics Perspective
(Dagstuhl Seminar 18031), volume 8(1) of Dagstuhl Reports, pages
85–94. Schloss Dagstuhl–LeibnizZentrum für Informatik, Germany, 2018.
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Keywords: multiple criteria decision making, evolutionary
multiobjective optimization

[305]

Dimo Brockhoff, Manuel LópezIbáñez, Boris Naujoks, and Günther
Rudolph.
Runtime Analysis of Simple Interactive Evolutionary Biobjective
Optimization Algorithms.
In C. A. Coello Coello et al., editors, Parallel Problem
Solving from Nature, PPSN XII, volume 7491 of Lecture Notes in Computer
Science, pages 123–132. Springer, Heidelberg, Germany, 2012.
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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
EMOAs may dramatically worsen if the utility function is
nonlinear instead of linear.

[306]

Peter Brucker, Johann Hurink, and Frank Werner.
Improving Local Search Heuristics for some Scheduling Problems
— Part I.
Discrete Applied Mathematics, 65(1–3):97–122, 1996.
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Peter Brucker, Johann Hurink, and Frank Werner.
Improving Local Search Heuristics for some Scheduling Problems
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M. J. Brusco, L. W. Jacobs, and G. M. Thompson.
A Morphing Procedure to Supplement a Simulated Annealing
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Artur Brum and Marcus Ritt.
Automatic Design of Heuristics for Minimizing the Makespan in
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Artur Brum and Marcus Ritt.
Automatic Algorithm Configuration for the Permutation Flow Shop
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John T. Buchanan.
An experimental evaluation of interactive MCDM methods and the
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Lecture Notes in Computer Science, pages 24–35. Springer, Heidelberg,
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B. Bullnheimer, Richard F. Hartl, and Christine Strauss.
An Improved Ant System Algorithm for the Vehicle Routing
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Annals of Operations Research, 89:319–328, 1999.
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B. Bullnheimer, Richard F. Hartl, and Christine Strauss.
A new rankbased version of the Ant System: A
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Central European Journal for Operations Research and Economics,
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Edmund K. Burke and Yuri Bykov.
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Edmund K. Burke and Yuri Bykov.
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Luciana Buriol, Paulo M. França, and Pablo Moscato.
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Edmund K. Burke, Michel Gendreau, Matthew R. Hyde, Graham Kendall, Gabriela
Ochoa, Ender Özcan, and Rong Qu.
Hyperheuristics: A Survey of the State of the Art.
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Edmund K. Burke, Matthew R. Hyde, Graham Kendall, and John R. Woodward.
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[321]

Edmund K. Burke, Matthew R. Hyde, Graham Kendall, and John R. Woodward.
A Genetic Programming HyperHeuristic Approach for Evolving 2D
Strip Packing Heuristics.
IEEE Transactions on Evolutionary Computation, 14(6):942–958,
2010.
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[322]

Edmund K. Burke, Matthew R. Hyde, and Graham Kendall.
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[323]

Edmund K. Burke, Matthew R. Hyde, Graham Kendall, Gabriela Ochoa, Ender
Özcan, and John R. Woodward.
A Classification of HyperHeuristic Approaches: Revisited.
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Keywords: 2exchange delta evaluation for QAP

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Evolutionary algorithms are widely used for solving
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In its current state, evolutionary multiobjective
optimization (EMO) is an established field of research and
application with more than 150 PhD theses, more than ten
dedicated texts and edited books, commercial softwares and
numerous freely downloadable codes, a biannual conference
series running successfully since 2001, special sessions and
workshops held at all major evolutionary computing
conferences, and 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
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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.
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Kalyanmoy Deb and Murat Köksalan.
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Kalyanmoy Deb and Christie Myburgh.
Breaking the billionvariable barrier in realworld optimization
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Kalyanmoy Deb and Ankur Sinha.
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Kalyanmoy Deb, J. Sundar, N. Udaya Bhaskara Rao, and Shamik Chaudhuri.
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Kalyanmoy Deb, Rahul Tewari, Mayur Dixit, and Joydeep Dutta.
Finding tradeoff solutions close to KKT points using
evolutionary multiobjective optimization.
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Scalable Test Problems for Evolutionary MultiObjective
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Do not cite this TR! It is incorrect and it is superseeded
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Keywords: DTLZ benchmark

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Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler.
Scalable Test Problems for Evolutionary Multiobjective
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Keywords: DTLZ benchmark

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Kalyanmoy Deb, Ling Zhu, and Sandeep Kulkarni.
Handling Multiple Scenarios in Evolutionary MultiObjective
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IEEE Transactions on Evolutionary Computation, 22(6):920–933,
2018.
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Solutions to most practical numerical optimization problems
must be evaluated for their performance over a number of
different loading or operating conditions, which we refer
here as scenarios. Therefore, a meaningful and resilient
optimal solution must be such that it remains feasible under
all scenarios and performs close to an individual optimal
solution corresponding to each scenario. Despite its
practical importance, 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

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positive supports has grown by over 22% between 1990 and
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biomedical disciplines. The United States had published, over
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The customer order scheduling problem (COSP) is defined as
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customers who order several types of products produced on a
single machine. A setup is required whenever a product type
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minimize the average customer order flow time. Since the
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In M. L. Soffa and E. Duesterwald, editors, Proceedings of the
6th Annual IEEE/ACM International Symposium on Code Generation and
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Stela Pudar Hozo, Benjamin Djulbegovic, and Iztok Hozo.
Estimating the mean and variance from the median, range, and the
size of a sample.
BMC Medical Research Methodology, 5(1):13, 2005.
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T. C. Hu, A. B. Kahng, and C.W. A. Tsao.
Old Bachelor Acceptance: A New Class of NonMonotone Threshold
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Wenbin Hu, Huan Wang, Zhenyu Qiu, Cong Nie, and Liping Yan.
A quantum particle swarm optimization driven urban traffic light
scheduling model.
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Keywords: BML,Optimization,Simulation,Traffic congestion,Updating
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[891]

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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[892]

Han Huang, Xiaowei Yang, Zhifeng Hao, and Ruichu Cai.
A Novel ACO Algorithm with Adaptive Parameter.
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Conference on Computational Science (3), volume 4115 of Lecture Notes
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K. Huang, C. Yang, and K. Tseng.
Fast algorithms for finding the common subsequences of multiple
sequences.
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S. Huband, P. Hingston, L. Barone, and L. While.
A review of multiobjective test problems and a scalable test
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B. Huberman, R. Lukose, and T. Hogg.
An Economic Approach to Hard Computational Problems.
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D. L. HuertaMuñoz, R. Z. RíosMercado, and Rubén Ruiz.
An Iterated Greedy Heuristic for a Market Segmentation Problem
with Multiple Attributes.
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[897]

Evan J. Hughes.
Multiple single objective Pareto sampling.
In Proceedings of the 2003 Congress on Evolutionary Computation
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[898]

Evan J. Hughes.
MSOPSII: A generalpurpose manyobjective optimiser.
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[899]

Evan J. Hughes.
Manyobjective directed evolutionary line search.
In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2011, pages 761–768.
ACM Press, New York, NY, 2011.
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[900]

Jérémie Humeau, Arnaud Liefooghe, ElGhazali Talbi, and Sébastien
Verel.
ParadisEOMO: From Fitness Landscape Analysis to Efficient
Local Search Algorithms.
Journal of Heuristics, 19(6):881–915, June 2013.
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[901]

Ying Hung, V. Roshan Joseph, and Shreyes N. Melkote.
Design and Analysis of Computer Experiments With Branching and
Nested Factors.
Technometrics, 51(4):354–365, 2009.
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[902]

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
Decision Support and Recommender systems, pages 17–20, London, UK,
November, 21–22 2019. Alan Turing Institute.
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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.

[903]

M. Hurtgen and J.C. Maun.
Optimal PMU placement using Iterated Local Search.
International Journal of Electrical Power & Energy Systems,
32(8):857–860, 2010.
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[904]

S. H. Hurlbert.
Pseudoreplication and the Design of Ecological Field
Experiments.
Ecological Monographs, 54(2):187–211, 1984.
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[905]

Mohamed Saifullah Hussin and Thomas Stützle.
Hierarchical Iterated Local Search for the Quadratic Assignment
Problem.
In M. J. Blesa, C. Blum, L. Di Gaspero, A. Roli, M. Sampels, and
A. Schaerf, editors, Hybrid Metaheuristics, volume 5818 of Lecture
Notes in Computer Science, pages 115–129. Springer, Heidelberg, Germany,
2009.
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[906]

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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[907]

Frank Hutter, Domagoj Babić, Holger H. Hoos, and Alan J. Hu.
Boosting Verification by Automatic Tuning of Decision
Procedures.
In J. Baumgartner and M. Sheeran, editors, FMCAD'07: Proceedings
of the 7th International Conference Formal Methods in Computer Aided Design,
pages 27–34, Austin, Texas, USA, 2007. IEEE Computer Society, Washington,
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[908]

Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Kevin P. Murphy.
An experimental investigation of modelbased parameter
optimisation: SPO and beyond.
In F. Rothlauf, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2009, pages 271–278. ACM Press,
New York, NY, 2009.
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[909]

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,
Germany, 2010.
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[910]

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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[911]

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.
[ bib ]
Keywords: SMAC,ROAR

[912]

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,
Heidelberg, Germany, 2012.
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[913]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Bayesian Optimization With Censored Response Data.
Arxiv preprint arXiv:1310.1947, 2013.
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[914]

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
Intelligent Optimization, 7th International Conference, LION 7, volume 7997
of Lecture Notes in Computer Science, pages 364–381. Springer,
Heidelberg, Germany, 2013.
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Keywords: parameter importance

[915]

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 31th
International Conference on Machine Learning, volume 32, pages 754–762,
2014.
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Keywords: fANOVA, parameter importance

[916]

Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Kevin Murphy.
TimeBounded Sequential Parameter Optimization.
In C. Blum and R. Battiti, editors, Learning and Intelligent
Optimization, 4th International Conference, LION 4, volume 6073 of
Lecture Notes in Computer Science, pages 281–298. Springer, Heidelberg,
Germany, 2010.
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[917]

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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[918]

Frank Hutter, Holger H. Hoos, and Thomas Stützle.
Automatic Algorithm Configuration Based on Local Search.
In R. C. Holte and A. Howe, editors, Proc. of the TwentySecond
Conference on Artifical Intelligence (AAAI '07), pages 1152–1157. AAAI
Press/MIT Press, Menlo Park, CA, 2007.
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[919]

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.
[ bib ]

[920]

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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[921]

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
Computer Science, pages 36–40. Springer, Heidelberg, Germany, 2014.
[ bib 
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pdf ]

[922]

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

[923]

Frank Hutter.
Automated Configuration of Algorithms for Solving Hard
Computational Problems.
PhD thesis, University of British Columbia, Department of Computer
Science, Vancouver, Canada, October 2009.
[ bib ]

[924]

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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[925]

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.
[ bib ]

[926]

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.
[ bib ]

[927]

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 ]

[928]

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 [1229].
[ bib ]

[929]

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 [1238].
[ bib ]

[930]

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 [555].
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[931]

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 [558].
[ bib 
http ]

[932]

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 [1800].
[ bib ]

[933]

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 [561].
[ bib 
http ]

[934]

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 ]

[935]

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 [560].
[ bib 
http ]

[936]

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 [1221].
[ bib 
http ]

[937]

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 
http ]

[938]

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 [1245].
[ bib 
http ]

[939]

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.
[ bib ]

[940]

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 ]

[941]

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 [1246].
[ bib ]

[942]

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 [1590].
[ bib ]

[943]

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 ]

[944]

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 ]

[945]

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 ]

[946]

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 ]

[947]

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 ]

[948]

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 ]

[949]

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 [200].
[ bib 
http ]

[950]

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 ]

[951]

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 [196].
[ bib 
http ]

[952]

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 ]

[953]

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

[954]

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 ]

[955]

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

[956]

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.

[957]

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

[958]

Christian Igel, V. HeidrichMeisner, and T. Glasmachers.
Shark.
Journal of Machine Learning Research, 9:993–996, June 2008.
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[959]

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 ]

[960]

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.

[961]

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

[962]

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 ]

[963]

Alfred Inselberg.
The Plane with Parallel Coordinates.
The visual computer, 1(2):69–91, 1985.
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[964]

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 ]

[965]

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

[966]

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 ]

[967]

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 ]

[968]

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.
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[969]

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 ]

[970]

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+

[971]

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 ]

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structure of the electricity tariff, the
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Joshua D. Knowles.
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Joshua D. Knowles.
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Joshua D. Knowles.
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Joshua D. Knowles and David Corne.
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Joshua D. Knowles and David Corne.
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Joshua D. Knowles and David Corne.
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Joshua D. Knowles and David Corne.
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Joshua D. Knowles and David Corne.
Properties of an Adaptive Archiving Algorithm for Storing
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Proposed to use Smetric (hypervolume metric) for
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Joshua D. Knowles and David Corne.
Bounded Pareto Archiving: Theory and Practice.
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Joshua D. Knowles, David Corne, and Mark Fleischer.
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TIKReport 214, Computer Engineering and Networks Laboratory (TIK),
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Management Science, 52(4):514–528, 2006.
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[1160]

R. J. Lempert, S. Popper, and Steven C. Bankes.
Shaping the Next One Hundred Years: New Methods for
Quantitative, Long Term Policy Analysis.
RAND, 2003.
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[1161]

C. Leon, S. Martin, J. M. Elena, and J. Luque.
EXPLORE: Hybrid expert system for water networks management.
Journal of Water Resources Planning and Management, ASCE,
126(2):65–74, 2000.
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[1162]

L. Lessing, I. Dumitrescu, and Thomas Stützle.
A Comparison Between ACO Algorithms for the Set Covering
Problem.
In M. Dorigo et al., editors, Ant Colony Optimization and Swarm
Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of
Lecture Notes in Computer Science, pages 1–12. Springer, Heidelberg,
Germany, 2004.
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[1163]

R. M. R. Lewis.
A Guide to Graph Colouring: Algorithms and Applications.
Springer, Cham, 2016.
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Supplementary material available at [1164]

[1164]

Rhyd M. R. Lewis.
Suite of Graph Colouring Algorithms – Supplementary Material to
the Book “A Guide to Graph Colouring: Algorithms and Applications”.
http://rhydlewis.eu/resources/gCol.zip, 2016.
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[1165]

Kevin LeytonBrown, Eugene Nudelman, and Yoav Shoham.
Learning the Empirical Hardness of Optimization Problems: The
Case of Combinatorial Auctions.
In P. van Hentenryck, editor, Principles and Practice of
Constraint Programming, CP 2002, Lecture Notes in Computer Science, pages
556–572. Springer, Heidelberg, Germany, 2002.
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[1166]

Kevin LeytonBrown, M. Pearson, and Y. Shoham.
Towards a Universal Test Suite for Combinatorial Auction
Algorithms.
In A. Jhingran et al., editors, ACM Conference on Electronic
Commerce (EC00), pages 66–76. ACM Press, New York, NY, 2000.
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CPLEXregions200 benchmark set,
http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/results.html

[1167]

Jianjun David Li.
A twostep rejection procedure for testing multiple hypotheses.
Journal of Statistical Planning and Inference,
138(6):1521–1527, 2008.
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[1168]

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

[1169]

Xiaoping Li, Long Chen, Haiyan Xu, and Jatinder N.D. Gupta.
Trajectory Scheduling Methods for Minimizing Total Tardiness in
a Flowshop.
Operations Research Perspectives, 2:13–23, 2015.
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[1170]

Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet
Talwalkar.
Hyperband: A Novel BanditBased Approach to Hyperparameter
Optimization.
Journal of Machine Learning Research, 18(185):1–52, 2018.
[ bib 
http ]
Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and earlystopping. We formulate hyperparameter optimization as a pureexploration nonstochastic infinitearmed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, our algorithm , for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare our algorithm with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that our algorithm can provide over an orderofmagnitude speedup over our competitor set on a variety of deeplearning and kernelbased learning problems.
Keywords: racing

[1171]

Y. Li and W. Li.
Adaptive Ant Colony Optimization Algorithm Based on Information
Entropy: Foundation and Application.
Fundamenta Informaticae, 77(3):229–242, 2007.
[ bib ]

[1172]

Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao.
An Improved Two Archive Algorithm for ManyObjective
Optimization.
In Proceedings of the 2014 Congress on Evolutionary Computation
(CEC 2014), pages 2869–2876, Piscataway, NJ, 2014. IEEE Press.
[ bib ]

[1173]

Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao.
ManyObjective Evolutionary Algorithms: A Survey.
ACM Computing Surveys, 48(1):1–35, 2015.
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[1174]

Z. Li, Y. Wang, J. Yu, Y. Zhang, and X. Li.
A Novel CloudBased Fuzzy SelfAdaptive Ant Colony System.
In ICNC'08: Proceedings of the 2008 Fourth International
Conference on Natural Computation, volume 7, pages 460–465, Washington, DC,
2008. IEEE Computer Society.
[ bib ]

[1175]

Miqing Li, Shengxiang Yang, Xiaohui Liu, and Ruimin Shen.
A Comparative Study on Evolutionary Algorithms for
ManyObjective Optimization.
In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and
J. Shaw, editors, Evolutionary Multicriterion Optimization, EMO 2013,
volume 7811 of Lecture Notes in Computer Science, pages 261–275.
Springer, Heidelberg, Germany, 2013.
[ bib ]

[1176]

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 ]

[1177]

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 ]

[1178]

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 ]

[1179]

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 ]

[1180]

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 ]

[1181]

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 ]

[1182]

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

[1183]

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

[1184]

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 ]

[1185]

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 ]

[1186]

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 ]

[1187]

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

[1188]

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 ]

[1189]

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 
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[1190]

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.

[1191]

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 ]

[1192]

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 
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pdf ]

[1193]

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 ]

[1194]

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 ]

[1195]

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

[1196]

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 ]

[1197]

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

[1198]

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 ]

[1199]

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

[1200]

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.

[1201]

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 ]

[1202]

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.
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[1203]

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 
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[1204]

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

[1205]

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 ]

[1206]

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 ]

[1207]

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 ]

[1208]

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 ]

[1209]

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 ]

[1210]

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 ]

[1211]

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 [1214].
[ bib ]

[1212]

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 ]

[1213]

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 ]

[1214]

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

[1215]

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 ]

[1216]

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 ]

[1217]

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

[1218]

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 [1216].
[ bib 
http ]

[1219]

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

[1220]

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.

[1221]

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

[1222]

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 ]

[1223]

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

[1224]

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 ]

[1225]

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

[1226]

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 ]

[1227]

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 [1228].
[ bib ]

[1228]

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.

[1229]

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.

[1230]

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” [1229].
[ bib ]
Please cite the book chapter, not this.

[1231]

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 ]

[1232]

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 ]

[1233]

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.

[1234]

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.

[1235]

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 ]

[1236]

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 ]

[1237]

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 ]

[1238]

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 ]

[1239]

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.

[1240]

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.

[1241]

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 [1240].
[ bib ]

[1242]

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

[1243]

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 ]

[1244]

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 ]

[1245]

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.

[1246]

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.

[1247]

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 ]

[1248]

Eunice LÃ³pezCamacho, 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 ]

[1249]

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

[1250]

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

[1251]

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 ]

[1252]

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 ]

[1253]

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 ]

[1254]

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 ]

[1255]

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, 2 edition, 2010.
[ bib 
DOI ]

[1256]

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 ]

[1257]

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 ]

[1258]

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

[1259]

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

[1260]

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 ]

[1261]

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 ]

[1262]

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 
DOI ]

[1263]

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

[1264]

T. 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.

[1265]

T. 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 ]

[1266]

T. Lust and Jacques Teghem.
The multiobjective multidimensional knapsack problem: a survey
and a new approach.
Arxiv preprint arXiv:1007.4063, 2010.
[ bib ]
Published as [1267]

[1267]

T. 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 
DOI ]

[1268]

T. 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

[1269]

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 ]

[1270]

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

[1271]

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

[1272]

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 ]

[1273]

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

[1274]

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

[1275]

Guilherme B. Mainieri and Débora P. Ronconi.
New heuristics for total tardiness minimization in a flexible
flowshop.
Optimization Letters, pages 1–20, 2012.
[ bib ]

[1276]

D. R. Broad, Graeme C. Dandy, and Holger R. Maier.
A Metamodeling Approach to Water Distribution System
Optimization.
In 6th Annual Symposium on Water Distribution Systems Analysis.
ASCE, June 2004.
[ bib ]

[1277]

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.
[ bib ]

[1278]

Yuri Malitsky and Meinolf Sellmann.
Instancespecific algorithm configuration as a method for
nonmodelbased portfolio generation.
In N. Beldiceanu, N. Jussien, and E. Pinson, editors,
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.
[ bib ]

[1279]

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

[1280]

Yuri Malitsky, Deepak Mehta, Barry Oâ€™Sullivan, and Helmut Simonis.
Tuning parameters of large neighborhood search for the machine
reassignment problem.
In G. C. and S. M., 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.
[ bib 
DOI ]

[1281]

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

[1282]

Vittorio Maniezzo, M. Boschetti, and M. Jelasity.
An Ant Approach to Membership Overlay Design.
In M. Dorigo et al., editors, Ant Colony Optimization and Swarm
Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of
Lecture Notes in Computer Science, pages 37–48. Springer, Heidelberg,
Germany, 2004.
[ bib ]

[1283]

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

[1284]

Vittorio Maniezzo and Alberto Colorni.
The Ant System Applied to the Quadratic Assignment Problem.
IEEE Transactions on Knowledge and Data Engineering,
11(5):769–778, 1999.
[ bib ]

[1285]

Vittorio Maniezzo and M. Milandri.
An AntBased Framework for Very Strongly Constrained Problems.
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encompass the total operational time horizon,
include: the scheduling of the pumping units,
settings of the water distribution system control
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booster chlorination stations. The constraints are
domain heads and chlorine concentrations at the
consumer nodes, maximum injection rates at the
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Luís Paquete.
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Universidades (Multiobjective Evolutionary Algorithms for Resource
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Master's thesis, University of Algarve, 2001.
In Portuguese.
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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
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the solution cost during the runs is analysed by
means of attainment functions, as well.

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Luís Paquete.
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[1503]

Luís Paquete, Marco Chiarandini, and Thomas Stützle.
Pareto Local Optimum Sets in the Biobjective Traveling
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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

[1504]

Luís Paquete, Carlos M. Fonseca, and Manuel LópezIbáñez.
An optimal algorithm for a special case of Klee's measure
problem in three dimensions.
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2006.
Superseded by paper in IEEE Transactions on Evolutionary
Computation [172].
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The measure of the region dominated by (the maxima
of) a set of n points in the positive dorthant
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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

[1505]

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

[1506]

Luís Paquete and Thomas Stützle.
A study of stochastic local search algorithms for the
biobjective QAP with correlated flow matrices.
European Journal of Operational Research, 169(3):943–959,
2006.
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Luís Paquete and Thomas Stützle.
Clusters of nondominated solutions in multiobjective
combinatorial optimization: An experimental analysis.
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Luís Paquete and Thomas Stützle.
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Computers & Operations Research, 36(9):2619–2631, 2009.
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Luís Paquete and Thomas Stützle.
An Experimental Investigation of Iterated Local Search for
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Luís Paquete and Thomas Stützle.
A TwoPhase Local Search for the Biobjective Traveling Salesman
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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 
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Effective Stochastic Local Search (SLS) algorithms
can be seen as being composed of several algorithmic
components, each of which plays some specific role
with respect to overall performance. In this
article, we explore the application of experimental
design techniques to analyze the effect of different
choices for these algorithmic components on SLS
algorithms applied to Multiobjective Combinatorial
Optimization Problems that are solved in terms of
Pareto optimality. This analysis is done using the
example application of SLS algorithms to the
biobjective Quadratic Assignment Problem and we show
also that the same choices for algorithmic
components can lead to different behavior in
dependence of various instance features, such as the
structure of input data and the correlation between
objectives.

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

[1513]

Luís Paquete, Thomas Stützle, and Manuel LópezIbáñez.
Using experimental design to analyze stochastic local search
algorithms for multiobjective problems.
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Hartl, and M. Reimann, editors, Metaheuristics: Progress in Complex
Systems Optimization, volume 39 of Operations Research / Computer
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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
Metaheuristics International Conference (MIC 2005)

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Lucas Marcondes Pavelski, Myriam Regattieri Delgado, and MarieEléonore
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Paola Pellegrini and Mauro Birattari.
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Paola Pellegrini, L. Castelli, and R. Pesenti.
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Paola Pellegrini, D. Favaretto, and E. Moretti.
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Paola Pellegrini, Franco Mascia, Thomas Stützle, and Mauro Birattari.
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Paola Pellegrini, Thomas Stützle, and Mauro Birattari.
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Puca Huachi Vaz Penna, Anand Subramanian, and Luiz Satoru Ochi.
An Iterated Local Search Heuristic for the Heterogeneous Fleet
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Leslie Pérez Cáceres, Bernd Bischl, and Thomas Stützle.
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Leslie Pérez Cáceres, Manuel LópezIbáñez, Holger H. Hoos,
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An experimental study of adaptive capping in irace.
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Leslie Pérez Cáceres, Manuel LópezIbáñez, Holger H. Hoos,
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Leslie Pérez Cáceres, Manuel LópezIbáñez, and Thomas
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Ant Colony Optimization on a Budget of 1000.
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Leslie Pérez Cáceres, Manuel LópezIbáñez, and Thomas
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Leslie Pérez Cáceres, Federico Pagnozzi, Alberto Franzin, and Thomas
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[ bib ]
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
and O3 optimization flags is possible.

[1543]

Leslie Pérez Cáceres, Federico Pagnozzi, Alberto Franzin, and Thomas
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Automatic configuration of GCC using irace: Supplementary
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Pedro Pinto, Thomas Runkler, and João Sousa.
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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
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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
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computational time.
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The problem of connecting a set of client nodes
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cost tree network, subject to capacity constraints
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data;traffic light programming;traffic
microsimulation;traffic signal optimization;urban traffic
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Operation of pumping stations represents high costs
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costs through an optimal pump scheduling becomes an
important issue. This work presents the use of
Multiobjective Evolutionary Algorithms (MOEAs) to
solve an optimal pumpscheduling problem. For the
first time, six different approaches were
implemented and compared. These algorithms aim to
minimise four objectives: electric energy cost,
pumps' maintenance cost, maximum power peak, and
level variation in the reservoir. In order to
consider hydraulic and technical constrains, a
heuristic constrain algorithm was developed and
combined with each MOEA utilised. Evaluation of
experimental results of a set of metrics shows that
the Strength Pareto Evolutionary Algorithm (SPEA)
achieves the best performance for this
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pumping station operation engineers with a wide
range of optimal pump schedules to chose from.

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Abdelghani Souilah.
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Arno Sprecher, Sönke Hartmann, and Andreas Drexl.
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Keywords: branchandbound, multimode resourceconstrained
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Arno Sprecher, Rainer Kolisch, and Andreas Drexl.
Semiactive, active, and nondelay schedules for the
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We consider the resourceconstrained project
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is on a formal definition of semiactive, active,
and nondelay schedules. Traditionally these
schedules establish basic concepts within the job
shop scheduling literature. There they are usually
defined in a rather informal way which does not
create any substantial problems. Using these
concepts in the more general RCPSP without giving
a formal definition may cause serious
problems. After providing a formal definition of
semiactive, active, and nondelay schedules for the
RCPSP we outline some of these problems occurring
within the disjunctive arc concept.
Keywords: active schedules, Branchandbound methods,
nondelay schedules, Resourceconstrained project
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T. J. Stewart.
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Fernando Stefanello, Vaneet Aggarwal, Luciana Salete Buriol, José Fernando
Gonçalves, and Mauricio G. C. Resende.
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Daniel H. Stolfi and Enrique Alba.
Red Swarm: Reducing travel times in smart cities by using
bioinspired algorithms.
Applied Soft Computing, 24:181–195, 2014.
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This article presents an innovative approach to solve one of
the most relevant problems related to smart mobility: the
reduction of vehicles' travel time. Our original approach,
called Red Swarm, suggests a potentially customized route to
each vehicle by using several spots located at traffic lights
in order to avoid traffic jams by using {V2I}
communications. That is quite different from other existing
proposals, as it deals with real maps and actual streets, as
well as several road traffic distributions. We propose an
evolutionary algorithm (later efficiently parallelized) to
optimize our case studies which have been imported from
OpenStreetMap into {SUMO} as they belong to a real city. We
have also developed a Rerouting Algorithm which accesses the
configuration of the Red Swarm and communicates the route
chosen to vehicles, using the spots (via WiFi
link). Moreover, we have developed three competing algorithms
in order to compare their results to those of Red Swarm and
have observed that Red Swarm not only achieved the best
results, but also outperformed the experts' solutions in a
total of 60 scenarios tested, with up to 19% shorter travel
times.
Keywords: Evolutionary algorithm,Road traffic,Smart city,Smart
mobility,Traffic light,WiFi connections

[1779]

Rainer Storn and Kenneth Price.
Differential Evolution – A Simple and Efficient Heuristic for
Global Optimization over Continuous Spaces.
Journal of Global Optimization, 11(4):341–359, 1997.
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[1780]

Daniel H. Stolfi and Enrique Alba.
An Evolutionary Algorithm to Generate Real Urban Traffic Flows.
In J. M. Puerta, J. A. Gámez, B. Dorronsoro, E. Barrenechea,
A. Troncoso, B. Baruque, and M. Galar, editors, Advances in Artificial
Intelligence, CAEPIA 2015, volume 9422 of Lecture Notes in Computer
Science, pages 332–343. Springer, Heidelberg, Germany, 2015.
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In this article we present a strategy based on an evolution
ary algorithm to calculate the real vehicle flows in cities
according to data from sensors placed in the streets. We have
worked with a map imported from OpenStreetMap into the SUMO
traffic simulator so that the resulting scenarios can be used
to perform different optimizations with the confidence of
being able to work with a traffic distribution close to
reality. We have compared the results of our algorithm to
other competitors and achieved results that replicate the
real traffic distribution with a precision higher than
90%.
Keywords: Evolutionary algorithm,SUMO,Smart city,Smart mobility,Traffic
simulation

[1781]

Philip N. Strenski and Scott Kirkpatrick.
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[1782]

Patrycja Strycharczuk, Manuel LópezIbáñez, Georgina Brown, and
Adrian Leemann.
General Northern English: Exploring regional variation in the
North of England with machine learning.
Frontiers in Artificial Intelligence, 2020.
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Keywords: vowels, accent features, dialect leveling, Random forest
(bagging), Feature selecion

[1783]

Thomas Stützle.
Iterated Local Search for the Quadratic Assignment Problem.
European Journal of Operational Research, 174(3):1519–1539,
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[1784]

Thomas Stützle.
Applying Iterated Local Search to the Permutation Flow Shop
Problem.
Technical Report AIDA–98–04, FG Intellektik, FB Informatik, TU
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[1785]

Thomas Stützle.
ACOTSP: A Software Package of Various Ant
Colony Optimization Algorithms Applied to the Symmetric Traveling Salesman
Problem, 2002.
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http://www.acometaheuristic.org/acocode/

[1786]

Thomas Stützle.
Some Thoughts on Engineering Stochastic Local Search
Algorithms.
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pages 47–52, 2009.
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[1787]

Thomas Stützle.
MaxMin Ant System for the Quadratic Assignment Problem.
Technical Report AIDA–97–4, FG Intellektik, FB Informatik, TU
Darmstadt, Germany, July 1997.
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[1788]

Thomas Stützle.
An Ant Approach to the Flow Shop Problem.
In Proceedings of the 6th European Congress on Intelligent
Techniques & Soft Computing (EUFIT'98), volume 3, pages 1560–1564.
Verlag Mainz, Aachen, Germany, 1998.
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[1789]

Thomas Stützle and Marco Dorigo.
A Short Convergence Proof for a Class of ACO Algorithms.
IEEE Transactions on Evolutionary Computation, 6(4):358–365,
2002.
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[1790]

Thomas Stützle and Marco Dorigo.
ACO Algorithms for the Quadratic Assignment Problem.
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[1791]

Thomas Stützle and Holger H. Hoos.
Analysing the Runtime Behaviour of Iterated Local Search for
the Travelling Salesman Problem.
In P. Hansen and C. Ribeiro, editors, Essays and Surveys on
Metaheuristics, Operations Research/Computer Science Interfaces Series,
pages 589–611. Kluwer Academic Publishers, Boston, MA, 2001.
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[1792]

Thomas Stützle and Holger H. Hoos.
Improving the Ant System: A Detailed Report on the
MaxMin Ant System.
Technical Report AIDA–96–12, FG Intellektik, FB Informatik, TU
Darmstadt, Germany, August 1996.
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[1793]

Thomas Stützle and Holger H. Hoos.
MaxMin Ant System.
Future Generation Computer Systems, 16(8):889–914, 2000.
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[1794]

Thomas Stützle and Holger H. Hoos.
The MaxMin Ant System and Local Search for the Traveling
Salesman Problem.
In T. Bäck, Z. Michalewicz, and X. Yao, editors, Proceedings
of the 1997 IEEE International Conference on Evolutionary Computation
(ICEC'97), pages 309–314. IEEE Press, Piscataway, NJ, 1997.
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[1795]

Thomas Stützle and Holger H. Hoos.
MaxMin Ant System and Local Search for Combinatorial
Optimization Problems.
In S. Voß, S. Martello, I. H. Osman, and C. Roucairol, editors,
MetaHeuristics: Advances and Trends in Local Search Paradigms for
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Thomas Stützle and Manuel LópezIbáñez.
Automatic (Offline) Configuration of Algorithms.
In J. L. J. Laredo, S. Silva, and A. I. EsparciaAlcázar,
editors, GECCO (Companion), pages 681–702. ACM Press, New York, NY,
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[1797]

Thomas Stützle and Manuel LópezIbáñez.
Automated Offline Design of Algorithms.
In P. A. N. Bosman, editor, GECCO'17 Companion, pages
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[1798]

Thomas Stützle and Manuel LópezIbáñez.
Automated Design of Metaheuristic Algorithms.
In M. Gendreau and J.Y. Potvin, editors, Handbook of
Metaheuristics, volume 272 of International Series in Operations
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[1799]

Thomas Stützle, Manuel LópezIbáñez, and Marco Dorigo.
A Concise Overview of Applications of Ant Colony Optimization.
In J. J. Cochran, editor, Wiley Encyclopedia of Operations
Research and Management Science, volume 2, pages 896–911. John Wiley &
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[1800]

Thomas Stützle, Manuel LópezIbáñez, Paola Pellegrini, Michael
Maur, Marco A. Montes de Oca, Mauro Birattari, and Marco Dorigo.
Parameter Adaptation in Ant Colony Optimization.
In Y. Hamadi, E. Monfroy, and F. Saubion, editors, Autonomous
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[1801]

Thomas Stützle and Rubén Ruiz.
Iterated Greedy.
In R. Martí, P. M. Pardalos, and M. G. C. Resende, editors,
Handbook of Heuristics, pages 1–31. Springer International Publishing,
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[1802]

Thomas Stützle and Rubén Ruiz.
Iterated Local Search.
In R. Martí, P. M. Pardalos, and M. G. C. Resende, editors,
Handbook of Heuristics, pages 1–27. Springer International Publishing,
2018.
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[1803]

Thomas Stützle.
Local Search Algorithms for Combinatorial Problems — Analysis,
Improvements, and New Applications.
PhD thesis, FB Informatik, TU Darmstadt, Germany, 1998.
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[1804]

James Styles and Holger H. Hoos.
Ordered racing protocols for automatically configuring
algorithms for scaling performance.
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[1805]

James Styles, Holger H. Hoos, and Martin Müller.
Automatically Configuring Algorithms for Scaling Performance.
In Y. Hamadi and M. Schoenauer, editors, Learning and
Intelligent Optimization, 6th International Conference, LION 6, volume 7219
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[1806]

Anand Subramanian and Maria Battarra.
An Iterated Local Search Algorithm for the Travelling Salesman
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[1807]

Anand Subramanian, Maria Battarra, and Chris N. Potts.
An Iterated Local Search Heuristic for the Single Machine Total
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[1808]

Ponnuthurai N. Suganthan, Nikolaus Hansen, J. J. Liang, Kalyanmoy Deb, Y. P.
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Problem definitions and evaluation criteria for the CEC 2005
special session on realparameter optimization.
Technical report, Nanyang Technological University, Singapore, 2005.
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Also known as KanGAL Report Number 2005005 (Kanpur Genetic Algorithms
Laboratory, IIT Kanpur)
Keywords: CEC'05 benchmark

[1809]

Yanan Sui, Alkis Gotovos, Joel W. Burdick, and Andreas Krause.
Safe Exploration for Optimization with Gaussian Processes.
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We consider sequential decision problems under uncertainty,
where we seek to optimize an unknown function from noisy
samples. This requires balancing exploration (learning about
the objective) and exploitation (localizing the maximum), a
problem wellstudied in the multiarmed bandit literature. In
many applications, however, we require that the sampled
function values exceed some prespecified "safety" threshold,
a requirement that existing algorithms fail to meet. Examples
include medical applications where patient comfort must be
guaranteed, recommender systems aiming to avoid user
dissatisfaction, and robotic control, where one seeks to
avoid controls causing physical harm to the platform. We
tackle this novel, yet rich, set of problems under the
assumption that the unknown function satisfies regularity
conditions expressed via a Gaussian process prior. We develop
an efficient algorithm called SafeOpt, and theoretically
guarantee its convergence to a natural notion of optimum
reachable under safety constraints. We evaluate SafeOpt on
synthetic data, as well as two real applications: movie
recommendation, and therapeutic spinal cord stimulation.
Keywords: SafeOpt

[1810]

Yanan Sui, Vincent Zhuang, Joel W. Burdick, and Yisong Yue.
Stagewise Safe Bayesian Optimization with Gaussian
Processes.
Arxiv preprint arXiv:1806.07555, 2018.
[ bib 
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Enforcing safety is a key aspect of many problems pertaining
to sequential decision making under uncertainty, which
require the decisions made at every step to be both
informative of the optimal decision and also safe. For
example, we value both efficacy and comfort in medical
therapy, and efficiency and safety in robotic control. We
consider this problem of optimizing an unknown utility
function with absolute feedback or preference feedback
subject to unknown safety constraints. We develop an
efficient safe Bayesian optimization algorithm, StageOpt,
that separates safe region expansion and utility function
maximization into two distinct stages. Compared to existing
approaches which interleave between expansion and
optimization, we show that StageOpt is more efficient and
naturally applicable to a broader class of problems. We
provide theoretical guarantees for both the satisfaction of
safety constraints as well as convergence to the optimal
utility value. We evaluate StageOpt on both a variety of
synthetic experiments, as well as in clinical practice. We
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safe optimization approaches, and is able to safely and
effectively optimize spinal cord stimulation therapy in our
clinical experiments.
Keywords: StageOpt

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We present a reinforcement learning approach to explore and
optimize a safetyconstrained Markov Decision
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discounted cumulative reward while constraining the
probability of entering unsafe states, defined using a safety
function being within some tolerance. The safety values of
all states are not known a priori, and we probabilistically
model them via a Gaussian Process (GP) prior. As such,
properly behaving in such an environment requires balancing a
threeway tradeoff of exploring the safety function,
exploring the reward function, and exploiting acquired
knowledge to maximize reward. We propose a novel approach to
balance this tradeoff. Specifically, our approach explores
unvisited states selectively; that is, it prioritizes the
exploration of a state if visiting that state significantly
improves the knowledge on the achievable cumulative
reward. Our approach relies on a novel information gain
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