IRIDIA BibTeX Repository

[1]

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

[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, 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.
[ bib 
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.
[ bib 
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.
[ bib 
DOI ]

[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.
[ bib ]

[75]

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

[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).
[ bib ]

[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.
Using Confidence Bounds for ExploitationExploration
Tradeoffs.
Journal of Machine Learning Research, 3:397–422, November
2002.
[ bib ]
We show how a standard tool from statistics — namely
confidence bounds — can be used to elegantly deal with
situations which exhibit an exploitationexploration
tradeoff. Our technique for designing and analyzing
algorithms for such situations is general and can be applied
when an algorithm has to make exploitationversusexploration
decisions based on uncertain information provided by a random
process. We apply our technique to two models with such an
exploitationexploration tradeoff. For the adversarial
bandit problem with shifting our new algorithm suffers only
O((ST)^{1/2}) regret with high probability over T trials
with S shifts. Such a regret bound was previously known
only in expectation. The second model we consider is
associative reinforcement learning with linear value
functions. For this model our technique improves the regret
from O(T^{3/4}) to O(T^{1/2}).

[79]

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

[80]

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 ]

[81]

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 ]

[82]

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 ]

[83]

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 ]

[84]

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 ]

[85]

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

[86]

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

[87]

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 ]

[88]

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 ]

[89]

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

[90]

Mahdi Aziz and MohammadH. TayaraniN.
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

[91]

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

[92]

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 ]

[93]

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Domagoj Babić.
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Domagoj Babić and Alan J. Hu.
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We consider the optimization of a computer model where each
simulation either fails or returns a valid output
performance. We first propose a new joint Gaussian process
model for classification of the inputs (computation failure
or success) and for regression of the performance
function. We provide results that allow for a computationally
efficient maximum likelihood estimation of the covariance
parameters, with a stochastic approximation of the likelihood
gradient. We then extend the classical improvement criterion
to our setting of joint classification and regression. We
provide an efficient computation procedure for the extended
criterion and its gradient. We prove the almost sure
convergence of the global optimization algorithm following
from this extended criterion. We also study the practical
performances of this algorithm, both on simulated data and on
a real computer model in the context of automotive fan
design.
Keywords: crashed simulation; latent gaussian process; automotive fan
design; industrial application; GP classification; Expected
Feasible Improvement with Gaussian Process Classification
with signs; EFI GPC sign

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Achim Bachem, Barthel Steckemetz, and Michael Wottawa.
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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
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fails for the quadratic assignment problem.
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Prasanna Balaprakash, Mauro Birattari, and Thomas Stützle.
Improvement Strategies for the FRace Algorithm: Sampling
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In T. BartzBeielstein, M. J. Blesa, C. Blum, B. Naujoks, A. Roli,
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[105]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
Adaptive Sampling Size and Importance Sampling in
Estimationbased Local Search for the Probabilistic Traveling Salesman
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Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
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Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
Estimationbased Metaheuristics for the Single Vehicle Routing
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Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Zhi Yuan, and Marco
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Egon Balas and C. Martin.
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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
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Egon Balas and A. Vazacopoulos.
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Steven C. Bankes.
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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
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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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Thomas BartzBeielstein, C. Lasarczyk, and Mike Preuss.
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Thomas BartzBeielstein, C. Lasarczyk, and Mike Preuss.
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Thomas BartzBeielstein and Sandor Markon.
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Dimitri P. Bertsekas, John N. Tsitsiklis, and Cynara Wu.
Rollout Algorithms for Combinatorial Optimization.
Journal of Heuristics, 3(3):245–262, 1997.
[ bib ]

[176]

Matthijs L. den Besten, Thomas Stützle, and Marco Dorigo.
Ant Colony Optimization for the Total Weighted Tardiness
Problem.
In M. Schoenauer et al., editors, Proceedings of PPSNVI, Sixth
International Conference on Parallel Problem Solving from Nature, volume
1917 of Lecture Notes in Computer Science, pages 611–620. Springer,
Heidelberg, Germany, 2000.
[ bib ]

[177]

Matthijs L. den Besten, Thomas Stützle, and Marco Dorigo.
Design of Iterated Local Search Algorithms: An Example
Application to the Single Machine Total Weighted Tardiness Problem.
In E. J. W. Boers et al., editors, Applications of Evolutionary
Computing, Proceedings of EvoWorkshops 2001, volume 2037 of Lecture
Notes in Computer Science, pages 441–452. Springer, Heidelberg, Germany,
2001.
[ bib ]

[178]

Nicola Beume, Carlos M. Fonseca, Manuel LópezIbáñez, Luís
Paquete, and Jan Vahrenhold.
On the complexity of computing the hypervolume indicator.
IEEE Transactions on Evolutionary Computation,
13(5):1075–1082, 2009.
[ bib 
DOI ]
The goal of multiobjective optimization is to find
a set of best compromise solutions for typically
conflicting objectives. Due to the complex nature of
most reallife problems, only an approximation to
such an optimal set can be obtained within
reasonable (computing) time. To compare such
approximations, and thereby the performance of
multiobjective optimizers providing them, unary
quality measures are usually applied. Among these,
the hypervolume indicator (or
Smetric) is of particular relevance due to
its favorable properties. Moreover, this indicator
has been successfully integrated into stochastic
optimizers, such as evolutionary algorithms, where
it serves as a guidance criterion for finding good
approximations to the Pareto front. Recent results
show that computing the hypervolume indicator can be
seen as solving a specialized version of Klee's
Measure Problem. In general, Klee's Measure Problem
can be solved with O(n logn +
n^{d/2}logn) comparisons for an input instance of
size n in d dimensions; as of this writing, it
is unknown whether a lower bound higher than
Ω(n logn) can be proven. In this article,
we derive a lower bound of Ω(nlogn) for the
complexity of computing the hypervolume indicator in
any number of dimensions d>1 by reducing the
socalled UniformGap problem to it. For
the three dimensional case, we also present a
matching upper bound of O(nlogn)
comparisons that is obtained by extending an
algorithm for finding the maxima of a point set.

[179]

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

[180]

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 ]

[181]

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

[182]

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 ]

[183]

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 ]

[184]

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 ]

[185]

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 ]

[186]

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 ]

[187]

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 ]

[188]

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 ]

[189]

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 ]

[190]

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 ]

[191]

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 ]

[192]

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 ]

[193]

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 ]

[194]

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 ]

[195]

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 ]

[196]

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 ]

[197]

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 ]

[198]

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 ]

[199]

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.

[200]

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 ]

[201]

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 ]

[202]

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 ]

[203]

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 ]

[204]

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 ]

[205]

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 ]

[206]

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.

[207]

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 ]

[208]

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 ]

[209]

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 ]

[210]

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 ]

[211]

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 ]

[212]

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 ]

[213]

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 ]

[214]

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 ]

[215]

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

[216]

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 ]

[217]

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 ]

[218]

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 ]

[219]

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 ]

[220]

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 ]

[221]

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 ]

[222]

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

[223]

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

[224]

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 ]

[225]

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 ]

[226]

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

[227]

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

[228]

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

[229]

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

[230]

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 ]

[231]

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 ]

[232]

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

[233]

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

[234]

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 ]

[235]

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 ]

[236]

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 ]

[237]

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 ]

[238]

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 ]

[239]

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 ]

[240]

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 ]

[241]

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 ]

[242]

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 ]

[243]

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 ]

[244]

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

[245]

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 ]

[246]

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 [247].
[ bib ]

[247]

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.

[248]

Christian Blum, Borja Calvo, and María J. Blesa.
FrogCOL and FrogMIS: new decentralized algorithms for
finding large independent sets in graphs.
Swarm Intelligence, 9(23):205–227, 2015.
[ bib 
DOI ]
Keywords: irace

[249]

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 ]

[250]

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

[251]

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

[252]

Christian Blum and Manuel LópezIbáñez.
Ant Colony Optimization.
In The Industrial Electronics Handbook: Intelligent Systems.
CRC Press, second edition, 2011.
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http ]

[253]

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

[254]

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

[255]

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

[256]

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

[257]

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

[258]

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

[259]

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 ]

[260]

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

[261]

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

[262]

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

[263]

K. D. Boese.
Models for Iterative Global Optimization.
PhD thesis, University of California, Computer Science Department,
Los Angeles, CA, 1996.
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[264]

Marko Bohanec.
Decision making: a computerscience and informationtechnology
viewpoint.
Interdisciplinary Description of Complex Systems, 7(2):22–37,
2009.
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[265]

Ihor O. Bohachevsky, Mark E. Johnson, and Myron L. Stein.
Generalized Simulated Annealing for Function Optimization.
Technometrics, 28(3):209–217, 1986.
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[266]

Béla Bollobás.
Random Graphs.
Cambridge University Press, New York, NY, 2nd edition, 2001.
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[267]

Grady Booch, James E. Rumbaugh, and Ivar Jacobson.
The Unified Modeling Language User Guide.
AddisonWesley, 2 edition, 2005.
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[268]

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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[269]

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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[270]

Allan Borodin and Ran ElYaniv.
Online computation and competitive analysis.
Cambridge University Press, New York, NY, 1998.
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[271]

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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[272]

Hozefa M. Botee and Eric Bonabeau.
Evolving Ant Colony Optimization.
Advances in Complex Systems, 1:149–159, 1998.
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[273]

Marco Botte and Anita Schöbel.
Dominance for multiobjective robust optimization concepts.
European Journal of Operational Research, 273(2):430–440,
2019.
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[274]

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.
[ bib ]

[275]

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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[276]

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.
[ bib 
DOI ]
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

[277]

Paul F. Boulos, Chun Hou Orr, Werner de Schaetzen, J. G. Chatila, Michael
Moore, Paul Hsiung, and Devan Thomas.
Optimal pump operation of water distribution systems using
genetic algorithms.
In AWWA Distribution System Symp., Denver, USA, 2001. American
Water Works Association.
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[278]

V. Bowman and Jr. Joseph.
On the Relationship of the Tchebycheff Norm and the Efficient
Frontier of MultipleCriteria Objectives.
In H. Thiriez and S. Zionts, editors, Multiple Criteria Decision
Making, volume 130 of Lecture Notes in Economics and Mathematical
Systems, pages 76–86. Springer, Berlin/Heidelberg, 1976.
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[279]

George E. P. Box and Norman R. Draper.
Response surfaces, mixtures, and ridge analyses.
John Wiley & Sons, 2007.
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[280]

G. E. P. Box, W. G. Hunter, and J. S. Hunter.
Statistics for experimenters: an introduction to design, data
analysis, and model building.
John Wiley & Sons, New York, NY, 1978.
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[281]

A. Brandt.
Multilevel Computations: Review and Recent Developments.
In S. F. McCormick, editor, Multigrid Methods: Theory,
Applications, and Supercomputing, Proceedings of the 3rd Copper Mountain
Conference on Multigrid Methods, volume 110 of Lecture Notes in Pure
and Applied Mathematics, pages 35–62. Marcel Dekker, New York, 1988.
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[282]

L. Bradstreet, L. Barone, L. While, S. Huband, and P. Hingston.
Use of the WFG Toolkit and PISA for Comparison of MOEAs.
In IEEE Symposium on Computational Intelligence in
Multicriteria DecisionMaking, IEEE MCDM, pages 382–389, 2007.
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[283]

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.
[ bib 
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Keywords: multiple criteria decision making, evolutionary
multiobjective optimization

[284]

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

[285]

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

[286]

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

[287]

Jürgen Branke, T. Kaussler, and H. Schmeck.
Guidance in evolutionary multiobjective optimization.
Advances in Engineering Software, 32:499–507, 2001.
[ bib ]

[288]

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

[289]

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

[290]

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

[291]

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

[292]

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

[293]

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

[294]

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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[295]

Jürgen Branke and Jawad Elomari.
Simultaneous tuning of metaheuristic parameters for various
computing budgets.
In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2011, pages 263–264.
ACM Press, New York, NY, 2011.
[ bib 
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Keywords: metaoptimization, offline parameter optimization

[296]

Jürgen Branke and Jawad Elomari.
Racing with a Fixed Budget and a SelfAdaptive Significance
Level.
In P. M. Pardalos and G. Nicosia, editors, Learning and
Intelligent Optimization, 7th International Conference, LION 7, volume 7997
of Lecture Notes in Computer Science. Springer, Heidelberg, Germany,
2013.
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[297]

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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[298]

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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[299]

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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[300]

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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[301]

Mátyás Brendel and Marc Schoenauer.
Instancebased Parameter Tuning for Evolutionary AI Planning.
In N. Krasnogor and P. L. Lanzi, editors, GECCO (Companion),
pages 591–598, New York, NY, 2011. ACM Press.
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[302]

Leo Breiman.
Random Forests.
Machine Learning, 45(1):5–32, 2001.
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[303]

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.
[ bib ]

[304]

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.
[ bib ]

[305]

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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[306]

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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[307]

Karl Bringmann and Tobias Friedrich.
Don't be greedy when calculating hypervolume contributions.
In I. I. Garibay, T. Jansen, R. P. Wiegand, and A. S. Wu, editors,
Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA), pages 103–112. ACM, 2009.
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[308]

Karl Bringmann, Tobias Friedrich, Frank Neumann, and Markus Wagner.
Approximationguided Evolutionary Multiobjective Optimization.
In T. Walsh, editor, Proceedings of the TwentySecond
International Joint Conference on Artificial Intelligence (IJCAI11), pages
1198–1203. IJCAI/AAAI Press, Menlo Park, CA, 2011.
[ bib ]

[309]

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.
[ bib 
DOI ]
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

[310]

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.
[ bib 
DOI ]
Keywords: multiple criteria decision making, evolutionary
multiobjective optimization

[311]

Eric Brochu, Vlad Cora, and Nando de Freitas.
A Tutorial on Bayesian Optimization of Expensive Cost
Functions, with Application to Active User Modeling and Hierarchical
Reinforcement Learning.
Arxiv preprint arXiv:1012.2599, December 2010.
[ bib 
http ]

[312]

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

[313]

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.
[ bib ]

[314]

Peter Brucker, Johann Hurink, and Frank Werner.
Improving Local Search Heuristics for some Scheduling Problems
— Part II.
Discrete Applied Mathematics, 72(1–2):47–69, 1997.
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M. J. Brusco, L. W. Jacobs, and G. M. Thompson.
A Morphing Procedure to Supplement a Simulated Annealing
Heuristic for Cost and Coveragecorrelated Set Covering Problems.
Annals of Operations Research, 86:611–627, 1999.
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[316]

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

[317]

Artur Brum and Marcus Ritt.
Automatic Algorithm Configuration for the Permutation Flow Shop
Scheduling Problem Minimizing Total Completion Time.
In Evolutionary Computation in Combinatorial Optimization,
pages 85–100. Springer International Publishing, 2018.
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[318]

John T. Buchanan.
An experimental evaluation of interactive MCDM methods and the
decision making process.
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A. L. Buchsbaum and M. T. Goodrich.
ThreeDimensional Layers of Maxima.
Algorithmica, 39:275–289, 2004.
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[320]

T. N. Bui and J. R. Rizzo, Jr.
Finding Maximum Cliques with Distributed Ants.
In K. Deb et al., editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2004, Part I, volume 3102 of
Lecture Notes in Computer Science, pages 24–35. Springer, Heidelberg,
Germany, 2004.
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[321]

B. Bullnheimer, Richard F. Hartl, and Christine Strauss.
An Improved Ant System Algorithm for the Vehicle Routing
Problem.
Annals of Operations Research, 89:319–328, 1999.
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[322]

B. Bullnheimer, Richard F. Hartl, and Christine Strauss.
A new rankbased version of the Ant System: A
computational study.
Central European Journal for Operations Research and Economics,
7(1):25–38, 1999.
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[323]

Edmund K. Burke and Yuri Bykov.
The Late Acceptance HillClimbing Heuristic.
Technical Report CSM192, University of Stirling, 2012.
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Edmund K. Burke and Yuri Bykov.
The Late Acceptance HillClimbing Heuristic.
European Journal of Operational Research, 258(1):70–78, 2017.
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Evolutionary algorithms are widely used for solving
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In its current state, evolutionary multiobjective
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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
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An Evolutionary ManyObjective Optimization Algorithm Using
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Kalyanmoy Deb and Christie Myburgh.
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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
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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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Handling Multiple Scenarios in Evolutionary MultiObjective
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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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Automatic configuration (AC) methods are increasingly used to
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multiple objectives. Most AC methods use unary quality
indicators, which assign a single scalar value to an
approximation to the Pareto front, to compare the performance
of different optimisers. These quality indicators, however,
imply preferences beyond Paretooptimality that may differ
from those of the decision maker (DM). Although it is
possible to incorporate DM's preferences into quality
indicators, e.g., by means of the weighted hypervolume
indicator (HV^{w}), expressing preferences in terms of weight
function is not always intuitive nor an easy task for a DM,
in particular, when comparing the stochastic outcomes of
several algorithm configurations. A more visual approach to
compare such outcomes is the visualisation of their empirical
attainment functions (EAFs) differences. This paper proposes
using such visualisations as a way of eliciting information
about regions of the objective space that are preferred by
the DM. We present a method to convert the information about
EAF differences into a HV^{w} that will assign higher quality
values to approximation fronts that result in EAF differences
preferred by the DM. We show that the resulting HV^{w} may be
used by an AC method to guide the configuration of
multiobjective optimisers according to the preferences of
the DM. We evaluate the proposed approach on a wellknown
benchmark problem. Finally, we apply our approach to
reconfiguring, according to different DM's preferences, a
multiobjective optimiser tackling a realworld production
planning problem arising in the manufacturing industry.

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20th IFAC World Congress
Keywords: Adaptive Control, Constrained Bayesian Optimization, Safety,
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Many realworld optimization problems can be
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Given a finite set Y ⊂R^{d} of n mutually
nondominated vectors in d ≥2 dimensions, the
hypervolume contribution of a point y ∈Y is the
difference between the hypervolume indicator of Y
and the hypervolume indicator of Y ∖ {y}. In
multiobjective metaheuristics, hypervolume
contributions are computed in several selection and
boundedsize archiving procedures. This paper
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computing all hypervolume contributions. It is
proved that for d = 2,3 the problem has time
complexity Θ(n logn), and, for d > 3,
the time complexity is bounded below by Ω(n
logn). Moreover, complexity bounds are derived for
computing a single hypervolume contribution. A
dimension sweep algorithm with time complexity
O (n logn) and space
complexity O(n) is
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but might also discourage highrisk projects and pressure
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positive supports has grown by over 22% between 1990 and
2007, with significant differences between disciplines and
countries. The increase was stronger in the social and some
biomedical disciplines. The United States had published, over
the years, significantly fewer positive results than Asian
countries (and particularly Japan) but more than European
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nature of tree search that acts as a chaotic amplifier, so it
is largely unavoidable. We propose a betandrun approach to
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make a number of short sample runs with randomized initial
conditions, to bet on the "most promising" run selected
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objective space and their spread along the tradeoff
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A core feature of evolutionary algorithms is their mutation
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(1+1) Evolutionary Algorithm (EA). Our analyses show that
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are derived from novel combinations of a set of
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Algorithm selection is typically based on models of
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Kaizhou Gao, Yicheng Zhang, Ali Sadollah, and Rong Su.
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After growing up together, and mostly growing apart in the
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The customer order scheduling problem (COSP) is defined as
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Jérémie Humeau, Arnaud Liefooghe, ElGhazali Talbi, and Sébastien
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Maura Hunt and Manuel LópezIbáñez.
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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.

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M. Hurtgen and J.C. Maun.
Optimal PMU placement using Iterated Local Search.
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Mohamed Saifullah Hussin and Thomas Stützle.
Hierarchical Iterated Local Search for the Quadratic Assignment
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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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[920]

Frank Hutter, Domagoj Babić, Holger H. Hoos, and Alan J. Hu.
Boosting Verification by Automatic Tuning of Decision
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Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Kevin P. Murphy.
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Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Automated Configuration of Mixed Integer Programming Solvers.
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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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[923]

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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[924]

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

[925]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Parallel Algorithm Configuration.
In Y. Hamadi and M. Schoenauer, editors, Learning and
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of Lecture Notes in Computer Science, pages 55–70. Springer,
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[926]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Bayesian Optimization With Censored Response Data.
Arxiv preprint arXiv:1310.1947, 2013.
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[927]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Identifying key algorithm parameters and instance features using
forward selection.
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Intelligent Optimization, 7th International Conference, LION 7, volume 7997
of Lecture Notes in Computer Science, pages 364–381. Springer,
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Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
An Efficient Approach for Assessing Hyperparameter Importance.
In E. P. Xing and T. Jebara, editors, Proceedings of the 31st
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[929]

Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Kevin Murphy.
TimeBounded Sequential Parameter Optimization.
In C. Blum and R. Battiti, editors, Learning and Intelligent
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Lecture Notes in Computer Science, pages 281–298. Springer, Heidelberg,
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[930]

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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[931]

Frank Hutter, Holger H. Hoos, and Thomas Stützle.
Automatic Algorithm Configuration Based on Local Search.
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[932]

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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[933]

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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[934]

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.
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International Conference, LION 8, volume 8426 of Lecture Notes in
Computer Science, pages 36–40. Springer, Heidelberg, Germany, 2014.
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[935]

Frank Hutter, Lin Xu, Holger H. Hoos, and Kevin LeytonBrown.
Algorithm runtime prediction: Methods & evaluation.
Artificial Intelligence, 206:79–111, 2014.
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[936]

Frank Hutter.
SAT benchmarks used in automated algorithm configuration.
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[937]

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.
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[938]

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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[939]

Mauro Birattari.
The race Package for R: Racing Methods
for the Selection of the Best.
Technical Report TR/IRIDIA/2003037, IRIDIA, Université Libre de
Bruxelles, Belgium, 2003.
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[940]

Mauro Birattari.
On the Estimation of the Expected Performance of a Metaheuristic
on a Class of Instances. How Many Instances, How Many Runs?
Technical Report TR/IRIDIA/2004001, IRIDIA, Université Libre de
Bruxelles, Belgium, 2004.
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[941]

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 ]

[942]

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 [1246].
[ bib ]

[943]

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 [1255].
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[944]

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 [563].
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[945]

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 [566].
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[946]

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 [1822].
[ bib ]

[947]

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 [569].
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http ]

[948]

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 ]

[949]

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 [568].
[ bib 
http ]

[950]

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 [1238].
[ bib 
http ]

[951]

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 ]

[952]

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 [1262].
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http ]

[953]

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 ]

[954]

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 ]

[955]

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 [1263].
[ bib ]

[956]

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 [1608].
[ bib ]

[957]

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 ]

[958]

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 ]

[959]

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 ]

[960]

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 
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[961]

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 ]

[962]

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 ]

[963]

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 [206].
[ bib 
http ]

[964]

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.
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The electricity cost of pumping accounts for a large
part of the total operating cost for watersupply
networks. This study presents a method based on
linear programming for determining an optimal
(minimum cost) schedule of pumping on a 24hr
basis. Both unit and maximum demand electricity
charges are considered. Account is taken of the
relative efficiencies of the available pumps, the
structure of the electricity tariff, the
consumerdemand profile, and the hydraulic
characteristics and operational constraints of the
network. The use of extendedperiod simulation of
the network operation in determining the parameters
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and in studying the optimized network operation is
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Elena A. Kabova, Jason C. Cole, Oliver Korb, Manuel LópezIbáñez,
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Significant gains in the performance of the simulated
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simulated annealing parameters. Specifically, the success
rate in finding the global minimum in intensity χ^{2}
space is improved by up to an order of magnitude. The general
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Serdar Kadioglu, Yuri Malitsky, Meinolf Sellmann, and Kevin Tierney.
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Keywords: evolutionary computation;constraint
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The team orienteering problem (TOP) involves
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restricted by a prespecified limit. In this paper,
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proposed for the team orienteering problem. Four
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simultaneous methods, are proposed to construct
candidate solutions in the framework of ACO. We
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the theoretical analysis of ant colony optimization and
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combinatorial optimization problems, namely the traveling
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graph and show that it has a stronger local property than one
commonly used for constructing solutions of the TSP. The
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Published as [1164]. Keep this reference for historical reasons.

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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
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Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and 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.
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Y. Li and W. Li.
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Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao.
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Hui Li and Qingfu Zhang.
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Tianjun Liao, Dogan Aydin, and Thomas Stützle.
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Tianjun Liao, Daniel Molina, and Thomas Stützle.
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Tianjun Liao, Marco A. Montes de Oca, Dogan Aydin, Thomas Stützle,
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Tianjun Liao, Marco A. Montes de Oca, and Thomas Stützle.
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Workshop on Scaling Behaviours of Landscapes,
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Tianjun Liao, Krzysztof Socha, Marco A. Montes de Oca, Thomas Stützle,
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Keywords: ACOR

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Tianjun Liao and Thomas Stützle.
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Tianjun Liao, Thomas Stützle, Marco A. Montes de Oca, and Marco Dorigo.
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Tianjun Liao.
Populationbased Heuristic Algorithms for Continuous and Mixed
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Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Hernán E. Aguirre, and
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Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Manuel
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Arnaud Liefooghe, Jérémie Humeau, Salma Mesmoudi, Laetitia Jourdan, and
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This paper discusses simple local search approaches for
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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.

[1207]

Arnaud Liefooghe, Laetitia Jourdan, and ElGhazali Talbi.
A Software Framework Based on a Conceptual Unified Model for
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2011.
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[1208]

Arnaud Liefooghe, Manuel LópezIbáñez, Luís Paquete, and
Sébastien Verel.
Dominance, Epsilon, and Hypervolume Local Optimal Sets in
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In H. E. Aguirre and K. Takadama, editors, Proceedings of the
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[1209]

Arnaud Liefooghe, Salma Mesmoudi, Jérémie Humeau, Laetitia Jourdan, and
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A Study on Dominancebased Local Search Approaches for
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[1210]

Arnaud Liefooghe, Luís Paquete, Marco Simoes, and José Rui
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Bojan Likar and Juš Kocijan.
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Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, and Torsten Schaub.
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Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, and Torsten Schaub.
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W. Ling and H. Luo.
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Marius Thomas Lindauer, Jan N. Van Rijn, and Lars Kotthoff.
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Andrei Lissovoi and Carsten Witt.
Runtime Analysis of Ant Colony Optimization on Dynamic Shortest
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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.

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Innovation 24.
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Andrea Lodi, Silvano Martello, and Daniele Vigo.
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Andrea Lodi and Andrea Tramontani.
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Andrea Lodi, Silvano Martello, and Daniele Vigo.
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PoLing Loh and Sebastian Nowozin.
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Manuel LópezIbáñez.
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Manuel LópezIbáñez and Christian Blum.
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TSP with Time Windows.
Technical Report LSI0828, Department LSI, Universitat
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Extended version published in Computers & Operations
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[1229]

Manuel LópezIbáñez, Christian Blum, Dhananjay Thiruvady,
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[1230]

Manuel LópezIbáñez and Christian Blum.
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[1231]

Manuel LópezIbáñez and Christian Blum.
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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

[1232]

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 ]

[1233]

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 ]

[1234]

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

[1235]

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 [1233].
[ bib 
http ]

[1236]

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

[1237]

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.

[1238]

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

[1239]

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 ]

[1240]

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

[1241]

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 ]

[1242]

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

[1243]

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 ]

[1244]

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 [1245].
[ bib ]

[1245]

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.

[1246]

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.

[1247]

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” [1246].
[ bib ]
Please cite the book chapter, not this.

[1248]

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 ]

[1249]

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 ]

[1250]

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.

[1251]

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.

[1252]

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 ]

[1253]

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 ]

[1254]

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 ]

[1255]

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 ]

[1256]

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.

[1257]

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.

[1258]

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 [1257].
[ bib ]

[1259]

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

[1260]

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 ]

[1261]

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 ]

[1262]

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.

[1263]

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.

[1264]

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 ]

[1265]

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 ]

[1266]

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

[1267]

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

[1268]

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 ]

[1269]

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 ]

[1270]

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 ]

[1271]

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 ]

[1272]

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 ]

[1273]

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 ]

[1274]

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 ]

[1275]

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

[1276]

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

[1277]

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 ]

[1278]

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 ]

[1279]

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efficient set of the biobjective traveling salesman
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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
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A simple Genetic Algorithm has been applied to the
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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
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[ bib ]
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optimization framework

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This paper describes the methodology and application
of a genetic algorithm (GA) scheme, tailormade to
EPANET for simultaneously optimizing the scheduling
of existing pumping and booster disinfection units,
as well as the design of new disinfection booster
chlorination stations, under unsteady
hydraulics. The objective is to minimize the total
cost of operating the pumping units and the chlorine
booster operation and design for a selected
operational time horizon, while delivering the
consumers required water quantities, at acceptable
pressures and chlorine residual concentrations. The
decision variables, for each of the time steps that
encompass the total operational time horizon,
include: the scheduling of the pumping units,
settings of the water distribution system control
valves, and the mass injection rates at each of the
booster chlorination stations. The constraints are
domain heads and chlorine concentrations at the
consumer nodes, maximum injection rates at the
chlorine injection stations, maximum allowable
amounts of water withdraws at the sources, and
returning at the end of the operational time horizon
to a prescribed total volume in the tanks. The model
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Federico Pagnozzi and Thomas Stützle.
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Federico Pagnozzi and Thomas Stützle.
Automatic Design of Hybrid Stochastic Local Search Algorithms
for Permutation Flowshop Problems.
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Federico Pagnozzi and Thomas Stützle.
Automatic Design of Hybrid Stochastic Local Search Algorithms
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Federico Pagnozzi and Thomas Stützle.
Automatic design of hybrid stochastic local search algorithms
for permutation flowshop problems.
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Federico Pagnozzi and Thomas Stützle.
Automatic design of hybrid stochastic local search algorithms
for permutation flowshop problems with additional constraints.
http://iridia.ulb.ac.be/supp/IridiaSupp2018002/, 2019.
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Daniel Palhazi Cuervo, Peter Goos, Kenneth Sörensen, and Emely
Arráiz.
An Iterated Local Search Algorithm for the Vehicle Routing
Problem with Backhauls.
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2014.
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QuanKe Pan and Rubén Ruiz.
Local Search Methods for the Flowshop Scheduling Problem with
Flowtime Minimization.
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QuanKe Pan and Rubén Ruiz.
A Comprehensive Review and Evaluation of Permutation Flowshop
Heuristics to Minimize Flowtime.
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QuanKe Pan, Rubén Ruiz, and Pedro AlfaroFernández.
Iterated Search Methods for Earliness and Tardiness Minimization
in Hybrid Flowshops with Due Windows.
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QuanKe Pan, Mehmet Fatih Tasgetiren, and YunChia Liang.
A Discrete Differential Evolution Algorithm for the Permutation
Flowshop Scheduling Problem.
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QuanKe Pan, Ling Wang, and BaoHua Zhao.
An improved iterated greedy algorithm for the nowait flow shop
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Luís Paquete.
Algoritmos Evolutivos Multiobjectivo para Afectação de
Recursos e sua Aplicação à Geração de Horários em
Universidades (Multiobjective Evolutionary Algorithms for Resource
Allocation and their Application to University Timetabling).
Master's thesis, University of Algarve, 2001.
In Portuguese.
[ bib ]
The aim of this study is the application of
multiobjective evolutionary algorithms to resource
allocation problems, such as university examination
timetabling and course timetabling
problems. Usually, these problems are characterized
by multiple conflicting objectives. A multiobjective
formalization of these problems is presented, based
on goals and priorities. Various aspects of
evolutionary algorithms are proposed and studied for
these problems, particulary, selection methods and
types and parameters of mutation operator. The
choice of both representation and operators is made
so as not to favour excessively certain objectives
with respect to others at the level of the
exploration mechanism. A comparative study of
performance is presented for the proposed algorithms
by means of statistical inference, based on real
problems of the University of Algarve. The notion of
attainment functions is used as a base for the
assessment of performance of multiobjective
evolutionary algorithms. Finally, the evolution of
the solution cost during the runs is analysed by
means of attainment functions, as well.

[1520]

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

[1521]

Luís Paquete, Marco Chiarandini, and Thomas Stützle.
Pareto Local Optimum Sets in the Biobjective Traveling
Salesman Problem: An Experimental Study.
In X. Gandibleux, M. Sevaux, K. Sörensen, and V. T'Kindt,
editors, Metaheuristics for Multiobjective Optimisation, volume 535 of
Lecture Notes in Economics and Mathematical Systems, pages 177–199.
Springer, Berlin, Germany, 2004.
[ bib 
DOI ]
In this article, we study Pareto local optimum sets for the
biobjective Traveling Salesman Problem applying
straightforward extensions of local search algorithms for the
single objective case. The performance of the local search
algorithms is illustrated by experimental results obtained
for well known benchmark instances and comparisons to methods
from literature. In fact, a 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

[1522]

Luís Paquete, Carlos M. Fonseca, and Manuel LópezIbáñez.
An optimal algorithm for a special case of Klee's measure
problem in three dimensions.
Technical Report CSIRTI01/2006, CSI, Universidade do Algarve,
2006.
Superseded by paper in IEEE Transactions on Evolutionary
Computation [178].
[ bib ]
The measure of the region dominated by (the maxima
of) a set of n points in the positive dorthant
has been proposed as an indicator of performance in
multiobjective optimization, known as the
hypervolume indicator, and the problem of computing
it efficiently is attracting increasing
attention. In this report, this problem is
formulated as a special case of Klee's measure
problem in d dimensions, which immediately
establishes O(n^{d/2}logn) as a, possibly
conservative, upper bound on the required
computation time. Then, an O(n log n) algorithm
for the 3dimensional version of this special case
is constructed, based on an existing dimensionsweep
algorithm for the related maxima problem. Finally,
O(n log n) is shown to remain a lower bound on the
time required by the hypervolume indicator for
d>1, which attests the optimality of the algorithm
proposed.
Proof of Theorem 3.1 is incorrect

[1523]

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

[1524]

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

[1525]

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

[1526]

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

[1527]

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

[1528]

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

[1529]

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

[1530]

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

[1531]

Luís Paquete, Thomas Stützle, and Manuel LópezIbáñez.
Using experimental design to analyze stochastic local search
algorithms for multiobjective problems.
In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F.
Hartl, and M. Reimann, editors, Metaheuristics: Progress in Complex
Systems Optimization, volume 39 of Operations Research / Computer
Science Interfaces, pages 325–344. Springer, New York, NY, 2007.
[ bib 
DOI ]
Stochastic Local Search (SLS) algorithms can be seen
as being composed of several algorithmic components,
each playing some specific role with respect to
overall performance. This article explores the
application of experimental design techniques to
analyze the effect of components of SLS algorithms
for Multiobjective Combinatorial Optimization
problems, in particular for the Biobjective
Quadratic Assignment Problem. The analysis shows
that there exists a strong dependence between the
choices for these components and various instance
features, such as the structure of the input data
and the correlation between the objectives.
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Metaheuristics International Conference (MIC 2005)

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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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Puca Huachi Vaz Penna, Anand Subramanian, and Luiz Satoru Ochi.
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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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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.

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Leslie Pérez Cáceres, Federico Pagnozzi, Alberto Franzin, and Thomas
Stützle.
Automatic configuration of GCC using irace: Supplementary
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Multiobjective evolutionary optimization of traffic flow and
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Michael L. Pinedo.
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Pedro Pinto, Thomas Runkler, and João Sousa.
Ant Colony Optimization and its Application to Regular and
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In this chapter we discuss the ant colony
optimization metaheuristic (ACO) and its
application to static and dynamic constraint
satisfaction optimization problems, in particular
the static and dynamic maximum satisfiability
problems (MAXSAT). In the first part of the
chapter we give an introduction to metaheuristics
in general and ant colony optimization in
particular, followed by an introduction to
constraint satisfaction and static and dynamic
constraint satisfaction optimization problems.
Then, we describe how to apply the ACO algorithm
to the problems, and do an analysis of the results
obtained for several benchmarks. The adapted ant
colony optimization accomplishes very well the task
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David Pisinger and Stefan Ropke.
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David Pisinger and Stefan Ropke.
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Rapeepan Pitakaso, Christian Almeder, Karl F. Doerner, and Richard F. Hartl.
Combining exact and populationbased methods for the Constrained
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Rapeepan Pitakaso, Christian Almeder, Karl F. Doerner, and Richard F. Hartl.
A MaxMin Ant System for unconstrained multilevel lotsizing
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In this paper, we present an antbased algorithm
for solving unconstrained multilevel lotsizing
problems called ant system for multilevel
lotsizing algorithm (ASMLLS). We apply a hybrid
approach where we use ant colony optimization in
order to find a good lotsizing sequence, i.e. a
sequence of the different items in the product
structure in which we apply a modified
WagnerWhitin algorithm for each item
separately. Based on the setup costs each ant
generates a sequence of items. Afterwards a simple
singlestage lotsizing rule is applied with
modified setup costs. This modification of the setup
costs depends on the position of the item in the
lotsizing sequence, on the items which have been
lotsized before, and on two further parameters,
which are tried to be improved by a systematic
search. For smallsized problems ASMLLS is among
the best algorithms, but for most medium and
largesized problems it outperforms all other
approaches regarding solution quality as well as
computational time.
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Tunability: Importance of Hyperparameters of Machine Learning
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Heatmap visualization of population based multi objective
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preferences for the different categories when
selecting and visiting the points of interests
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decision situation arises. To determine all the
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uncertainties about the future which is unpredictable. These
uncertainties are almost impossible to reduce by gathering
more information and are not statistical in
nature. Therefore, classical probabilitybased approaches,
such as stochastic programming, do not address these
problems; as they require a correctlydefined complete sample
space, strong assumptions (e.g. normality), or both. The
proposed method extends the concept of twostage stochastic
programming with recourse to address the capability of
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planning rather than statistical expectation. In this
research, scenarios are used as a dimension of preference to
avoid problems relating to the assessment and use of
probabilities under deep uncertainty. Such scenariobased
thinking involved a multiobjective representation of
performance under different future conditions as an
alternative to expectation. To the best of our knowledge,
this is the first attempt of performing a multicriteria
evaluation under deep uncertainty through a structured
optimisation model. The proposed structure replacing
probabilities (in dynamic systems with deep uncertainties) by
aspirations within a goal programming structure. In fact,
this paper also proposes an extension of the goal programming
paradigm to deal with deep uncertainty. Furthermore, we will
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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)
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We consider the resourceconstrained project
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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
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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.
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mobility,Traffic light,WiFi connections

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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.
[ bib 
DOI ]
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

[1803]

Philip N. Strenski and Scott Kirkpatrick.
Analysis of Finite Length Annealing Schedules.
Algorithmica, 6(16):346–366, 1991.
[ bib ]

[1804]

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.
[ bib 
DOI ]
Keywords: vowels, accent features, dialect leveling, Random forest
(bagging), Feature selecion

[1805]

Thomas Stützle.
Iterated Local Search for the Quadratic Assignment Problem.
European Journal of Operational Research, 174(3):1519–1539,
2006.
[ bib ]

[1806]

Thomas Stützle.
Applying Iterated Local Search to the Permutation Flow Shop
Problem.
Technical Report AIDA–98–04, FG Intellektik, FB Informatik, TU
Darmstadt, Germany, August 1998.
[ bib ]

[1807]

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

[1808]

Thomas Stützle.
Some Thoughts on Engineering Stochastic Local Search
Algorithms.
In A. Viana et al., editors, Proceedings of the EU/MEeting 2009:
Debating the future: new areas of application and innovative approaches,
pages 47–52, 2009.
[ bib ]

[1809]

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

[1810]

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.
[ bib ]

[1811]

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.
[ bib ]

[1812]

Thomas Stützle and Marco Dorigo.
ACO Algorithms for the Quadratic Assignment Problem.
In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in
Optimization, pages 33–50. McGraw Hill, London, UK, 1999.
[ bib ]

[1813]

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.
[ bib ]

[1814]

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.
[ bib ]

[1815]

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

[1816]

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.
[ bib ]

[1817]

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
Optimization, pages 137–154. Kluwer Academic Publishers, Dordrecht, The
Netherlands, 1999.
[ bib ]

[1818]

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,
2015.
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[1819]

Thomas Stützle and Manuel LópezIbáñez.
Automated Offline Design of Algorithms.
In P. A. N. Bosman, editor, GECCO'17 Companion, pages
1038–1065. ACM Press, New York, NY, 2017.
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[1820]

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
Research & Management Science, pages 541–579. Springer, 2019.
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[1821]

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 &
Sons, 2011.
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[1822]

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
Search, pages 191–215. Springer, Berlin, Germany, 2012.
[ bib 
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[1823]

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,
2018.
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[1824]

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.
[ bib 
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[1825]

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

[1826]

James Styles and Holger H. Hoos.
Ordered racing protocols for automatically configuring
algorithms for scaling performance.
In C. Blum and E. Alba, editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2013, pages 551–558. ACM Press,
New York, NY, 2013.
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[1827]

James Styles, Holger H. Hoos, and Martin Müller.
Automatically Configuring Algorithms for Scaling Performance.
In Y. Hamadi and M. Schoenauer, editors, Learning and
Intelligent Optimization, 6th International Conference, LION 6, volume 7219
of Lecture Notes in Computer Science, pages 205–219. Springer,
Heidelberg, Germany, 2012.
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[1828]

Anand Subramanian and Maria Battarra.
An Iterated Local Search Algorithm for the Travelling Salesman
Problem with Pickups and Deliveries.
Journal of the Operational Research Society, 64(3):402–409,
2013.
[ bib ]

[1829]

Anand Subramanian, Maria Battarra, and Chris N. Potts.
An Iterated Local Search Heuristic for the Single Machine Total
Weighted Tardiness Scheduling Problem with Sequencedependent Setup Times.
International Journal of Production Research, 52(9):2729–2742,
2014.
[ bib ]

[1830]

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

[1831]

Jens Schreiter, Duy NguyenTuong, Mona Eberts, Bastian Bischoff, Heiner
Markert, and Marc Toussaint.
Safe Exploration for Active Learning with Gaussian Processes.
In Machine Learning and Knowledge Discovery in Databases, ECML
PKDD 2015, volume 9286 of Lecture Notes in Computer Science, pages
133–149. Springer, 2015.
[ bib 
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Proposed Safe Active Learning (SAL) algorithm

[1832]

Yanan Sui, Alkis Gotovos, Joel W. Burdick, and Andreas Krause.
Safe Exploration for Optimization with Gaussian Processes.
In F. Bach and D. Blei, editors, Proceedings of the 32nd
International Conference on Machine Learning, ICML 2015, volume 37, pages
997–1005, 2015.
[ bib 
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pdf ]
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

[1833]

Yanan Sui, Vincent Zhuang, Joel W. Burdick, and Yisong Yue.
Stagewise Safe Bayesian Optimization with Gaussian
Processes.
Arxiv preprint arXiv:1806.07555, 2018.
[ bib 
http ]
Enforcing safety is a key aspect of many problems pertaining
to sequential decision making under uncertainty, which
require the decisions made at every step to be both
informative of the optimal decision and also safe. For
example, we value both efficacy and comfort in medical
therapy, and efficiency and safety in robotic control. We
consider this problem of optimizing an unknown utility
function with absolute feedback or preference feedback
subject to unknown safety constraints. We develop an
efficient safe Bayesian optimization algorithm, StageOpt,
that separates safe region expansion and utility function
maximization into two distinct stages. Compared to existing
approaches which interleave between expansion and
optimization, we show that StageOpt is more efficient and
naturally applicable to a broader class of problems. We
provide theoretical guarantees for both the satisfaction of
safety constraints as well as convergence to the optimal
utility value. We evaluate StageOpt on both a variety of
synthetic experiments, as well as in clinical practice. We
demonstrate that StageOpt is more effective than existing
safe optimization approaches, and is able to safely and
effectively optimize spinal cord stimulation therapy in our
clinical experiments.
Published as [1834]
Keywords: StageOpt

[1834]

Yanan Sui, Vincent Zhuang, Joel W. Burdick, and Yisong Yue.
Stagewise Safe Bayesian Optimization with Gaussian
Processes.
In J. G. Dy and A. Krause, editors, Proceedings of the 35th
International Conference on Machine Learning, ICML 2018, volume 80 of
Proceedings of Machine Learning Research, pages 4788–4796. PMLR, 2018.
[ bib 
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Keywords: StageOpt

[1835]

Zhaoxu Sun and Min Han.
Multicriteria Decision Making Based on PROMETHEE Method.
In Proceedings of the 2010 International Conference on
Computing, Control and Industrial Engineering, pages 416–418, Los Alamitos,
CA, 2010. IEEE Computer Society Press.
[ bib ]

[1836]

A. Suppapitnarm, K. A. Seffen, G. T. Parks, and P. J. Clarkson.
A simulated annealing algorithm for multiobjective
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Engineering Optimization, 33(1):59–85, 2000.
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Richard S. Sutton and Andrew G. Barto.
Reinforcement Learning: An Introduction.
MIT Press, Cambridge, MA, 1998.
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Richard S. Sutton and Andrew G. Barto.
Reinforcement Learning: An Introduction.
MIT Press, Cambridge, MA, 2nd edition, 2018.
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D. C. Sutton, D. S. Keane, and S. J. Sherriff.
Optimizing the Real Time Operation of a Pumping Station at a
Water Filtration Plant using Genetic Algorithms.
Honors thesis, Department of Civil and Environmental Engineering, The
University of Adelaide, 1998.
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Johan A. K. Suykens and Joos Vandewalle.
Least Squares Support Vector Machine Classifiers.
Neural Processing Letters, 9(3):293–300, 1999.
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Jerry Swan, Ender Özcan, and Graham Kendall.
Hyperion  a recursive hyperheuristic framework.
In C. A. Coello Coello, editor, Learning and Intelligent
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Searching the Hyperheuristic Design Space.
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Jerry Swan et al.
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Gilbert Syswerda.
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Keywords: uniform crossover

[1845]

Harold Szu and Ralph Hartley.
Fast Simulated Annealing.
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Kiyoharu Tagawa, Hidehito Shimizu, and Hiroyuki Nakamura.
Indicatorbased Differential Evolution Using Exclusive
Hypervolume Approximation and Parallelization for Multicore Processors.
In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2011, pages 657–664.
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Éric D. Taillard.
Some Efficient Heuristic Methods for the Flow Shop Sequencing
Problem.
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[1848]

Éric D. Taillard.
Robust Taboo Search for the Quadratic Assignment Problem.
Parallel Computing, 17(45):443–455, 1991.
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faster 2exchange delta evaluation in QAP

[1849]

Éric D. Taillard.
Benchmarks for Basic Scheduling Problems.
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[1850]

Éric D. Taillard.
Comparison of Iterative Searches for the Quadratic Assignment
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Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf.
Deepface: Closing the gap to humanlevel performance in face
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ElGhazali Talbi.
A Taxonomy of Hybrid Metaheuristics.
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Kar Yan Tam.
A Simulated Annealing Algorithm for Allocating Space to
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Shunji Tanaka and Mituhiko Araki.
An Exact Algorithm for the Singlemachine Total Weighted
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R. Tanabe, Hisao Ishibuchi, and A. Oyama.
Benchmarking Multi and ManyObjective Evolutionary Algorithms
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Lixin Tang and Xianpeng Wang.
Iterated local search algorithm based on very largescale
neighborhood for prizecollecting vehicle routing problem.
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M. Fatih Tasgetiren, Ozge Buyukdagli, QuanKe Pan, and Ponnuthurai N.
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A general variable neighborhood search algorithm for the noidle
permutation flowshop scheduling problem.
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M. F. Tasgetiren, D. Kizilay, QuanKe Pan, and Ponnuthurai N. Suganthan.
Iterated Greedy Algorithms for the Blocking Flowshop Scheduling
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M. Fatih Tasgetiren, YunChia Liang, Mehmet Sevkli, and Gunes Gencyilmaz.
A particle swarm optimization algorithm for makespan and total
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M. Fatih Tasgetiren, QuanKe Pan, Ponnuthurai N. Suganthan, and Ozge
Buyukdagli.
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for the noidle permutation flowshop scheduling problem.
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Jorge Tavares and Francisco B. Pereira.
Automatic Design of Ant Algorithms with Grammatical Evolution.
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Joc Cing Tay and Nhu Binh Ho.
Evolving dispatching rules using genetic programming for solving
multiobjective flexible jobshop problems.
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[1864]

Cristina Teixeira, José Covas, Thomas Stützle, and António
GasparCunha.
Engineering an Efficient TwoPhase Local Search for the
CoRotating TwinScrew Configuration Problem.
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18(2):271–291, 2011.
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[1865]

Cristina Teixeira, José Covas, Thomas Stützle, and António
GasparCunha.
MultiObjective Ant Colony Optimization for Solving the
TwinScrew Extrusion Configuration Problem.
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[1866]

Cristina Teixeira, José Covas, Thomas Stützle, and António
GasparCunha.
Hybrid Algorithms for the TwinScrew Extrusion Configuration
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[1867]

Cristina Teixeira, José Covas, Thomas Stützle, and António
GasparCunha.
Application of Pareto Local Search and MultiObjective Ant
Colony Algorithms to the Optimization of CoRotating Twin Screw Extruders.
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pages 115–120, 2009.
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Fitsum Teklu, Agachai Sumalee, and David Watling.
A Genetic Algorithm Approach for Optimizing Traffic Control
Signals Considering Routing.
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Google.
TensorFlow.
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Optimization of traffic flow within an urban traffic light
intersection with genetic algorithm.
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IEEE Press, 2010.
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Keywords: Genetic algorithm,Tjunction,Traffic control system,Traffic
flows

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J. Teo and Hussein A. Abbass.
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Hugo TerashimaMarín, Peter Ross, and Manuel ValenzuelaRendón.
Evolution of Constraint Satisfaction Strategies in Examination
Timetabling.
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Patrick Thibodeau.
Machinebased decisionmaking is coming.
Computer World, November 2011.
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Dirk Thierens.
Adaptive strategies for operator allocation.
In F. Lobo, C. F. Lima, and Z. Michalewicz, editors, Parameter
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[1876]

Dirk Thierens.
Adaptive operator selection for iterated local search.
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[1877]

Dirk Thierens.
Populationbased Iterated Local Search: Restricting the
Neighborhood Search by Crossover.
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[1878]

Dirk Thierens.
An Adaptive Pursuit Strategy for Allocating Operator
Probabilities.
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Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
AutoWEKA: Combined Selection and Hyperparameter Optimization
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In I. S. Dhillon, Y. Koren, R. Ghani, T. E. Senator, P. Bradley,
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[1880]

TiewOn Ting, M. V. C. Rao, C. K. Loo, and S. S. Ngu.
Solving Unit Commitment Problem Using Hybrid Particle Swarm
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[1881]

Renato Tinós, Darrell Whitley, and Gabriela Ochoa.
Generalized Asymmetric Partition Crossover (GAPX) for the
Asymmetric TSP.
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[1882]

V. T'Kindt, Nicolas Monmarché, F. Tercinet, and D. Laügt.
An ant colony optimization algorithm to solve a 2machine
bicriteria flowshop scheduling problem.
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[1883]

C. E. Torres, L. F. Rossi, J. Keffer, K. Li, and C.C. Shen.
Modeling, analysis and simulation of antbased network routing
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[1884]

F. Toyama, K. Shoji, H. Mori, and J. Miyamichi.
An Iterated Greedy Algorithm for the Binary Quadratic
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[1885]

Heike Trautmann and Jörn Mehnen.
Preferencebased Pareto optimization in certain and noisy
environments.
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[1886]

Christoph Treude and Markus Wagner.
Predicting Good Configurations for GitHub and Stack Overflow
Topic Models.
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Keywords: algorithm portfolio, corpus features, topic modelling

[1887]

Vito Trianni and Manuel LópezIbáñez.
Advantages of TaskSpecific MultiObjective Optimisation in
Evolutionary Robotics.
PLoS One, 10(8):e0136406, 2015.
[ bib 
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The application of multiobjective optimisation to
evolutionary robotics is receiving increasing attention. A
survey of the literature reveals the different possibilities
it offers to improve the automatic design of efficient and
adaptive robotic systems, and points to the successful
demonstrations available for both taskspecific and
taskagnostic approaches (i.e., with or without reference to
the specific design problem to be tackled). However, the
advantages of multiobjective approaches over
singleobjective ones have not been clearly spelled out and
experimentally demonstrated. This paper fills this gap for
taskspecific approaches: starting from wellknown results in
multiobjective optimisation, we discuss how to tackle
commonly recognised problems in evolutionary robotics. In
particular, we show that multiobjective optimisation (i)
allows evolving a more varied set of behaviours by exploring
multiple tradeoffs of the objectives to optimise, (ii)
supports the evolution of the desired behaviour through the
introduction of objectives as proxies, (iii) avoids the
premature convergence to local optima possibly introduced by
multicomponent fitness functions, and (iv) solves the
bootstrap problem exploiting ancillary objectives to guide
evolution in the early phases. We present an experimental
demonstration of these benefits in three different case
studies: maze navigation in a single robot domain, flocking
in a swarm robotics context, and a strictly collaborative
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The classic book on statistical graphics, charts,
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statistical graphics, with detailed analysis of how to
display data for precise, effective, quick analysis. Design
of the highresolution displays, small multiples. Editing and
improving graphics. The dataink ratio. Timeseries,
relational graphics, data maps, multivariate
designs. Detection of graphical deception: design variation
vs. data variation. Sources of deception. Aesthetics and data
graphical displays. This new edition provides excellent color
reproductions of the many graphics of William Playfair, adds
color to other images, and includes all the changes and
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We present a reinforcement learning approach to explore and
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discounted cumulative reward while constraining the
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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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