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References

[1]

AAAI.
35th AAAI Conference on Artificial Intelligence: Reproducibility
Checklist.
https://aaai.org/Conferences/AAAI21/reproducibilitychecklist/, 2021.
Last accessed: June 6th, 2021.
[ bib ]

[2]

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

[3]

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

[4]

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

[5]

Ricardo Henrique Remes de Lima and Aurora Trinidad Ramirez Pozo.
A study on autoconfiguration of MultiObjective Particle Swarm
Optimization Algorithm.
In Proceedings of the 2017 Congress on Evolutionary Computation
(CEC 2017), pp. 718–725, Piscataway, NJ, 2017. IEEE Press.
[ bib 
DOI ]

[6]

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), pp. 971–978, Piscataway, NJ, 2001. IEEE Press.
[ bib ]

[7]

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

[8]

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 ]

[9]

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

[10]

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

[11]

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

[12]

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

[13]

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

[14]

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

[15]

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 ]

[16]

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

[17]

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

[18]

Bekir Afsar, Kaisa Miettinen, and Francisco Ruiz.
Assessing the Performance of Interactive Multiobjective
Optimization Methods: A Survey.
ACM Computing Surveys, 54(4), 2021.
[ bib 
DOI ]
Interactive methods are useful decisionmaking tools for
multiobjective optimization problems, because they allow a
decisionmaker to provide her/his preference information
iteratively in a comfortable way at the same time as (s)he
learns about all different aspects of the problem. A wide
variety of interactive methods is nowadays available, and
they differ from each other in both technical aspects and
type of preference information employed. Therefore, assessing
the performance of interactive methods can help users to
choose the most appropriate one for a given problem. This is
a challenging task, which has been tackled from different
perspectives in the published literature. We present a
bibliographic survey of papers where interactive
multiobjective optimization methods have been assessed
(either individually or compared to other methods). Besides
other features, we collect information about the type of
decisionmaker involved (utility or value functions,
artificial or human decisionmaker), the type of preference
information provided, and aspects of interactive methods that
were somehow measured. Based on the survey and on our own
experiences, we identify a series of desirable properties of
interactive methods that we believe should be assessed.
Keywords: decisionmakers, Interactive methods, performance assessment,
preference information, multiobjective optimization problems

[19]

Ralph D'Agostino and E. S. Pearson.
Tests for Departure from Normality. Empirical Results for the
Distributions of b_{2} and b_{1}.
Biometrika, 60(3):613–622, December 1973.
[ bib 
DOI ]

[20]

Per J. Agrell.
On redundancy in multi criteria decision making.
European Journal of Operational Research, 98(3):571–586, 1997.
[ bib 
DOI ]
The concept of redundancy is accepted in Operations Research
and Information Theory. In Linear Programming, a constraint
is said to be redundant if the feasible decision space is
identical with or without the constraint. In Information
Theory, redundancy is used as a measure of the stability
against noise in transmission. Analogies with Multi Criteria
Decision Making (MCDM) are indicated and it is argued that
the redundancy concept should be used as a regular feature in
conditioning and analysis of Multi Criteria
Programs. Properties of a proposed conflictbased
characterisation are stated and some existence results are
derived. Redundancy is here intended for interactive methods,
when the efficient set is progressively explored. A new
redundancy test for the linear case is formulated from the
framework. A probabilistic method based on correlation is
proposed and tested for the nonlinear case. Finally, some
general guidelines are given concerning the redundancy
problem.
Keywords: Multi criteria decision making, Redundancy, objective
reduction, Vector optimisation

[21]

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 ]

[22]

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, pp. 407–422.
Springer, Heidelberg, 2009.
[ bib ]

[23]

Hernán E. Aguirre.
Advances on Manyobjective Evolutionary Optimization.
In C. Blum and E. Alba, editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO Companion 2013, pp. 641–666,
New York, NY, 2013. ACM Press.
[ bib ]
Keywords: manyobjective evolutionary optimization

[24]

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 ]

[25]

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

[26]

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

[27]

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 ]

[28]

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

[29]

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

[30]

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

[31]

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, pp. 87–121.
Springer, US, 2010.
[ bib ]

[32]

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama.
Optuna: A Nextgeneration Hyperparameter Optimization
Framework.
In Teredesai et al., editors, 25th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, pp. 2623–2631. ACM
Press, New York, NY, July 2019.
[ bib 
DOI ]

[33]

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 ]

[34]

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, pp. 450–457. IEEE Computer Society
Press, Los Alamitos, CA, 2007.
[ bib ]

[35]

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), pp. 63–72, 2004.
[ bib 
http ]

[36]

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

[37]

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 ]

[38]

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 ]

[39]

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

[40]

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, pp. 198–206, New York, NY, 2019. ACM Press.
[ bib 
DOI 
epub ]

[41]

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

[42]

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

[43]

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

[44]

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

[45]

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

[46]

Richard Allmendinger and Joshua D. Knowles.
On Handling Ephemeral Resource Constraints in Evolutionary
Search.
Evolutionary Computation, 21(3):497–531, September 2013.
[ bib 
DOI ]
We consider optimization problems where the set of solutions
available for evaluation at any given time t during
optimization is some subset of the feasible space. This model
is appropriate to describe many closedloop optimization
settings (i.e. where physical processes or experiments are
used to evaluate solutions) where, due to resource
limitations, it may be impossible to evaluate particular
solutions at particular times (despite the solutions being
part of the feasible space). We call the constraints
determining which solutions are nonevaluable ephemeral
resource constraints (ERCs). In this paper, we investigate
two specific types of ERC: one encodes periodic resource
availabilities, the other models `commitment' constraints
that make the evaluable part of the space a function of
earlier evaluations conducted. In an experimental study, both
types of constraint are seen to impact the performance of an
evolutionary algorithm significantly. To deal with the
effects of the ERCs, we propose and test five different
constrainthandling policies (adapted from those used to
handle `standard' constraints), using a number of different
test functions including a fitness landscape from a real
closedloop problem. We show that knowing information about
the type of resource constraint in advance may be sufficient
to select an effective policy for dealing with it, even when
advance knowledge of the fitness landscape is limited.

[47]

Richard Allmendinger.
Tuning Evolutionary Search for ClosedLoop Optimization.
PhD thesis, The University of Manchester, UK, January 2012.
[ bib ]

[48]

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

[49]

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 ]

[50]

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 ]

[51]

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 (NeurIPS 32), pp. 9256–9266, 2019.
[ bib 
epub ]

[52]

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

[53]

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 ]

[54]

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

[55]

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

[56]

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

[57]

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

[58]

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

[59]

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

[60]

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

[61]

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, pp. 232–244. Springer, Heidelberg, 2007.
[ bib 
DOI ]

[62]

Kurt Anstreicher, Nathan Brixius, JeanPierre Goux, and Jeff Linderoth.
Solving large quadratic assignment problems on computational
grids.
Mathematical Programming Series B, 91(3):563–588, February
2002.
[ bib 
DOI ]
The quadratic assignment problem (QAP) is among the hardest
combinatorial optimization problems. Some instances of size
n = 30 have remained unsolved for decades. The solution of
these problems requires both improvements in mathematical
programming algorithms and the utilization of powerful
computational platforms. In this article we describe a novel
approach to solve QAPs using a stateoftheart
branchandbound algorithm running on a federation of
geographically distributed resources known as a computational
grid. Solution of QAPs of unprecedented complexity, including
the nug30, kra30b, and tho30 instances, is reported.

[63]

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

[64]

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 24th
International Joint Conference on Artificial Intelligence (IJCAI15), pp.
733–739. IJCAI/AAAI Press, Menlo Park, CA, 2015.
[ bib 
DOI ]
Keywords: GGA++

[65]

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

[66]

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, pp. 142–157. Springer, Heidelberg, 2009.
[ bib 
DOI ]
Keywords: GGA

[67]

David Applegate, Robert E. Bixby, Vašek 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 
DOI ]

[68]

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

[69]

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

[70]

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

[71]

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 ]

[72]

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

[73]

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 ]

[74]

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

[75]

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

[76]

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, pp. 71–78, New York, NY, December 2003. ACM Press.
[ bib 
DOI ]

[77]

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

[78]

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

[79]

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 ]

[80]

Sanjeev Arora and Boaz Barak.
Computational complexity: a modern approach.
Cambridge University Press, 2009.
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An empirical comparison of tabu search, simulated annealing, and
genetic algorithms for facilities location problems.
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[82]

José Elias C. Arroyo and V. A. Armentano.
A partial enumeration heuristic for multiobjective flowshop
scheduling problems.
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[83]

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Genetic local search for multiobjective flowshop scheduling
problems.
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Keywords: Multicriteria Scheduling

[84]

José Elias C. Arroyo and Joseph Y.T. Leung.
An Effective Iterated Greedy Algorithm for Scheduling Unrelated
Parallel Batch Machines with Nonidentical Capacities and Unequal Ready
Times.
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Etor Arza, Josu Ceberio, Aritz Pérez, and Ekhine Irurozki.
Approaching the quadratic assignment problem with kernels of
mallows models under the hamming distance.
In M. LópezIbáñez, A. Auger, and T. Stützle,
editors, Proceedings of the Genetic and Evolutionary Computation
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[86]

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
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N. Ascheuer, Matteo Fischetti, and M. Grötschel.
Solving asymmetric travelling salesman problem with time windows
by branchandcut.
Mathematical Programming, 90:475–506, 2001.
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[88]

N. Ascheuer.
Hamiltonian Path Problems in the Online Optimization of
Flexible Manufacturing Systems.
PhD thesis, Technische Universität Berlin, Berlin, Germany, 1995.
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Alper Atamtürk.
On the facets of the mixed–integer knapsack polyhedron.
Mathematical Programming, 98(1):145–175, 2003.
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[90]

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,
pp. 79–90. Centre for Water Systems, Exeter, UK, 2000.
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[91]

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, pp.
255–274. Springer, 2010.
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Charles Audet, CongKien Dang, and Dominique Orban.
Optimization of Algorithms with OPAL.
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[93]

P. Audze and Vilnis Eglãjs.
New approach to the design of multifactor experiments.
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Charles Audet and Dominique Orban.
Finding Optimal Algorithmic Parameters Using DerivativeFree
Optimization.
SIAM Journal on Optimization, 17(3):642–664, 2006.
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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}).

[96]

Peter Auer, Nicolo CesaBianchi, and Paul Fischer.
Finitetime analysis of the multiarmed bandit problem.
Machine Learning, 47(23):235–256, 2002.
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[97]

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, pp. 555–562. ACM Press,
New York, NY, 2009.
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[98]

Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler.
Investigating and Exploiting the Bias of the Weighted
Hypervolume to Articulate User Preferences.
In F. Rothlauf, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2009, pp. 563–570. ACM Press,
New York, NY, 2009.
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[99]

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, pp. 87–102. ACM Press,
New York, NY, 2009.
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[100]

Anne Auger, Johannes Bader, Dimo Brockhoff, and Eckart Zitzler.
Hypervolumebased multiobjective optimization: Theoretical
foundations and practical implications.
Theoretical Computer Science, 425:75–103, 2012.
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[101]

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, pp. 92–93. Schloss
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[102]

A. Auger and B. Doerr, editors.
Theory of Randomized Search Heuristics: Foundations and Recent
Developments, volume 1 of Series on Theoretical Computer Science.
World Scientific Publishing Co., Singapore, 2011.
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[103]

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

[104]

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

[105]

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.
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[106]

Andreea Avramescu, Richard Allmendinger, and Manuel LópezIbáñez.
Managing Manufacturing and Delivery of Personalised Medicine:
Current and Future Models.
Arxiv preprint arXiv:2105.12699 [econ.GN], 2021.
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[107]

Andreea Avramescu, Richard Allmendinger, and Manuel LópezIbáñez.
A MultiObjective MultiType Facility Location Problem for the
Delivery of Personalised Medicine.
In P. Castillo and J. L. Jiménez Laredo, editors,
Applications of Evolutionary Computation, volume 12694 of Lecture Notes
in Computer Science, pp. 388–403. Springer, Cham, Switzerland, 2021.
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supplementary material ]
Advances in personalised medicine targeting specific
subpopulations and individuals pose a challenge to the
traditional pharmaceutical industry. With a higher level of
personalisation, an already critical supply chain is facing
additional demands added by the very sensitive nature of its
products. Nevertheless, studies concerned with the efficient
development and delivery of these products are scarce. Thus,
this paper presents the case of personalised medicine and the
challenges imposed by its mass delivery. We propose a
multiobjective mathematical model for the
locationallocation problem with two interdependent facility
types in the case of personalised medicine products. We show
its practical application through a cell and gene therapy
case study. A multiobjective genetic algorithm with a novel
population initialisation procedure is used as solution
method.
Keywords: Personalised medicine, Biopharmaceuticals Supply chain,
Facility locationallocation, Evolutionary multiobjective
optimisation

[108]

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, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO Companion 2017, pp. 1311–1318,
New York, NY, 2017. ACM Press.
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[109]

Dogan Aydin, Gürcan Yavuz, and Thomas Stützle.
ABCX: A Generalized, Automatically Configurable Artificial
Bee Colony Framework.
Swarm Intelligence, 11(1):1–38, 2017.
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[110]

Mayowa Ayodele, Richard Allmendinger, Manuel LópezIbáñez, and
Matthieu Parizy.
MultiObjective QUBO Solver: BiObjective Quadratic Assignment
Problem.
In J. E. Fieldsend and M. Wagner, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2022, pp. 467–475.
ACM Press, New York, NY, 2022.
[ bib 
DOI ]
Quantum and quantuminspired optimisation algorithms are
designed to solve problems represented in binary, quadratic
and unconstrained form. Combinatorial optimisation problems
are therefore often formulated as Quadratic Unconstrained
Binary Optimisation Problems (QUBO) to solve them with these
algorithms. Moreover, these QUBO solvers are often
implemented using specialised hardware to achieve enormous
speedups, e.g. Fujitsu's Digital Annealer (DA) and DWave's
Quantum Annealer. However, these are singleobjective
solvers, while many realworld problems feature multiple
conflicting objectives. Thus, a common practice when using
these QUBO solvers is to scalarise such multiobjective
problems into a sequence of singleobjective problems. Due to
design tradeoffs of these solvers, formulating each
scalarisation may require more time than finding a local
optimum. We present the first attempt to extend the algorithm
supporting a commercial QUBO solver as a multiobjective
solver that is not based on scalarisation. The proposed
multiobjective DA algorithm is validated on the biobjective
Quadratic Assignment Problem. We observe that algorithm
performance significantly depends on the archiving strategy
adopted, and that combining DA with nonscalarisation methods
to optimise multiple objectives outperforms the current
scalarised version of the DA in terms of final solution
quality.
Keywords: digital annealer, multiobjective, biobjective QAP, QUBO

[111]

Mayowa Ayodele.
Penalty Weights in QUBO Formulations: Permutation Problems.
In L. Pérez Cáceres and S. Verel, editors, Proceedings
of EvoCOP 2022 – 22nd European Conference on Evolutionary Computation in
Combinatorial Optimization, Lecture Notes in Computer Science, pp.
159–174. Springer, Cham, Switzerland, 2022.
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[112]

Amine AzizAlaoui, Carola Doerr, and Johann Dréo.
Towards Large Scale Automated Algorithm Design by Integrating
Modular Benchmarking Frameworks.
In F. Chicano and K. Krawiec, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO Companion 2021, pp.
1365–1374, New York, NY, 2021. ACM Press.
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[113]

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.
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Keywords: Frace

[114]

Ilya Loshchilov and T. Glasmachers.
Black Box Optimization Competition, 2017.
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[115]

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.
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[116]

Eckart Zitzler, Marco Laumanns, and S. Bleuler.
A tutorial on evolutionary multiobjective optimization.
In X. Gandibleux, M. Sevaux, K. Sörensen, and V. T'Kindt,
editors, Metaheuristics for Multiobjective Optimisation, volume 535 of
Lecture Notes in Economics and Mathematical Systems, pp. 3–37.
Springer, Berlin, Germany, 2004.
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[117]

S. Bleuler, Marco Laumanns, Lothar Thiele, and Eckart Zitzler.
PISA – A Platform and Programming Language Independent
Interface for Search Algorithms.
In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele,
editors, Evolutionary Multicriterion Optimization, EMO 2003, volume
2632 of Lecture Notes in Computer Science, pp. 494–508. Springer,
Heidelberg, 2003.
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[118]

Domagoj Babić.
Spear theorem prover.
https://www.domagojbabic.com/index.php/ResearchProjects/Spear,
2008.
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[119]

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

[120]

Domagoj Babić and Frank Hutter.
Spear Theorem Prover.
In SAT'08: Proceedings of the SAT 2008 Race, 2008.
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Unreviewed paper

[121]

Thomas Bäck, David B. Fogel, and Zbigniew Michalewicz.
Handbook of evolutionary computation.
IOP Publishing, 1997.
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[122]

François Bachoc, Céline Helbert, and Victor Picheny.
Gaussian process optimization with failures: Classification and
convergence proof.
Journal of Global Optimization, 2020.
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epub ]
We consider the optimization of a computer model where each
simulation either fails or returns a valid output
performance. We first propose a new joint Gaussian process
model for classification of the inputs (computation failure
or success) and for regression of the performance
function. We provide results that allow for a computationally
efficient maximum likelihood estimation of the covariance
parameters, with a stochastic approximation of the likelihood
gradient. We then extend the classical improvement criterion
to our setting of joint classification and regression. We
provide an efficient computation procedure for the extended
criterion and its gradient. We prove the almost sure
convergence of the global optimization algorithm following
from this extended criterion. We also study the practical
performances of this algorithm, both on simulated data and on
a real computer model in the context of automotive fan
design.
Keywords: crashed simulation; latent gaussian process; automotive fan
design; industrial application; GP classification; Expected
Feasible Improvement with Gaussian Process Classification
with signs; EFI GPC sign

[123]

Achim Bachem, Barthel Steckemetz, and Michael Wottawa.
An efficient parallel clusterheuristic for large Traveling
Salesman Problems.
Technical Report 94150, University of Koln, Germany, 1994.
[ bib ]
Keywords: Genetic Edge Recombination (ERX)

[124]

Thomas Bäck.
Evolutionary algorithms in theory and practice: evolution
strategies, evolutionary programming, genetic algorithms.
Oxford University Press, 1996.
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[125]

Johannes Bader and Eckart Zitzler.
HypE: An Algorithm for Fast HypervolumeBased ManyObjective
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[126]

Hossein Baharmand, Tina Comes, and Matthieu Lauras.
Biobjective multilayer location– allocation model
for the immediate aftermath of suddenonset disasters.
Transportation Research Part E: Logistics and Transportation
Review, 127:86–110, 2019.
[ bib 
DOI ]
Locating distribution centers is critical for humanitarians
in the immediate aftermath of a suddenonset disaster. A
major challenge lies in balancing the complexity and
uncertainty of the problem with time and resource
constraints. To address this problem, we propose a
locationallocation model that divides the topography of
affected areas into multiple layers; considers constrained
number and capacity of facilities and fleets; and allows
decisionmakers to explore tradeoffs between response time
and logistics costs. To illustrate our theoretical work, we
apply the model to a real dataset from the 2015 Nepal
earthquake response. For this case, our method results in a
considerable reduction of logistics costs.

[127]

Monya Baker.
Is there a reproducibility crisis?
Nature, 533:452–454, 2016.
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[128]

Edward K. Baker.
An Exact Algorithm for the TimeConstrained Traveling Salesman
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Operations Research, 31(5):938–945, 1983.
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Burcu Balcik and Benita M. Beamon.
Facility location in humanitarian relief.
International Journal of Logistics, 11(2):101–121, 2008.
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[130]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
Incremental local search in ant colony optimization: Why it
fails for the quadratic assignment problem.
In M. Dorigo et al., editors, Ant Colony Optimization and Swarm
Intelligence, 5th International Workshop, ANTS 2006, volume 4150 of
Lecture Notes in Computer Science, pp. 156–166. Springer, Heidelberg,
2006.
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[131]

Prasanna Balaprakash, Mauro Birattari, and Thomas Stützle.
Improvement Strategies for the FRace Algorithm: Sampling
Design and Iterative Refinement.
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, pp. 108–122. Springer,
Heidelberg, 2007.
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[132]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
Adaptive Sampling Size and Importance Sampling in
Estimationbased Local Search for the Probabilistic Traveling Salesman
Problem.
European Journal of Operational Research, 199(1):98–110, 2009.
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Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
Estimationbased Metaheuristics for the Probabilistic Travelling
Salesman Problem.
Computers & Operations Research, 37(11):1939–1951, 2010.
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[134]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, and Marco Dorigo.
Estimationbased Metaheuristics for the Single Vehicle Routing
Problem with Stochastic Demands and Customers.
Computational Optimization and Applications, 61(2):463–487,
2015.
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[135]

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Zhi Yuan, and Marco
Dorigo.
Estimationbased Ant Colony Optimization Algorithms for the
Probabilistic Travelling Salesman Problem.
Swarm Intelligence, 3(3):223–242, 2009.
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[136]

Egon Balas and M. C. Carrera.
A Dynamic Subgradientbased Branch and Bound Procedure for Set
Covering.
Operations Research, 44(6):875–890, 1996.
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[137]

Egon Balas and Andrew Ho.
Set Covering Algorithms Using Cutting Planes, Heuristics, and
Subgradient Optimization: A Computational Study.
In M. W. Padberg, editor, Combinatorial optimization, volume 12
of Mathematical Programming Studies, pp. 37–60. Springer,
Berlin/Heidelberg, 1980.
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[138]

Egon Balas and C. Martin.
Pivot and Complement–A Heuristic for 0–1 Programming.
Management Science, 26(1):86–96, 1980.
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[139]

Egon Balas and M. W. Padberg.
Set Partitioning: A Survey.
SIAM Review, 18:710–760, 1976.
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[140]

Egon Balas and Neil Simonetti.
Linear Time DynamicProgramming Algorithms for New Classes of
Restricted TSPs: A Computational Study.
INFORMS Journal on Computing, 13(1):56–75, 2001.
[ bib 
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epub ]
Consider the following restricted (symmetric or
asymmetric) travelingsalesman problem (TSP):
given an initial ordering of the n cities and an
integer k > 0, find a minimumcost
feasible tour, where a feasible tour is one in which
city i precedes city j whenever j >= i + k in the
initial ordering. Balas (1996) has proposed a
dynamicprogramming algorithm that solves this
problem in time linear in n, though exponential in
k. Some important realworld problems are amenable
to this model or some of its close relatives. The
algorithm of Balas (1996) constructs a layered
network with a layer of nodes for each position in
the tour, such that sourcesink paths in this
network are in onetoone correspondence with tours
that satisfy the postulated precedence
constraints. In this paper we discuss an
implementation of the dynamicprogramming algorithm
for the general case when the integer k is replaced
with cityspecific integers k(j), j = 1, . . .,
n. We discuss applications to, and computational
experience with, TSPs with time windows, a model
frequently used in vehicle routing as well as in
scheduling with setup, release and delivery
times. We also introduce a new model, the TSP with
target times, applicable to JustinTime
scheduling problems. Finally for TSPs that have no
precedence restrictions, we use the algorithm as a
heuristic that finds in linear time a local optimum
over an exponentialsize neighborhood. For this
case, we implement an iterated version of our
procedure, based on contracting some arcs of the
tour produced by a first application of the
algorithm, then reapplying the algorithm to the
shrunken graph with the same k.
Keywords: tsptw

[141]

Egon Balas and A. Vazacopoulos.
Guided Local Search with Shifting Bottleneck for Job Shop
Scheduling.
Management Science, 44(2):262–275, 1998.
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[142]

Steven C. Bankes.
Tools and techniques for developing policies for complex and
uncertain systems.
Proceedings of the National Academy of Sciences, 99(suppl
3):7263–7266, 2002.
[ bib 
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Agentbased models (ABM) are examples of complex adaptive
systems, which can be characterized as those systems for
which no model less complex than the system itself can
accurately predict in detail how the system will behave at
future times. Consequently, the standard tools of policy
analysis, based as they are on devising policies that perform
well on some best estimate model of the system, cannot be
reliably used for ABM. This paper argues that policy analysis
by using ABM requires an alternative approach to decision
theory. The general characteristics of such an approach are
described, and examples are provided of its application to
policy analysis.ABM, agentbased model

[143]

P. Baptiste and L. K. Hguny.
A branch and bound algorithm for the
F/no_idle/C_{}max.
In Proceedings of the international conference on industrial
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[144]

Thomas BartzBeielstein.
Experimental Research in Evolutionary Computation: The New
Experimentalism.
Springer, Berlin, Germany, 2006.
[ bib ]
Keywords: SPO

[145]

Thomas BartzBeielstein.
How to Create Generalizable Results.
In J. Kacprzyk and W. Pedrycz, editors, Springer Handbook of
Computational Intelligence, pp. 1127–1142. Springer, Berlin/
Heidelberg, 2015.
[ bib ]
Keywords: Mixedeffects models, randomeffects model, problem instance
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[146]

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

[147]

Alejandro Barredo Arrieta, Natalia DíazRodríguez, Javier Del Ser,
Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio
GilLopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco
Herrera.
Explainable Artificial Intelligence (XAI): Concepts,
taxonomies, opportunities and challenges toward responsible AI.
Information Fusion, 58:82–115, June 2020.
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[148]

Thomas BartzBeielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya
Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La
Cava, Manuel LópezIbáñez, Katherine M. Malan, Jason H. Moore,
Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, and Thomas
Weise.
Benchmarking in Optimization: Best Practice and Open Issues.
Arxiv preprint arXiv:2007.03488 [cs.NE], 2020.
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[149]

Thomas BartzBeielstein, Oliver Flasch, Patrick Koch, and Wolfgang Konen.
SPOT: A Toolbox for Interactive and Automatic Tuning in the
R Environment.
In Proceedings 20. Workshop Computational Intelligence, pp.
264–273, Karlsruhe, 2010. KIT Scientific Publishing.
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[150]

Richard S. Barr, Bruce L. Golden, James P. Kelly, Mauricio G. C. Resende, and
Jr. William R. Stewart.
Designing and Reporting on Computational Experiments with
Heuristic Methods.
Journal of Heuristics, 1(1):9–32, 1995.
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[151]

Cynthia Barnhart, Ellis L. Johnson, George L. Nemhauser, Martin W. P.
Savelsbergh, and Pamela H. Vance.
Branchandprice: Column generation for solving huge integer
programs.
Operations Research, 46(3):316–329, 1998.
[ bib ]

[152]

Erin Bartholomew and Jan H. Kwakkel.
On considering robustness in the search phase of Robust Decision
Making: A comparison of ManyObjective Robust Decision Making, multiscenario
ManyObjective Robust Decision Making, and Many Objective Robust
Optimization.
Environmental Modelling & Software, 127:104699, 2020.
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[153]

Thomas BartzBeielstein, C. Lasarczyk, and Mike Preuss.
Sequential Parameter Optimization.
In Proceedings of the 2005 Congress on Evolutionary Computation
(CEC 2005), pp. 773–780, Piscataway, NJ, September 2005. IEEE Press.
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[154]

Thomas BartzBeielstein, C. Lasarczyk, and Mike Preuss.
The Sequential Parameter Optimization Toolbox.
In T. BartzBeielstein, M. Chiarandini, L. Paquete, and M. Preuss,
editors, Experimental Methods for the Analysis of Optimization
Algorithms, pp. 337–360. Springer, Berlin, Germany, 2010.
[ bib 
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Keywords: SPOT

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Automatic Design of Evolutionary Algorithms for MultiObjective
Combinatorial Optimization.
http://iridia.ulb.ac.be/supp/IridiaSupp2014007/, 2014.
[ bib ]

[235]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatic ComponentWise Design of MultiObjective Evolutionary
Algorithms.
https://github.com/iridiaulb/automoeatevc2016, 2015.
[ bib ]

[236]

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 ]

[237]

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, pp. 48–63.
Springer, Heidelberg, 2015.
[ bib 
DOI ]

[238]

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, pp. 396–410,
Heidelberg, 2015. Springer.
[ bib 
DOI ]

[239]

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 
supplementary material ]

[240]

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 ]

[241]

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 ]

[242]

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 
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.

[243]

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, volume 10173 of Lecture Notes in Computer
Science, pp. 31–45. Springer International Publishing, Cham, Switzerland,
2017.
[ bib 
DOI ]

[244]

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 ]

[245]

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

[246]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatically Designing StateoftheArt Multi and
ManyObjective Evolutionary Algorithms: Supplementary material.
https://github.com/iridiaulb/automoeaecj2020, 2019.
[ bib ]

[247]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Archiver Effects on the Performance of Stateoftheart Multi
and Manyobjective Evolutionary Algorithms.
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 
epub 
supplementary material ]

[248]

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 ]

[249]

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, pp.
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.

[250]

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

[251]

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 ]

[252]

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 ]

[253]

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

[254]

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, pp. 39–40.
University of Helsinki, 2014.
[ bib ]

[255]

André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, and
Marius Thomas Lindauer.
Dynamic Algorithm Configuration: Foundation of a New
MetaAlgorithmic Framework.
In G. D. Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro,
A. BugarÃn, and J. Lang, editors, ECAI 2020, volume 325 of
Frontiers in Artificial Intelligence and Applications, pp. 427–434. IOS
Press, 2020.
[ bib 
DOI ]

[256]

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

[257]

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

[258]

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

[259]

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

[260]

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, pp. 189–203. Springer, New York, NY, 2006.
[ bib ]

[261]

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 ]

[262]

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, 2011.
[ bib ]

[263]

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

[264]

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 ]

[265]

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 ]

[266]

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

[267]

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, pp. 311–336. Springer, Berlin, Germany, 2010.
[ bib 
DOI ]
Keywords: Frace, iterated Frace, irace, tuning

[268]

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 ]

[269]

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 ]

[270]

Mauro Birattari.
Tuning Metaheuristics: A Machine Learning Perspective, volume
197 of Studies in Computational Intelligence.
Springer, Berlin/Heidelberg, 2009.
[ bib 
DOI ]
Based on the PhD thesis [271]

[271]

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

[272]

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

[273]

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

[274]

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 ]

[275]

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 
epub ]

[276]

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, pp. 313–320. ACM
Press, New York, NY, 2012.
[ bib ]
Keywords: continuous optimization, landscape analysis, algorithm selection

[277]

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

[278]

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), pp. 1792–1799.
IEEE, 2019.
[ bib 
DOI ]

[279]

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 ]

[280]

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 ]

[281]

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, pp. 160–169. Springer, Heidelberg, 2004.
[ bib ]

[282]

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 ]

[283]

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, pp. 32–47. Springer,
Cham, Switzerland, 2016.
[ bib ]

[284]

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, pp. 227–234. ACM Press,
New York, NY, 2017.
[ bib 
DOI ]

[285]

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, pp.
323–334. Springer, Cham, Switzerland, 2018.
[ bib 
DOI ]

[286]

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, volume 10173 of Lecture Notes in Computer
Science, pp. 61–76. Springer International Publishing, Cham, Switzerland,
2017.
[ bib ]

[287]

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 ]

[288]

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

[289]

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, pp. 96–107. Springer, Heidelberg, 2006.
[ bib 
DOI ]

[290]

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 [291].
[ bib ]

[291]

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 ]
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.

[292]

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

[293]

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

[294]

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 ]

[295]

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 ]

[296]

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

[297]

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, pp. 123–139. Springer,
Heidelberg, 2007.
[ bib ]

[298]

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

[299]

Christian Blum and Gabriela Ochoa.
A comparative analysis of two matheuristics by means of merged
local optima networks.
European Journal of Operational Research, 290(1):36–56, 2021.
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[300]

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

[301]

Christian Blum, Jakob Puchinger, Günther R. Raidl, and Andrea Roli.
Hybrid Metaheuristics in Combinatorial Optimization: A Survey.
Applied Soft Computing, 11(6):4135–4151, 2011.
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[302]

Christian Blum and Günther R. Raidl.
Hybrid Metaheuristics—Powerful Tools for Optimization.
Artificial Intelligence: Foundations, Theory, and Algorithms.
Springer, Berlin, Germany, 2016.
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[303]

Christian Blum and Andrea Roli.
Metaheuristics in Combinatorial Optimization: Overview and
Conceptual Comparison.
ACM Computing Surveys, 35(3):268–308, 2003.
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[304]

Christian Blum and Andrea Roli.
Hybrid metaheuristics: an introduction.
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Germany, 2008.
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[305]

Christian Blum and M. Sampels.
An Ant Colony Optimization Algorithm for Shop Scheduling
Problems.
Journal of Mathematical Modelling and Algorithms,
3(3):285–308, 2004.
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[306]

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, pp. 94–109. Springer,
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[307]

Christian Blum, M. Yábar Vallès, and María J. Blesa.
An ant colony optimization algorithm for DNA sequencing by
hybridization.
Computers & Operations Research, 35(11):3620–3635, 2008.
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Andrea F. Bocchese, Chris Fawcett, Mauro Vallati, Alfonso E. Gerevini, and
Holger H. Hoos.
Performance robustness of AI planners in the 2014
International Planning Competition.
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Solver competitions have been used in many areas of AI to
assess the current state of the art and guide future research
and development. AI planning is no exception, and the
International Planning Competition (IPC) has been frequently
run for nearly two decades. Due to the organisational and
computational burden involved in running these competitions,
solvers are generally compared using a single homogeneous
hardware and software environment for all competitors. To
what extent does the specific choice of hardware and software
environment have an effect on solver performance, and is that
effect distributed equally across the competing solvers? In
this work, we use the competing planners and benchmark
instance sets from the 2014 IPC to investigate these two
questions. We recreate the 2014 IPC Optimal and Agile tracks
on two distinct hardware environments and eight distinct
software environments. We show that solver performance varies
significantly based on the hardware and software environment,
and that this variation is not equal for all
planners. Furthermore, the observed variation is sufficient
to change the competition rankings, including the topranked
planners for some tracks.

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[310]

K. D. Boese.
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PhD thesis, University of California, Computer Science Department,
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Marko Bohanec.
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Béla Bollobás.
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P. C. Borges and Michael Pilegaard Hansen.
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CHESS  Changing Horizon Efficient Set Search: A simple
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Local search heuristics for Quadratic Unconstrained Binary
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Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, and Hannah R.
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Histoire de l'Académie Royal des Sciences, 1781.
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[321]

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Marco Botte and Anita Schöbel.
Dominance for multiobjective robust optimization concepts.
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Salim Bouamama, Christian Blum, and Abdellah Boukerram.
A Populationbased Iterated Greedy Algorithm for the Minimum
Weight Vertex Cover Problem.
Applied Soft Computing, 12(6):1632–1639, 2012.
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[325]

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

[327]

Paul F. Boulos, Chun Hou Orr, Werner de Schaetzen, J. G. Chatila, Michael
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Artificial Decision Maker Driven by PSO: An Approach for
Testing Reference Point Based Interactive Methods.
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[334]

Jürgen Branke, Salvatore Corrente, Salvatore Greco, Milosz
Kadziński, Manuel LópezIbáñez, Vincent Mousseau, Mauro
Munerato, and Roman Slowiński.
BehaviorRealistic Artificial DecisionMakers to Test
PreferenceBased Multiobjective Optimization Method (Working Group
“Machine DecisionMaking”).
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Understanding Complexity in Multiobjective Optimization (Dagstuhl
Seminar 15031), volume 5(1) of Dagstuhl Reports, pp. 110–116.
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Keywords: multiple criteria decision making, evolutionary
multiobjective optimization

[335]

Yesnier Bravo, Javier Ferrer, Gabriel J. Luque, and Enrique Alba.
Smart Mobility by Optimizing the Traffic Lights: A New Tool for
Traffic Control Centers.
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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

[336]

Jürgen Branke, Salvatore Greco, Roman Slowiński, and P Zielniewicz.
Interactive evolutionary multiobjective optimization driven by
robust ordinal regression.
Bulletin of the Polish Academy of Sciences: Technical Sciences,
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[337]

S. C. Brailsford, Walter J. Gutjahr, M. S. Rauner, and W. Zeppelzauer.
Combined Discreteevent Simulation and Ant Colony Optimisation
Approach for Selecting Optimal Screening Policies for Diabetic Retinopathy.
Computational Management Science, 4(1):59–83, 2006.
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[338]

Jürgen Branke, T. Kaussler, and H. Schmeck.
Guidance in evolutionary multiobjective optimization.
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[339]

JeanPierre Brans and Bertrand Mareschal.
PROMETHEEGAIA. Une méthode d'aide à la décision
en présence de critères multiples.
Editions Ellipses, Paris, FR, 2002.
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[340]

JeanPierre Brans and Bertrand Mareschal.
PROMETHEE Methods.
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163–195. Springer, 2005.
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[341]

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,
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[342]

Jürgen Branke and C. Schmidt.
Faster Convergence by Means of Fitness Estimation.
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Jürgen Branke, C. Schmidt, and H. Schmeck.
Efficient fitness estimation in noisy environments.
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[344]

Roland Braune and G. Zäpfel.
Shifting Bottleneck Scheduling for Total Weighted Tardiness
Minimization—A Computational Evaluation of Subproblem and Reoptimization
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[345]

Jürgen Branke, Salvatore Corrente, Salvatore Greco, Roman Slowiński,
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Using Choquet integral as preference model in interactive
evolutionary multiobjective optimization.
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[346]

Jürgen Branke, Salvatore Corrente, Salvatore Greco, Roman Slowiński,
and P. Zielniewicz.
Using Choquet integral as preference model in interactive
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[347]

Jürgen Branke and Jawad Elomari.
Simultaneous tuning of metaheuristic parameters for various
computing budgets.
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Keywords: metaoptimization, offline parameter optimization

[348]

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
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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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[350]

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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[351]

Yaochu Jin and Jürgen Branke.
Evolutionary Optimization in Uncertain Environments—A Survey.
IEEE Transactions on Evolutionary Computation, 9(5):303–317,
2005.
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Mátyás Brendel and Marc Schoenauer.
LearnandOptimize: A Parameter Tuning Framework for
Evolutionary AI Planning.
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M. Schoenauer, editors, Artificial Evolution: 10th International
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[353]

Mátyás Brendel and Marc Schoenauer.
Instancebased Parameter Tuning for Evolutionary AI Planning.
In N. Krasnogor and P. L. Lanzi, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO Companion 2011, pp.
591–598, New York, NY, 2011. ACM Press.
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[354]

Leo Breiman.
Random Forests.
Machine Learning, 45(1):5–32, 2001.
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[355]

Karl Bringmann and Tobias Friedrich.
Approximating the Least Hypervolume Contributor: NPHard in
General, But Fast in Practice.
Theoretical Computer Science, 425:104–116, 2012.
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[356]

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, pp. 6–20.
Springer, Heidelberg, 2009.
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Extended version published in [355]

[357]

Karl Bringmann and Tobias Friedrich.
An efficient algorithm for computing hypervolume contributions.
Evolutionary Computation, 18(3):383–402, 2010.
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[358]

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, pp. 511–518. ACM
Press, New York, NY, 2010.
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[359]

Karl Bringmann and Tobias Friedrich.
Tight bounds for the approximation ratio of the hypervolume
indicator.
In R. Schaefer, C. Cotta, J. Kolodziej, and G. Rudolph, editors,
Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture
Notes in Computer Science, pp. 607–616, Heidelberg, 2010. Springer.
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[360]

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, pp. 745–752.
ACM Press, New York, NY, 2011.
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Extended version published as [362]

[361]

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, pp. 457–464. ACM
Press, New York, NY, 2012.
[ bib 
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epub ]
Extended version published as [362]

[362]

Karl Bringmann and Tobias Friedrich.
Convergence of hypervolumebased archiving algorithms.
IEEE Transactions on Evolutionary Computation, 18(5):643–657,
2014.
[ bib ]
Proof that all (μ+ λ) archiving algorithms with λ< μ are ineffective.
Keywords: competitive ratio

[363]

Karl Bringmann, Tobias Friedrich, and Patrick Klitzke.
Twodimensional subset selection for hypervolume and
epsilonindicator.
In C. Igel and D. V. Arnold, editors, Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2014. ACM Press, New York,
NY, 2014.
[ bib 
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[364]

Karl Bringmann and Tobias Friedrich.
Don't be greedy when calculating hypervolume contributions.
In I. I. Garibay, T. Jansen, R. P. Wiegand, and A. S. Wu, editors,
Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic
Algorithms (FOGA), pp. 103–112. ACM, 2009.
[ bib ]
Extended version published in [357]

[365]

Karl Bringmann, Tobias Friedrich, Frank Neumann, and Markus Wagner.
Approximationguided Evolutionary Multiobjective Optimization.
In T. Walsh, editor, Proceedings of the 22nd International Joint
Conference on Artificial Intelligence (IJCAI11), pp. 1198–1203.
IJCAI/AAAI Press, Menlo Park, CA, 2011.
[ bib ]

[366]

Dimo Brockhoff.
A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons
for Code Release and a Performance ReAssessment.
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, pp. 187–201.
Springer, Heidelberg, 2015.
[ bib 
DOI ]

[367]

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

[368]

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, pp.
85–94. Schloss Dagstuhl–LeibnizZentrum für Informatik, Germany, 2018.
[ bib 
DOI ]
Keywords: multiple criteria decision making, evolutionary
multiobjective optimization

[369]

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.
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[370]

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, Part I, volume 7491 of Lecture
Notes in Computer Science, pp. 123–132. Springer, Heidelberg, 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.

[371]

Dimo Brockhoff, Dhish Kumar Saxena, Kalyanmoy Deb, and Eckart Zitzler.
On Handling a Large Number of Objectives A Posteriori and During
Optimization.
In J. D. Knowles, D. Corne, K. Deb, and D. R. Chair, editors,
Multiobjective Problem Solving from Nature, Natural Computing Series, pp.
377–403. Springer, Berlin/Heidelberg, 2008.
[ bib 
DOI ]
Dimensionality reduction methods are used routinely in
statistics, pattern recognition, data mining, and machine
learning to cope with highdimensional spaces. Also in the
case of highdimensional multiobjective optimization
problems, a reduction of the objective space can be
beneficial both for search and decision making. New questions
arise in this context, e.g., how to select a subset of
objectives while preserving most of the problem structure. In
this chapter, two different approaches to the task of
objective reduction are developed, one based on assessing
explicit conflicts, the other based on principal component
analysis (PCA). Although both methods use different
principles and preserve different properties of the
underlying optimization problems, they can be effectively
utilized either in an a posteriori scenario or during
search. Here, we demonstrate the usability of the
conflictbased approach in a decisionmaking scenario after
the search and show how the principalcomponentbased
approach can be integrated into an evolutionary
multicriterion optimization (EMO) procedure.

[372]

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

[373]

Dimo Brockhoff, T. Wagner, and Heike Trautmann.
R2 indicatorbased multiobjective search.
Evolutionary Computation, 23(3):369–395, 2015.
[ bib ]

[374]

Dimo Brockhoff and Eckart Zitzler.
Are All Objectives Necessary? On Dimensionality Reduction in
Evolutionary Multiobjective Optimization.
In T. P. Runarsson, H.G. Beyer, E. K. Burke, J.J. Merelo,
D. Whitley, and X. Yao, editors, Parallel Problem Solving from Nature –
PPSN IX, volume 4193 of Lecture Notes in Computer Science, pp.
533–542, Heidelberg, 2006. Springer.
[ bib ]
Most of the available multiobjective evolutionary algorithms
(MOEA) for approximating the Pareto set have been designed
for and tested on low dimensional problems (≤3
objectives). However, it is known that problems with a high
number of objectives cause additional difficulties in terms
of the quality of the Pareto set approximation and running
time. Furthermore, the decision making process becomes the
harder the more objectives are involved. In this context, the
question arises whether all objectives are necessary to
preserve the problem characteristics. One may also ask under
which conditions such an objective reduction is feasible, and
how a minimum set of objectives can be computed. In this
paper, we propose a general mathematical framework, suited to
answer these three questions, and corresponding algorithms,
exact and heuristic ones. The heuristic variants are geared
towards direct integration into the evolutionary search
process. Moreover, extensive experiments for four wellknown
test problems show that substantial dimensionality reductions
are possible on the basis of the proposed methodology.

[375]

Dimo Brockhoff and Eckart Zitzler.
Dimensionality Reduction in Multiobjective Optimization: The
Minimum Objective Subset Problem.
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Research Proceedings 2006, pp. 423–429. Springer, Berlin/Heidelberg,
2007.
[ bib 
DOI ]
The number of objectives in a multiobjective optimization
problem strongly influences both the performance of
generating methods and the decision making process in
general. On the one hand, with more objectives, more
incomparable solutions can arise, the number of which affects
the generating method's performance. On the other hand, the
more objectives are involved the more complex is the choice
of an appropriate solution for a (human) decision maker. In
this context, the question arises whether all objectives are
actually necessary and whether some of the objectives may be
omitted; this question in turn is closely linked to the
fundamental issue of conflicting and nonconflicting
optimization criteria. Besides a general definition of
conflicts between objective sets, we here introduce the
NPhard problem of computing a minimum subset of objectives
without losing information (MOSS). Furthermore, we present
for MOSS both an approximation algorithm with optimum
approximation ratio and an exact algorithm which works well
for small input instances. We conclude with experimental
results for a random problem and the multiobjective
0/1knapsack problem
Keywords: objective reduction

[376]

Dimo Brockhoff and Eckart Zitzler.
Improving hypervolumebased multiobjective evolutionary
algorithms by using objective reduction methods.
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Theory and Applications.
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Manyobjective problems represent a major challenge in the
field of evolutionary multiobjective optimization, in terms of
search efficiency, computational cost, decision making,
visualization, and so on. This leads to various research
questions, in particular whether certain objectives can be
omitted in order to overcome or at least diminish the
difficulties that arise when many, that is, more than three,
objective functions are involved. This study addresses this
question from different perspectives. First, we investigate
how adding or omitting objectives affects the problem
characteristics and propose a general notion of conflict
between objective sets as a theoretical foundation for
objective reduction. Second, we present both exact and
heuristic algorithms to systematically reduce the number of
objectives, while preserving as much as possible of the
dominance structure of the underlying optimization
problem. Third, we demonstrate the usefulness of the proposed
objective reduction method in the context of both decision
making and search for a radar waveform application as well as
for wellknown test functions.

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Peter Brucker, Johann Hurink, and Frank Werner.
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Peter Brucker, Johann Hurink, and Frank Werner.
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Automatic Design of Heuristics for Minimizing the Makespan in
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Artur Brum and Marcus Ritt.
Automatic Algorithm Configuration for the Permutation Flow Shop
Scheduling Problem Minimizing Total Completion Time.
In A. Liefooghe and M. LópezIbáñez, editors,
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Maxim Buzdalov.
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Why the Intelligent Water Drops Cannot Be Considered as a Novel
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Adapting to a realistic decision maker: experiments towards a
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Christian Leonardo CamachoVillalón, Thomas Stützle, and Marco Dorigo.
PSOX: A ComponentBased Framework for the Automatic Design of
Particle Swarm Optimization Algorithms.
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Felipe Campelo, Áthila R. Trindade, and Manuel LópezIbáñez.
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Felipe Campelo and Elizabeth F. Wanner.
Sample size calculations for the experimental comparison of
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Gilles Caporossi.
Variable Neighborhood Search for Extremal Vertices : The
AutoGraphiXIII System.
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J. Carlier.
The Onemachine Sequencing Problem.
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Alex Guimarães Cardoso de Sá, Walter José G. S. Pinto, Luiz
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Ioannis Caragiannis, Ariel D. Procaccia, and Nisarg Shah.
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A wellstudied approach to the design of voting rules views
them as maximum likelihood estimators; given votes that are
seen as noisy estimates of a true ranking of the
alternatives, the rule must reconstruct the most likely true
ranking. We argue that this is too stringent a requirement,
and instead ask: How many votes does a voting rule need to
reconstruct the true ranking? We define the family of
pairwisemajority consistent rules, and show that for all
rules in this family the number of samples required from the
Mallows noise model is logarithmic in the number of
alternatives, and that no rule can do asymptotically better
(while some rules like plurality do much worse). Taking a
more normative point of view, we consider voting rules that
surely return the true ranking as the number of samples tends
to infinity (we call this property accuracy in the limit);
this allows us to move to a higher level of abstraction. We
study families of noise models that are parametrized by
distance functions, and find voting rules that are accurate
in the limit for all noise models in such general
families. We characterize the distance functions that induce
noise models for which pairwisemajority consistent rules are
accurate in the limit, and provide a similar result for
another novel family of positiondominance consistent
rules. These characterizations capture three wellknown
distance functions.
Keywords: computer social choice, mallows model, sample complexity

[428]

Yves Caseau and François Laburthe.
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Josu Ceberio, Ekhine Irurozki, Alexander Mendiburu, and José A. Lozano.
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[ bib 
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The aim of this paper is twofold. First, we introduce a
novel general estimation of distribution algorithm to deal
with permutationbased optimization problems. The algorithm
is based on the use of a probabilistic model for permutations
called the generalized Mallows model. In order to prove the
potential of the proposed algorithm, our second aim is to
solve the permutation flowshop scheduling problem. A hybrid
approach consisting of the new estimation of distribution
algorithm and a variable neighborhood search is
proposed. Conducted experiments demonstrate that the proposed
algorithm is able to outperform the stateoftheart
approaches. Moreover, from the 220 benchmark instances
tested, the proposed hybrid approach obtains new best known
results in 152 cases. An indepth study of the results
suggests that the successful performance of the introduced
approach is due to the ability of the generalized Mallows
estimation of distribution algorithm to discover promising
regions in the search space.
Keywords: Estimation of distribution algorithms,Generalized Mallows
model,Permutation flowshop scheduling
problem,Permutationsbased optimization problems

[433]

Josu Ceberio, Alexander Mendiburu, and José A. Lozano.
Kernels of Mallows Models for Solving Permutationbased
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Eranda Çela.
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Sara Ceschia, Luca Di Gaspero, and Andrea Schaerf.
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Amadeo Cesta, Angelo Oddi, and Stephen F. Smith.
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Clément Chevalier, David Ginsbourger, Julien Bect, and Ilya Molchanov.
Estimating and Quantifying Uncertainties on Level Sets Using the
Vorob'ev Expectation and Deviation with Gaussian Process Models.
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Several methods based on Kriging have recently been proposed
for calculating a probability of failure involving
costlytoevaluate functions. A closely related problem is to
estimate the set of inputs leading to a response exceeding a
given threshold. Now, estimating such a level set—and not
solely its volume—and quantifying uncertainties on it are
not straightforward. Here we use notions from random set
theory to obtain an estimate of the level set, together with
a quantification of estimation uncertainty. We give explicit
formulae in the Gaussian process setup and provide a
consistency result. We then illustrate how spacefilling
versus adaptive design strategies may sequentially reduce
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Yuning Chen, JinKao Hao, and Fred Glover.
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Rachid Chelouah and Patrick Siarry.
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[456]

Marco Chiarandini, Mauro Birattari, Krzysztof Socha, and O. RossiDoria.
An Effective Hybrid Algorithm for University Course
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Keywords: 2003 international timetabling competition, Frace

[457]

Manuel Chica, Oscar Cordón, Sergio Damas, and Joaquín Bautista.
A New Diversity Induction Mechanism for a Multiobjective Ant
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Marco Chiarandini and Yuri Goegebeur.
Mixed Models for the Analysis of Optimization Algorithms.
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Stochastic Local Search Methods for Highly Constrained
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Francisco Chicano, Gabriel J. Luque, and Enrique Alba.
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Evolutionary algorithms are widely used for solving
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In the traffic light scheduling problem the evaluation of
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they must be robust (low variance) across all different
scenarios. Previous work has shown that combining IRACE with
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power of evolutionary operators in numerical optimization. In
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In many realworld optimization problems, like the traffic
light scheduling problem tackled here, the evaluation of
candidate solutions requires the simulation of a process
under various scenarios. Thus, good solutions should not only
achieve good objective function values, but they must be
robust (low variance) across all different scenarios.
Previous work has revealed the effectiveness of IRACE for
this task. However, the operators used by IRACE to generate
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parameters, that have various data types (categorical,
numerical, etc.). Meanwhile, evolutionary algorithms have
powerful operators for numerical optimization, which could
help to sample new solutions from the best ones found in the
search. Therefore, in this work, we propose a hybridization
of the elitist iterated racing mechanism of IRACE with
evolutionary operators from differential evo lution and
genetic algorithms. We consider a realistic case study
derived from the traffic network of Malaga (Spain) with 275
traffic lights that should be scheduled optimally. After a
meticulous study, we discovered that the hybrid algorithm
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Extended version published as [473]
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[552]

Swagatam Das and Ponnuthurai N. Suganthan.
Differential Evolution: A Survey of the Stateoftheart.
IEEE Transactions on Evolutionary Computation, 15(1), February
2011.
[ bib ]

[553]

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

[554]

Samuel Daulton, Maximilian Balandat, and Eytan Bakshy.
Differentiable Expected Hypervolume Improvement for Parallel
MultiObjective Bayesian Optimization.
In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin,
editors, Advances in Neural Information Processing Systems (NeurIPS
33), pp. 9851–9864, 2020.
[ bib 
epub ]

[555]

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

[556]

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

[557]

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

[558]

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

[559]

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

[560]

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

[561]

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

[562]

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

[563]

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

[564]

Kalyanmoy Deb.
Multiobjective optimization.
In E. K. Burke and G. Kendall, editors, Search Methodologies,
pp. 273–316. Springer, Boston, MA, 2005.
[ bib 
DOI ]

[565]

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

[566]

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

[567]

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

[568]

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

[569]

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

[570]

Kalyanmoy Deb, S. Gupta, D. Daum, Jürgen Branke, A. Mall, and
D. Padmanabhan.
Reliabilitybased optimization using evolutionary algorithms.
IEEE Transactions on Evolutionary Computation,
13(5):1054–1074, October 2009.
[ bib 
DOI ]

[571]

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

[572]

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

[573]

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

[574]

Kalyanmoy Deb, Manikanth Mohan, and Shikhar Mishra.
Evaluating the εdomination based multiobjective
evolutionary algorithm for a quick computation of Paretooptimal
solutions.
Evolutionary Computation, 13(4):501–525, December 2005.
[ bib 
DOI ]
Keywords: εdominance, εMOEA

[575]

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

[576]

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

[577]

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

[578]

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

[579]

Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler.
Scalable Test Problems for Evolutionary MultiObjective
Optimization.
Technical Report 112, Computer Engineering and Networks Laboratory
(TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland,
2001.
Do not cite this TR! It is incorrect and it is superseeded
by [580].
[ bib ]
Keywords: DTLZ benchmark

[580]

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

[581]

Kalyanmoy Deb, Ling Zhu, and Sandeep Kulkarni.
Handling Multiple Scenarios in Evolutionary MultiObjective
Numerical Optimization.
IEEE Transactions on Evolutionary Computation, 22(6):920–933,
2018.
[ bib 
DOI ]
Solutions to most practical numerical optimization problems
must be evaluated for their performance over a number of
different loading or operating conditions, which we refer
here as scenarios. Therefore, a meaningful and resilient
optimal solution must be such that it remains feasible under
all scenarios and performs close to an individual optimal
solution corresponding to each scenario. Despite its
practical importance, multiscenario consideration has
received a lukewarm attention, particularly in the context of
multiobjective optimization. The usual practice is to
optimize for the worstcase scenario. In this paper, we
review existing methodologies in this direction and set our
goal to suggest a new and potential populationbased method
for handling multiple scenarios by defining scenariowise
domination principle and scenariowise diversitypreserving
operators. To evaluate, the proposed method is applied to a
number of numerical test problems and engineering design
problems with a detail explanation of the obtained results
and compared with an existing method. This first systematic
evolutionary based multiscenario, multiobjective,
optimization study on numerical problems indicates that
multiple scenarios can be handled in an integrated manner
using an EMO framework to find a wellbalanced compromise set
of solutions to multiple scenarios and maintain a tradeoff
among multiple objectives. In comparison to an existing
serial multiple optimization approach, the proposed approach
finds a set of compromised tradeoff solutions
simultaneously. An achievement of multiobjective tradeoff
and multiscenario tradeoff is algorithmically challenging,
but due to its practical appeal, further research and
application must be spent.
Keywords: scenariobased

[582]

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

[583]

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

[584]

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

[585]

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

[586]

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

[587]

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

[588]

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

[589]

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

[590]

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

[591]

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

[592]

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

[593]

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

[594]

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

[595]

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

[596]

JeanLouis Deneubourg, S. Aron, S. Goss, and J.M. Pasteels.
The SelfOrganizing Exploratory Pattern of the Argentine Ant.
Journal of Insect Behavior, 3(2):159–168, 1990.
[ bib 
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[597]

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

[598]

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

[599]

Jia Deng, Wei Dong, Richard Socher, LiJia Li, Kai Li, and Li FeiFei.
Imagenet: A largescale hierarchical image database.
In Computer Vision and Pattern Recognition, 2009. CVPR 2009.
IEEE Conference on, pp. 248–255. IEEE, 2009.
[ bib ]

[600]

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

[601]

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

[602]

Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, and Dae Hyun Kim.
Bayesian Optimization over Permutation Spaces.
Arxiv preprint arXiv:2112.01049, 2021.
[ bib 
DOI ]
Keywords: BOPS, CEGO

[603]

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

[604]

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

[605]

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

[606]

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

[607]

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

[608]

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

[609]

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

[610]

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

[611]

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

[612]

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

[613]

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

[614]

Juan Esteban Diaz and Manuel LópezIbáñez.
Incorporating DecisionMaker's Preferences into the Automatic
Configuration of BiObjective Optimisation Algorithms.
European Journal of Operational Research, 289(3):1209–1222,
2021.
[ bib 
DOI 
supplementary material ]
Automatic configuration (AC) methods are increasingly used to
tune and design optimisation algorithms for problems with
multiple objectives. Most AC methods use unary quality
indicators, which assign a single scalar value to an
approximation to the Pareto front, to compare the performance
of different optimisers. These quality indicators, however,
imply preferences beyond Paretooptimality that may differ
from those of the decision maker (DM). Although it is
possible to incorporate DM's preferences into quality
indicators, e.g., by means of the weighted hypervolume
indicator (HV^{w}), expressing preferences in terms of weight
function is not always intuitive nor an easy task for a DM,
in particular, when comparing the stochastic outcomes of
several algorithm configurations. A more visual approach to
compare such outcomes is the visualisation of their empirical
attainment functions (EAFs) differences. This paper proposes
using such visualisations as a way of eliciting information
about regions of the objective space that are preferred by
the DM. We present a method to convert the information about
EAF differences into a HV^{w} that will assign higher quality
values to approximation fronts that result in EAF differences
preferred by the DM. We show that the resulting HV^{w} may be
used by an AC method to guide the configuration of
multiobjective optimisers according to the preferences of
the DM. We evaluate the proposed approach on a wellknown
benchmark problem. Finally, we apply our approach to
reconfiguring, according to different DM's preferences, a
multiobjective optimiser tackling a realworld production
planning problem arising in the manufacturing industry.

[615]

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

[616]

Ilias Diakonikolas and M. Yannakakis.
Small approximate Pareto sets for biobjective shortest paths
and other problems.
SIAM Journal on Computing, 39(4):1340–1371, 2009.
[ bib ]

[617]

Gianni A. Di Caro and Marco Dorigo.
AntNet: Distributed Stigmergetic Control for Communications
Networks.
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[618]

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

[619]

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

[620]

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

[621]

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

[622]

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

[623]

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

[624]

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

[625]

Daniel Doblas, Antonio J. Nebro, Manuel LópezIbáñez, José
GarcíaNieto, and Carlos A. Coello Coello.
Automatic Design of Multiobjective Particle Swarm Optimizers.
In M. Dorigo, H. Hamann, M. LópezIbáñez,
J. GarcíaNieto, A. Engelbrecht, C. Pinciroli, V. Strobel, and C. L.
CamachoVillalón, editors, Swarm Intelligence, 13th International
Conference, ANTS 2022, volume 13491 of Lecture Notes in Computer
Science, pp. 28–40. Springer, Cham, Switzerland, 2022.
[ bib 
DOI ]

[626]

Benjamin Doerr, Carola Doerr, and Franziska Ebel.
From blackbox complexity to designing new genetic algorithms.
Theoretical Computer Science, 567:87–104, 2015.
[ bib 
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[627]

Benjamin Doerr, Carola Doerr, and Jing Yang.
Optimal parameter choices via precise blackbox analysis.
Theoretical Computer Science, 801:1–34, 2020.
[ bib 
DOI ]

[628]

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

[629]

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

[630]

Karl F. Doerner, Walter J. Gutjahr, Richard F. Hartl, Christine Strauss, and
Christian Stummer.
NatureInspired Metaheuristics in Multiobjective Activity
Crashing.
Omega, 36(6):1019–1037, 2008.
[ bib ]

[631]

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

[632]

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

[633]

Karl F. Doerner, Richard F. Hartl, and Marc Reimann.
Are COMPETants more competent for problem solving? The case
of a multiple objective transportation problem.
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20th IFAC World Congress
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Given a finite set Y ⊂R^{d} of n mutually
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and the hypervolume indicator of Y ∖ {y}. In
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boundedsize archiving procedures. This paper
presents new results on the (time) complexity of
computing all hypervolume contributions. It is
proved that for d = 2 and 3 the problem has time
complexity Θ(n logn), and, for d > 3,
the time complexity is bounded below by Ω(n
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dimension sweep algorithm with time complexity
O (n logn) and space
complexity O(n) is
proposed for computing all hypervolume contributions
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2007, with significant differences between disciplines and
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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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Carlos M. Fonseca and Peter J. Fleming.
Genetic Algorithms for Multiobjective Optimization: Formulation,
Discussion and Generalization.
In S. Forrest, editor, ICGA, pp. 416–423. Morgan Kaufmann
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Proposes MOGA and PMOGA

[814]

Carlos M. Fonseca and Peter J. Fleming.
On the Performance Assessment and Comparison of Stochastic
Multiobjective Optimizers.
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[815]

Carlos M. Fonseca and Peter J. Fleming.
Multiobjective Optimization and Multiple Constraint Handling
with Evolutionary Algorithms (II): Application Example.
IEEE Transactions on Systems, Man, and Cybernetics – Part A,
28(1):38–44, January 1998.
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[816]

Carlos M. Fonseca and Peter J. Fleming.
Multiobjective Optimization and Multiple Constraint Handling
with Evolutionary Algorithms (I): A Unified Formulation.
IEEE Transactions on Systems, Man, and Cybernetics – Part A,
28(1):26–37, January 1998.
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[817]

Viviane Grunert da Fonseca and Carlos M. Fonseca.
The Relationship between the Covered Fraction, Completeness and
Hypervolume Indicators.
In J.K. Hao, P. Legrand, P. Collet, N. Monmarché, E. Lutton, and
M. Schoenauer, editors, Artificial Evolution: 10th International
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[818]

Carlos M. Fonseca, Viviane Grunert da Fonseca, and Luís Paquete.
Exploring the Performance of Stochastic Multiobjective
Optimisers with the SecondOrder Attainment Function.
In C. A. Coello Coello, A. Hernández Aguirre, and E. Zitzler,
editors, Evolutionary Multicriterion Optimization, EMO 2005, volume
3410 of Lecture Notes in Computer Science, pp. 250–264. Springer,
Heidelberg, 2005.
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The attainment function has been proposed as a
measure of the statistical performance of stochastic
multiobjective optimisers which encompasses both the
quality of individual nondominated solutions in
objective space and their spread along the tradeoff
surface. It has also been related to results from
random closedset theory, and cast as a meanlike,
firstorder moment measure of the outcomes of
multiobjective optimisers. In this work, the use of
more informative, secondorder moment measures for
the evaluation and comparison of multiobjective
optimiser performance is explored experimentally,
with emphasis on the interpretability of the
results.

[819]

Carlos M. Fonseca, Andreia P. Guerreiro, Manuel LópezIbáñez, and
Luís Paquete.
On the Computation of the Empirical Attainment Function.
In R. H. C. Takahashi et al., editors, Evolutionary
Multicriterion Optimization, EMO 2011, volume 6576 of Lecture Notes in
Computer Science, pp. 106–120. Springer, Heidelberg, 2011.
[ bib 
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The attainment function provides a description of the
location of the distribution of a random nondominated point
set. This function can be estimated from experimental data
via its empirical counterpart, the empirical attainment
function (EAF). However, computation of the EAF in more than
two dimensions is a nontrivial task. In this article, the
problem of computing the empirical attainment function is
formalised, and upper and lower bounds on the corresponding
number of output points are presented. In addition, efficient
algorithms for the two and threedimensional cases are
proposed, and their time complexities are related to lower
bounds derived for each case.

[820]

Carlos M. Fonseca, Luís Paquete, and Manuel LópezIbáñez.
An improved dimension sweep algorithm for the
hypervolume indicator.
In Proceedings of the 2006 Congress on Evolutionary Computation
(CEC 2006), pp. 1157–1163. IEEE Press, Piscataway, NJ, July 2006.
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This paper presents a recursive, dimensionsweep
algorithm for computing the hypervolume indicator of
the quality of a set of n nondominated points in
d>2 dimensions. It improves upon the existing HSO
(Hypervolume by Slicing Objectives) algorithm by
pruning the recursion tree to avoid repeated
dominance checks and the recalculation of partial
hypervolumes. Additionally, it incorporates a recent
result for the threedimensional special case. The
proposed algorithm achieves O(n^{d2} logn) time
and linear space complexity in the worstcase, but
experimental results show that the pruning
techniques used may reduce the time complexity
exponent even further.

[821]

Jorge Ramón Fonseca Cacho and Kazem Taghva.
The State of Reproducible Research in Computer Science.
In S. Latifi, editor, 17th International Conference on
Information TechnologyNew Generations (ITNG 2020), Advances in
Intelligent Systems and Computing, pp. 519–524. Springer International
Publishing, 2020.
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Reproducible research is the cornerstone of cumulative
science and yet is one of the most serious crisis that we
face today in all fields. This paper aims to describe the
ongoing reproducible research crisis along with
counterarguments of whether it really is a crisis, suggest
solutions to problems limiting reproducible research along
with the tools to implement such solutions by covering the
latest publications involving reproducible research.
Keywords: Docker, Improving transparency, OCR, Open science,
Replicability, Reproducibility

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Alexander I. J. Forrester and Andy J. Keane.
Recent advances in surrogatebased optimization.
Progress in Aerospace Sciences, 45(13):50–79, 2009.
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Keywords: Kriging; Gaussian Process; EGO; Design of Experiments

[823]

Manuel Förster, Bettina Bickel, Bernd Hardung, and Gabriella Kókai.
Selfadaptive ant colony optimisation applied to function
allocation in vehicle networks.
In D. Thierens et al., editors, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2007, pp. 1991–1998. ACM Press,
New York, NY, 2007.
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[824]

Michael Foster, Matthew Hughes, George O'Brien, Pietro S. Oliveto, James Pyle,
Dirk Sudholt, and James Williams.
Do sophisticated evolutionary algorithms perform better than
simple ones?
In C. A. Coello Coello, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2020, pp. 184–192, New York,
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[825]

Robert Fourer, David M. Gay, and Brian W. Kernighan.
AMPL: A Modeling Language for Mathematical Programming.
Duxbury, 2nd edition, 2002.
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[826]

Bennett L. Fox.
Uniting probabilistic methods for optimization.
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[827]

Bennett L. Fox.
Integrating and accelerating tabu search, simulated annealing,
and genetic algorithms.
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[828]

Bennett L. Fox.
Simulated annealing: folklore, facts, and directions.
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Peter I. Frazier.
A Tutorial on Bayesian Optimization.
Arxiv preprint arXiv:1807.02811, 2018.
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[830]

Alberto Franzin.
Empirical Analysis of Stochastic Local Search Behaviour:
Connecting Structure, Components and Landscape.
PhD thesis, IRIDIA, École polytechnique, Université Libre de
Bruxelles, Belgium, 2021.
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[831]

Alberto Franzin.
Empirical Analysis of Stochastic Local Search Behaviour:
Connecting Structure, Components and Landscape.
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[832]

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G. Francesca, M. Brambilla, A. Brutschy, Vito Trianni, and Mauro Birattari.
AutoMoDe: A Novel Approach to the Automatic Design of Control
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[834]

Gianpiero Francesca, Manuele Brambilla, Arne Brutschy, Lorenzo Garattoni, Roman
Miletitch, Gaetan Podevijn, Andreagiovanni Reina, Touraj Soleymani, Mattia
Salvaro, Carlo Pinciroli, Franco Mascia, Vito Trianni, and Mauro Birattari.
AutoMoDeChocolate: Automatic Design of Control Software for
Robot Swarms.
Swarm Intelligence, 2015.
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[835]

Jose M. Framiñán, Jatinder N.D. Gupta, and Rainer Leisten.
A Review and Classification of Heuristics for Permutation
Flowshop Scheduling with Makespan Objective.
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Alberto Franzin, Raphaël Gyory, JeanCharles Nadé, Guillaume Aubert,
Georges Klenkle, and Hughes Bersini.
Philéas: Anomaly Detection for IoT Monitoring.
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[837]

Jose M. Framiñán, Rainer Leisten, and Rubén Ruiz.
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Springer, New York, NY, 2014.
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[838]

Alberto Franzin, Leslie Pérez Cáceres, and Thomas Stützle.
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Algorithm Configuration.
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[839]

Alberto Franzin, Francesco Sambo, and Barbara Di Camillo.
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Alberto Franzin and Thomas Stützle.
Exploration of Metaheuristics through Automatic Algorithm
Configuration Techniques and Algorithmic Frameworks.
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Alberto Franzin and Thomas Stützle.
Comparison of Acceptance Criteria in Randomized Local Searches.
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Alberto Franzin and Thomas Stützle.
Revisiting Simulated Annealing: a ComponentBased Analysis:
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Alberto Franzin and Thomas Stützle.
Revisiting Simulated Annealing: A ComponentBased Analysis.
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[844]

Alberto Franzin and Thomas Stützle.
Towards transferring algorithm configurations across problems.
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pp. 1–6, 2020.
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Alberto Franzin and Thomas Stützle.
A causal framework for understanding optimisation algorithms.
In F. Heintz, M. Milano, and B. O'Sullivan, editors, Trustworthy
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Cham, Switzerland, 2021.
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[846]

Alberto Franzin and Thomas Stützle.
A Landscapebased Analysis of Fixed Temperature and Simulated
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Alberto Franzin and Thomas Stützle.
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Brendan J. Frey and Delbert Dueck.
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A. R. R. Freitas, Peter J. Fleming, and Frederico G. Guimarães.
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Hela Frikha, Habib Chabchoub, and JeanMarc Martel.
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Tobias Friedrich, Andreas Göbel, Francesco Quinzan, and Markus Wagner.
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A core feature of evolutionary algorithms is their mutation
operator. Recently, much attention has been devoted to the
study of mutation operators with dynamic and nonuniform
mutation rates. Following up on this line of work, we propose
a new mutation operator and analyze its performance on the
(1+1) Evolutionary Algorithm (EA). Our analyses show that
this mutation operator competes with preexisting ones, when
used by the (1+1)EA on classes of problems for which
results on the other mutation operators are available. We
present a “jump” function for which the performance of the
(1+1)EA using any static uniform mutation and any restart
strategy can be worse than the performance of the (1+1)EA
using our mutation operator with no restarts. We show that
the (1+1)EA using our mutation operator finds a
(1/3)approximation ratio on any nonnegative submodular
function in polynomial time. This performance matches that of
combinatorial local search algorithms specifically designed
to solve this problem.

[854]

Matteo Frigo and Steven G. Johnson.
The Design and Implementation of FFTW3.
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[855]

Tobias Friedrich, Timo Kötzing, Martin S. Krejca, and Andrew M. Sutton.
Robustness of Ant Colony Optimization to Noise.
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Tobias Friedrich, Timo Kötzing, and Markus Wagner.
A Generic BetandRun Strategy for Speeding Up Stochastic Local
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Tobias Friedrich, Francesco Quinzan, and Markus Wagner.
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Keywords: combinatorial optimization, heavytailed mutation,
singleobjective optimization, experimentsmotivated theory,
irace

[858]

Milton Friedman.
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Alex S. Fukunaga.
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Satisfiability testing (SAT) is a very active area
of research today, with numerous realworld
applications. We describe CLASS2.0, a genetic
programming system for semiautomatically designing
SAT local search heuristics. An empirical
comparison shows that that the heuristics generated
by our GP system outperform the state of the art
humandesigned local search algorithms, as well as
previously proposed evolutionary approaches, with
respect to both runtime as well as search efficiency
(number of variable flips to solve a problem).

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

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Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon, Zbigniew Chamski, Olivier
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Courtois, Francois Bodin, Phil Barnard, Elton Ashton, Edwin Bonilla, John
Thomson, Christopher K. I. Williams, and Michael O'Boyle.
Milepost GCC: Machine Learning Enabled Selftuning Compiler.
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D. Gaertner and K. Clark.
On Optimal Parameters for Ant Colony Optimization Algorithms.
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Matteo Gagliolo and Catherine Legrand.
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Algorithm selection is typically based on models of
algorithm performance,learned during a separate
offline training sequence, which can be
prohibitively expensive. In recent work, we adopted
an online approach, in which models of the runtime
distributions of the available algorithms are
iteratively updated and used to guide the allocation
of computational resources, while solving a sequence
of problem instances. The models are estimated using
survival analysis techniques, which allow us to
reduce computation time, censoring the runtimes of
the slower algorithms. Here, we review the
statistical aspects of our online selection method,
discussing the bias induced in the runtime
distributions (RTD) models by the competition of
different algorithms on the same problem instances.

[871]

Caroline Gagné, W. L. Price, and M. Gravel.
Comparing an ACO algorithm with other heuristics for the
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Philippe Galinier and JinKao Hao.
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Tomas Gal and Heiner Leberling.
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Suppose that in a multicriteria linear programming problem
among the given objective functions there are some which can
be deleted without influencing the set E of all efficient
solutions. Such objectives are said to be
redundant. Introducing systems of objective functions which
realize their individual optimum in a single vertex of the
polyhedron generated by the restriction set, the notion of
relative or absolute redundant objectives is defined. A
theory which describes properties of absolute and relative
redundant objectives is developed. A method for determining
all the relative and absolute redundant objectives, based on
this theory, is given. Illustrative examples demonstrate the
procedure.

[874]

L. M. Gambardella and Marco Dorigo.
Ant Colony System Hybridized with a New Local Search for the
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L. M. Gambardella and Marco Dorigo.
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L. M. Gambardella and Marco Dorigo.
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L. M. Gambardella, Roberto Montemanni, and Dennis Weyland.
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L. M. Gambardella, Éric D. Taillard, and G. Agazzi.
MACSVRPTW: A Multiple Ant Colony System for Vehicle Routing
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Xavier Gandibleux, X. Delorme, and V. T'Kindt.
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[880]

Xavier Gandibleux, Andrzej Jaszkiewicz, A. Fréville, and Roman
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Special Issue on Multiple Objective Metaheuristics.
Journal of Heuristics, 6(3), 2000.
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Xavier Gandibleux, N. Mezdaoui, and A. Fréville.
A tabu search procedure to solve multiobjective combinatorial
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Xavier Gandibleux, H. Morita, and N. Katoh.
Use of a genetic heritage for solving the assignment problem
with two objectives.
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[883]

Kaizhou Gao, Yicheng Zhang, Ali Sadollah, and Rong Su.
Optimizing urban traffic light scheduling problem using harmony
search with ensemble of local search.
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[884]

Huiru Gao, Haifeng Nie, and Ke Li.
Visualisation of Pareto Front Approximation: A Short Survey
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Huiru Gao, Haifeng Nie, and Ke Li.
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José GarcíaNieto, Enrique Alba, and Ana Carolina Olivera.
Swarm intelligence for traffic light scheduling: Application to
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optimization,Realistic traffic instances,SUMO microscopic
simulator of urban mobility,Traffic light scheduling

[887]

Carlos GarcíaMartínez, Oscar Cordón, and Francisco Herrera.
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The performance of stochastic optimisers can be assessed
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Multiobjective optimization has recently emerged as a useful
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To the best of our knowledge, this paper describes the first
ant colony optimization (ACO) approach applied to nurse
scheduling, analyzing a dynamic regional problem which is
currently under discussion at the Vienna hospital
compound. Each day, pool nurses have to be assigned for the
following days to public hospitals while taking into account
a variety of soft and hard constraints regarding working date
and time, working patterns, nurses qualifications, nurses
and hospitals preferences, as well as costs. Extensive
computational experiments based on a four week simulation
period were used to evaluate three different scenarios
varying the number of nurses and hospitals for six different
hospitals demand intensities. The results of our simulations
and optimizations reveal that the proposed ACO algorithm
achieves highly significant improvements compared to a greedy
assignment algorithm.

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Walter J. Gutjahr.
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Isabelle Guyon, Jason Weston, Stephen Barnhill, and Vladimir Vapnik.
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Heikki Haario, Eero Saksman, and Johanna Tamminen.
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Evert Haasdijk, Arif Attaul Qayyum, and Agoston E. Eiben.
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Apache Software Foundation.
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Prabhat Hajela and CY Lin.
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Bruce Hajek.
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George T. Hall, Pietro S. Oliveto, and Dirk Sudholt.
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[999]

George T. Hall, Pietro S. Oliveto, and Dirk Sudholt.
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George T. Hall, Pietro S. Oliveto, and Dirk Sudholt.
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George T. Hall, Pietro S. Oliveto, and Dirk Sudholt.
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Greg Hamerly and Charles Elkan.
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[1003]

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

[1004]

Raimo P. Hämäläinen, Jukka Luoma, and Esa Saarinen.
On the importance of behavioral operational research: The case
of understanding and communicating about dynamic systems.
European Journal of Operational Research, 228(3):623–634,
August 2013.
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We point out the need for Behavioral Operational Research
(BOR) in advancing the practice of OR. So far, in OR
behavioral phenomena have been acknowledged only in
behavioral decision theory but behavioral issues are always
present when supporting human problem solving by
modeling. Behavioral effects can relate to the group
interaction and communication when facilitating with OR
models as well as to the possibility of procedural mistakes
and cognitive biases. As an illustrative example we use well
known system dynamics studies related to the understanding of
accumulation. We show that one gets completely opposite
results depending on the way the phenomenon is described and
how the questions are phrased and graphs used. The results
suggest that OR processes are highly sensitive to various
behavioral effects. As a result, we need to pay attention to
the way we communicate about models as they are being
increasingly used in addressing important problems like
climate change.

[1005]

Horst W. Hamacher and Günter Ruhe.
On spanning tree problems with multiple objectives.
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Hayfa Hammami and Thomas Stützle.
A Computational Study of Neighborhood Operators for JobShop
Scheduling Problems with Regular Objectives.
In B. Hu and M. LópezIbáñez, editors, Proceedings
of EvoCOP 2017 – 17th European Conference on Evolutionary Computation in
Combinatorial Optimization, volume 10197 of Lecture Notes in Computer
Science, pp. 1–17. Springer, Heidelberg, 2017.
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[1007]

Michael Pilegaard Hansen.
Tabu search for multiobjective optimization: MOTS.
In J. Climaco, editor, Proceedings of the 13th International
Conference on Multiple Criteria Decision Making (MCDM'97), pp. 574–586.
Springer Verlag, 1997.
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[1008]

Nikolaus Hansen, Anne Auger, S. Finck, and R. Ros.
RealParameter BlackBox Optimization Benchmarking 2009:
Experimental setup.
Technical Report RR6828, INRIA, France, 2009.
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[1009]

Nikolaus Hansen, Anne Auger, Olaf Mersmann, Tea Tušar, and Dimo Brockhoff.
COCO: A platform for comparing continuous optimizers in a
blackbox setting.
Arxiv preprint arXiv:1603.08785, 2016.
Published as [1010].
[ bib ]

[1010]

Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar, and
Dimo Brockhoff.
COCO: A platform for comparing continuous optimizers in a
blackbox setting.
Optimization Methods and Software, 36(1):1–31, 2020.
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[1011]

Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen Finck, and Petr
Pošík.
Comparing Results of 31 Algorithms from the BlackBox
Optimization Benchmarking BBOB2009.
In M. Pelikan and J. Branke, editors, Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO Companion 2010, pp.
1689–1696. ACM Press, New York, NY, 2010.
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This paper presents results of the BBOB2009 benchmarking of
31 search algorithms on 24 noiseless functions in a blackbox
optimization scenario in continuous domain. The runtime of
the algorithms, measured in number of function evaluations,
is investigated and a connection between a single convergence
graph and the runtime distribution is uncovered. Performance
is investigated for different dimensions up to 40D, for
different target precision values, and in different subgroups
of functions. Searching in larger dimension and multimodal
functions appears to be more difficult. The choice of the
best algorithm also depends remarkably on the available
budget of function evaluations.
Keywords: benchmarking, blackbox optimization

[1012]

Nikolaus Hansen, Steffen Finck, Raymond Ros, and Anne Auger.
RealParameter BlackBox Optimization Benchmarking 2009:
Noiseless Functions Definitions.
Technical Report RR6829, INRIA, France, 2009.
Updated February 2010.
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http://coco.gforge.inria.fr/bbob2012downloads

[1013]

Michael Pilegaard Hansen and Andrzej Jaszkiewicz.
Evaluating the quality of approximations to the nondominated
set.
Technical Report IMMREP19987, Institute of Mathematical Modelling,
Technical University of Denmark, Lyngby, Denmark, 1998.
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Pierre Hansen and B. Jaumard.
Algorithms for the Maximum Satisfiability Problem.
Computing, 44:279–303, 1990.
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[1015]

Julia Handl and Joshua D. Knowles.
Modes of Problem Solving with Multiple Objectives: Implications
for Interpreting the Pareto Set and for Decision Making.
In J. D. Knowles, D. Corne, K. Deb, and D. R. Chair, editors,
Multiobjective Problem Solving from Nature, Natural Computing Series, pp.
131–151. Springer, Berlin/Heidelberg, 2008.
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[1016]

Pierre Hansen and Nenad Mladenović.
Variable neighborhood search: Principles and applications.
European Journal of Operational Research, 130(3):449–467,
2001.
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[1017]

Pierre Hansen and Nenad Mladenović.
Variable Neighborhood Search.
In F. Glover and G. A. Kochenberger, editors, Handbook of
Metaheuristics, pp. 145–184. Kluwer Academic Publishers, Norwell, MA,
2002.
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[1018]

Pierre Hansen, Nenad Mladenović, Jack Brimberg, and José A. Moreno
Pérez.
Variable Neighborhood Search.
In M. Gendreau and J.Y. Potvin, editors, Handbook of
Metaheuristics, volume 146 of International Series in Operations
Research & Management Science, pp. 61–86. Springer, New York, NY, 2nd
edition, 2010.
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[1019]

Nikolaus Hansen and A. Ostermeier.
Completely derandomized selfadaptation in evolution
strategies.
Evolutionary Computation, 9(2):159–195, 2001.
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Keywords: CMAES

[1020]

Nikolaus Hansen, Raymond Ros, Nikolaus Mauny, Marc Schoenauer, and Anne Auger.
Impacts of invariance in search: When CMAES and PSO face
illconditioned and nonseparable problems.
Applied Soft Computing, 11(8):5755–5769, 2011.
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[1021]

Thomas Hanne.
On the convergence of multiobjective evolutionary algorithms.
European Journal of Operational Research, 117(3):553–564,
1999.
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[1022]

Thomas Hanne.
Global Multiobjective Optimization with Evolutionary Algorithms:
Selection Mechanisms and Mutation Control.
In E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, and
D. Corne, editors, Evolutionary Multicriterion Optimization, EMO 2001,
volume 1993 of Lecture Notes in Computer Science, pp. 197–212.
Springer, Heidelberg, 2001.
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[1023]

Thomas Hanne.
A multiobjective evolutionary algorithm for approximating the
efficient set.
European Journal of Operational Research, 176(3):1723–1734,
2007.
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[1024]

Michael Pilegaard Hansen.
Metaheuristics for multiple objective combinatorial
optimization.
PhD thesis, Institute of Mathematical Modelling, Technical University
of Denmark, March 1998.
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[1025]

Nikolaus Hansen.
The CMA evolution strategy: a comparing review.
In Towards a new evolutionary computation, pp. 75–102.
Springer, 2006.
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[1026]

Nikolaus Hansen.
Benchmarking a BIpopulation CMAES on the BBOB2009
function testbed.
In F. Rothlauf, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO Companion 2009, pp. 2389–2396.
ACM Press, New York, NY, 2009.
[ bib ]
Keywords: bipopcmaes

[1027]

Zhifeng Hao, Ruichu Cai, and Han Huang.
An Adaptive Parameter Control Strategy for ACO.
In Proceedings of the International Conference on Machine
Learning and Cybernetics, pp. 203–206. IEEE Press, 2006.
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Zhifeng Hao, Han Huang, Yong Qin, and Ruichu Cai.
An ACO Algorithm with Adaptive Volatility Rate of Pheromone
Trail.
In Y. Shi, G. D. van Albada, J. Dongarra, and P. M. A. Sloot,
editors, Computational Science – ICCS 2007, 7th International
Conference, Proceedings, Part IV, volume 4490 of Lecture Notes in
Computer Science, pp. 1167–1170. Springer, Heidelberg, 2007.
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JinKao Hao and Jêrome Pannier.
Simulated Annealing and Tabu Search for Constraint Solving.
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William D. Harvey and Matthew L. Ginsberg.
Limited Discrepancy Search.
In C. S. Mellish, editor, Proceedings of the 14th International
Joint Conference on Artificial Intelligence (IJCAI95), pp. 607–615.
Morgan Kaufmann Publishers, 1995.
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Douglas P. Hardin and Edward B. Saff.
Discretizing Manifolds via Minimum Energy Points.
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51(10):1186–1194, 2004.
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J. P. Hart and A. W. Shogan.
Semigreedy heuristics: An empirical study.
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Emma Hart and Kevin Sim.
A HyperHeuristic Ensemble Method for Static JobShop
Scheduling.
Evolutionary Computation, 24(4):609–635, 2016.
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[1034]

Kazuya Haraguchi.
Iterated Local Search with TrellisNeighborhood for the
Partial Latin Square Extension Problem.
Journal of Heuristics, 22(5):727–757, 2016.
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Sameer Hasija and Chandrasekharan Rajendran.
Scheduling in flowshops to minimize total tardiness of jobs.
International Journal of Production Research,
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[1036]

Hideki Hashimoto, Mutsunori Yagiura, and Toshihide Ibaraki.
An Iterated Local Search Algorithm for the Timedependent
Vehicle Routing Problem with Time Windows.
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Hado van Hasselt, Arthur Guez, and David Silver.
Deep Reinforcement Learning with Double QLearning.
In D. Schuurmans and M. P. Wellman, editors, Proceedings of the
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Simon Haykin.
A comprehensive foundation.
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Öncü Hazir, Yavuz Günalay, and Erdal Erel.
Customer order scheduling problem: a comparative metaheuristics
study.
International Journal of Advanced Manufacturing Technology,
37(5):589–598, May 2008.
[ bib 
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The customer order scheduling problem (COSP) is defined as
to determine the sequence of tasks to satisfy the demand of
customers who order several types of products produced on a
single machine. A setup is required whenever a product type
is launched. The objective of the scheduling problem is to
minimize the average customer order flow time. Since the
customer order scheduling problem is known to be strongly
NPhard, we solve it using four major metaheuristics and
compare the performance of these heuristics, namely,
simulated annealing, genetic algorithms, tabu search, and ant
colony optimization. These are selected to represent various
characteristics of metaheuristics: natureinspired
vs. artificially created, populationbased vs. local search,
etc. A set of problems is generated to compare the solution
quality and computational efforts of these heuristics.
Results of the experimentation show that tabu search and ant
colony perform better for large problems whereas simulated
annealing performs best in smallsize problems. Some
conclusions are also drawn on the interactions between
various problem parameters and the performance of the
heuristics.
Keywords: ACO,Customer order scheduling,Genetic
algorithms,Metaheuristics,Simulated annealing,Tabu
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Xin He, Kaiyong Zhao, and Xiaowen Chu.
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Verena HeidrichMeisner and Christian Igel.
Hoeffding and Bernstein races for selecting policies in
evolutionary direct policy search.
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Proceedings of the 26th International Conference on Machine Learning, ICML
2009, pp. 401–408, New York, NY, 2009. ACM Press.
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Keywords: automated algorithm configuration, CMAES, racing

[1042]

Keld Helsgaun.
Source Code of the LinKernighanHelsgaun Traveling
Salesman Heuristic.
http://webhotel4.ruc.dk/~keld/research/LKH/, 2018.
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Keld Helsgaun.
Efficient Recombination in the LinKernighanHelsgaun
Traveling Salesman Heuristic.
In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete,
and D. Whitley, editors, Parallel Problem Solving from Nature – PPSN
XV, volume 11101 of Lecture Notes in Computer Science, pp. 95–107.
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Michael Held and Richard M. Karp.
The TravelingSalesman Problem and Minimum Spanning Trees.
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Christoph Helmberg and Franz Rendl.
Solving quadratic (0,1)problems by semidefinite programs and
cutting planes.
Mathematical Programming, 82(3):291–315, 1998.
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[1046]

Keld Helsgaun.
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Salesman Heuristic.
European Journal of Operational Research, 126:106–130, 2000.
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Keld Helsgaun.
General kopt Submoves for the LinKernighan TSP
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[1048]

Pascal van Hentenryck.
The OPL optimization programming language.
MIT Press, Cambridge, MA, 1999.
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[1049]

Darrall Henderson, Sheldon H. Jacobson, and Alan W. Johnson.
The Theory and Practice of Simulated Annealing.
In F. Glover and G. A. Kochenberger, editors, Handbook of
Metaheuristics, pp. 287–319. Springer, Boston, MA, 2003.
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[1050]

Pascal van Hentenryck and Laurent D. Michel.
Constraintbased Local Search.
MIT Press, Cambridge, MA, 2005.
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[1051]

Pascal van Hentenryck and Laurent D. Michel.
Synthesis of constraintbased local search algorithms from
highlevel models.
In R. C. Holte and A. Howe, editors, Proceedings of the AAAI
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[1052]

Michael A. Heroux.
Editorial: ACM TOMS Replicated Computational Results
Initiative.
ACM Transactions on Mathematical Software, 41(3):1–5, June
2015.
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[1053]

H. Hernández and Christian Blum.
Ant colony optimization for multicasting in static wireless
adhoc networks.
Swarm Intelligence, 3(2):125–148, 2009.
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[1054]

Francisco Herrera, Manuel Lozano, and Daniel Molina.
Test suite for the special issue of Soft Computing on
scalability of evolutionary algorithms and other metaheuristics for large
scale continuous optimization problems.
http://sci2s.ugr.es/eamhco/, 2010.
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Keywords: SOCO benchmark

[1055]

Francisco Herrera, Manuel Lozano, and A. M. Sánchez.
A taxonomy for the crossover operator for realcoded genetic
algorithms: An experimental study.
International Journal of Intelligent Systems, 18(3):309–338,
2003.
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Francisco Herrera, Manuel Lozano, and J. L. Verdegay.
Tackling RealCoded Genetic Algorithms: Operators and Tools for
Behavioural Analysis.
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spaces, mutation, recombination

[1057]

Jano I. van Hemert.
Evolving Combinatorial Problem Instances That Are Difficult to
Solve.
Evolutionary Computation, 14(4):433–462, 2006.
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This paper demonstrates how evolutionary computation can be
used to acquire difficult to solve combinatorial problem
instances. As a result of this technique, the corresponding
algorithms used to solve these instances are
stresstested. The technique is applied in three important
domains of combinatorial optimisation, binary constraint
satisfaction, Boolean satisfiability, and the travelling
salesman problem. The problem instances acquired through this
technique are more difficult than the ones found in popular
benchmarks. In this paper, these evolved instances are
analysed with the aim to explain their difficulty in terms of
structural properties, thereby exposing the weaknesses of
corresponding algorithms.

[1058]

Robert Heumüller, Sebastian Nielebock, Jacob Krüger, and Frank
Ortmeier.
Publish or perish, but do not forget your software artifacts.
Empirical Software Engineering, 25(6):4585–4616, 2020.
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Daniel P Heyman and Matthew J Sobel.
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Christian Hicks.
A Genetic Algorithm tool for optimising cellular or functional
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International Journal of Production Economics, 104(2):598–614,
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Geoffrey E. Hinton and Ruslan R. Salakhutdinov.
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Wassily Hoeffding.
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J. Holland.
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I. Hong, A. B. Kahng, and B. R. Moon.
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John N. Hooker.
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Giles Hooker.
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Holger H. Hoos and Thomas Stützle.
Stochastic Local Search—Foundations and Applications.
Morgan Kaufmann Publishers, San Francisco, CA, 2005.
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[1072]

Holger H. Hoos and Thomas Stützle.
Evaluating Las Vegas Algorithms — Pitfalls and Remedies.
In G. F. Cooper and S. Moral, editors, Proceedings of the
Fourteenth Conference on Uncertainty in Artificial Intelligence, pp.
238–245. Morgan Kaufmann Publishers, San Francisco, CA, 1998.
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[1073]

Holger H. Hoos and Thomas Stützle.
On the Empirical Scaling of Runtime for Finding Optimal
Solutions to the Traveling Salesman Problem.
European Journal of Operational Research, 238(1):87–94, 2014.
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[1074]

Holger H. Hoos and Thomas Stützle.
On the Empirical Time Complexity of Finding Optimal Solutions
vs. Proving Optimality for Euclidean TSP Instances.
Optimization Letters, 9(6):1247–1254, 2015.
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[1075]

Holger H. Hoos.
Programming by Optimisation: Towards a new Paradigm for
Developing HighPerformance Software.
In MIC 2011, the 9th Metaheuristics International Conference,
2011.
Plenary talk.
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[1076]

Holger H. Hoos.
Automated Algorithm Configuration and Parameter Tuning.
In Y. Hamadi, E. Monfroy, and F. Saubion, editors, Autonomous
Search, pp. 37–71. Springer, Berlin, Germany, 2012.
[ bib 
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[1077]

Holger H. Hoos.
Programming by optimization.
Communications of the ACM, 55(2):70–80, February 2012.
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[1078]

Christian Horoba and Frank Neumann.
Benefits and drawbacks for the use of epsilondominance in
evolutionary multiobjective optimization.
In C. Ryan, editor, Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2008, pp. 641–648. ACM Press, New York, NY,
2008.
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Proposed εbox

[1079]

J. Horn, N. Nafpliotis, and David E. Goldberg.
A niched Pareto genetic algorithm for multiobjective
optimization.
In Proceedings of the 1994 World Congress on Computational
Intelligence (WCCI 1994), pp. 82–87, Piscataway, NJ, June 1994. IEEE
Press.
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[1080]

Kenneth Hoste and Lieven Eeckhout.
Cole: Compiler Optimization Level Exploration.
In M. L. Soffa and E. Duesterwald, editors, Proceedings of the
6th Annual IEEE/ACM International Symposium on Code Generation and
Optimization, CGO '08, pp. 165–174, New York, NY, 2008. ACM Press.
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[1081]

Stela Pudar Hozo, Benjamin Djulbegovic, and Iztok Hozo.
Estimating the mean and variance from the median, range, and the
size of a sample.
BMC Medical Research Methodology, 5(1):13, 2005.
[ bib ]

[1082]

T. C. Hu, A. B. Kahng, and C.W. A. Tsao.
Old Bachelor Acceptance: A New Class of NonMonotone Threshold
Accepting Methods.
ORSA Journal on Computing, 7(4):417–425, 1995.
[ bib ]

[1083]

Wenbin Hu, Huan Wang, Zhenyu Qiu, Cong Nie, and Liping Yan.
A quantum particle swarm optimization driven urban traffic light
scheduling model.
Neural Computing & Applications, 2018.
[ bib 
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Keywords: BML,Optimization,Simulation,Traffic congestion,Updating
rules

[1084]

Wenbin Hu, Liping Yan, Huan Wang, Bo Du, and Dacheng Tao.
Realtime traffic jams prediction inspired by Biham,
Middleton and Levine (BML) model.
Information Sciences, 2017.
[ bib ]
Keywords: BML model,Prediction,Realtime,Traffic jam,Urban traffic
network

[1085]

Deng Huang, Theodore T. Allen, William I. Notz, and Ning Zeng.
Global Optimization of Stochastic BlackBox Systems via
Sequential Kriging MetaModels.
Journal of Global Optimization, 34(3):441–466, 2006.
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[1086]

Changwu Huang, Yuanxiang Li, and Xin Yao.
A Survey of Automatic Parameter Tuning Methods for
Metaheuristics.
IEEE Transactions on Evolutionary Computation, 24(2):201–216,
2020.
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[1087]

Han Huang, Xiaowei Yang, Zhifeng Hao, and Ruichu Cai.
A Novel ACO Algorithm with Adaptive Parameter.
In D.S. Huang, K. Li, and G. W. Irwin, editors, International
Conference on Computational Science (3), volume 4115 of Lecture Notes
in Computer Science, pp. 12–21. Springer, Heidelberg, 2006.
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[1088]

K. Huang, C. Yang, and K. Tseng.
Fast algorithms for finding the common subsequences of multiple
sequences.
In Proceedings of the International Computer Symposium, pp.
1006–1011. IEEE Press, 2004.
[ bib ]

[1089]

S. Huband, P. Hingston, L. Barone, and L. While.
A review of multiobjective test problems and a scalable test
problem toolkit.
IEEE Transactions on Evolutionary Computation, 10(5):477–506,
2006.
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[1090]

B. Huberman, R. Lukose, and T. Hogg.
An Economic Approach to Hard Computational Problems.
Science, 275:51–54, 1997.
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[1091]

D. L. HuertaMuñoz, R. Z. RíosMercado, and Rubén Ruiz.
An Iterated Greedy Heuristic for a Market Segmentation Problem
with Multiple Attributes.
European Journal of Operational Research, 261(1):75–87, 2017.
[ bib ]

[1092]

Evan J. Hughes.
Multiple single objective Pareto sampling.
In Proceedings of the 2003 Congress on Evolutionary Computation
(CEC'03), pp. 2678–2684, Piscataway, NJ, December 2003. IEEE Press.
[ bib ]

[1093]

Evan J. Hughes.
MSOPSII: A generalpurpose manyobjective optimiser.
In Proceedings of the 2007 Congress on Evolutionary Computation
(CEC 2007), pp. 3944–3951, Piscataway, NJ, 2007. IEEE Press.
[ bib ]

[1094]

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

[1095]

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

[1096]

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

[1097]

Maura Hunt and Manuel LópezIbáñez.
Modeling a DecisionMaker in Goal Programming by means of
Computational Rationality.
In I. Palomares, editor, International Alan Turing Conference on
Decision Support and Recommender systems, pp. 17–20, London, UK, November,
21–22 2019. Alan Turing Institute.
[ bib 
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This paper extends a simulation of cognitive mechanisms in
the context of multicriteria decisionmaking by using ideas
from computational rationality. Specifically, this paper
improves the simulation of a human decisionmaker (DM) by
considering how resource constraints impact their evaluation
process in an interactive Goal Programming problem. Our
analysis confirms and emphasizes a previous simulation study
by showing key areas that could be effected by cognitive
mechanisms. While the results are promising, the effects
should be validated by future experiments with human DMs.

[1098]

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

[1099]

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

[1100]

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

[1101]

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

[1102]

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

[1103]

Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Kevin P. Murphy.
An experimental investigation of modelbased parameter
optimisation: SPO and beyond.
In F. Rothlauf, editor, Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2009, pp. 271–278. ACM Press,
New York, NY, 2009.
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[1104]

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

[1105]

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.
[ bib ]

[1106]

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, pp. 507–523. Springer, Heidelberg,
2011.
[ bib 
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Keywords: SMAC,ROAR

[1107]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Parallel Algorithm Configuration.
In Y. Hamadi and M. Schoenauer, editors, Learning and
Intelligent Optimization, 6th International Conference, LION 6, volume 7219
of Lecture Notes in Computer Science, pp. 55–70. Springer,
Heidelberg, 2012.
[ bib ]

[1108]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Bayesian Optimization With Censored Response Data.
Arxiv preprint arXiv:1310.1947, 2013.
[ bib 
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[1109]

Frank Hutter, Holger H. Hoos, and Kevin LeytonBrown.
Identifying Key Algorithm Parameters and Instance Features using
Forward Selection.
In P. M. Pardalos and G. Nicosia, editors, Learning and
Intelligent Optimization, 7th International Conference, LION 7, volume 7997
of Lecture Notes in Computer Science, pp. 364–381. Springer,
Heidelberg, 2013.
[ bib 
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Keywords: parameter importance

[1110]

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
International Conference on Machine Learning, ICML 2014, volume 32, pp.
754–762, 2014.
[ bib 
http ]
Keywords: fANOVA, parameter importance

[1111]

Frank Hutter, Holger H. Hoos, Kevin LeytonBrown, and Kevin Murphy.
TimeBounded Sequential Parameter Optimization.
In C. Blum and R. Battiti, editors, Learning and Intelligent
Optimization, 4th International Conference, LION 4, volume 6073 of
Lecture Notes in Computer Science, pp. 281–298. Springer, Heidelberg,
2010.
[ bib 
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[1112]

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.
[ bib 
DOI ]

[1113]

Frank Hutter, Holger H. Hoos, and Thomas Stützle.
Automatic Algorithm Configuration Based on Local Search.
In R. C. Holte and A. Howe, editors, Proceedings of the AAAI
Conference on Artificial Intelligence, pp. 1152–1157. AAAI Press/
MIT Press, Menlo Park, CA, 2007.
[ bib ]

[1114]

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

[1115]

Frank Hutter, Manuel LópezIbáñez, Chris Fawcett, Marius Thomas
Lindauer, Holger H. Hoos, Kevin LeytonBrown, and Thomas Stützle.
AClib: A Benchmark Library for Algorithm Configuration.
In P. M. Pardalos, M. G. C. Resende, C. Vogiatzis, and J. L.
Walteros, editors, Learning and Intelligent Optimization, 8th
International Conference, LION 8, volume 8426 of Lecture Notes in
Computer Science, pp. 36–40. Springer, Heidelberg, 2014.
[ bib 
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[1116]

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

[1117]

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

[1118]

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

[1119]

Zhiyuan Liu and Jian Tang.
IJCAI 2021 Reproducibility Guidelines, 35th International Joint
Conference on Artificial Intelligence.
https://ijcai21.org/wpcontent/uploads/2020/12/20201226IJCAIReproducibility.pdf,
2021.
[ bib ]

[1120]

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.
[ bib 
epub ]

[1121]

Hao Wang, Diederick Vermetten, Furong Ye, Carola Doerr, and Thomas Bäck.
IOHanalyzer: Detailed Performance Analyses for Iterative
Optimization Heuristics.
ACM Transactions on Evolutionary Learning and Optimization,
2(1):3:1–3:29, 2022.
[ bib 
DOI ]

[1122]

Carola Doerr, Hao Wang, Furong Ye, Sander van Rijn, and Thomas Bäck.
IOHprofiler: A Benchmarking and Profiling Tool for Iterative
Optimization Heuristics.
Arxiv preprint arXiv:1806.07555, October 2018.
[ bib 
DOI ]
Keywords: Benchmarking; Heuristics

[1123]

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

[1124]

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

[1125]

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 ]

[1126]

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 [1488].
[ bib ]

[1127]

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 [1498].
[ bib ]

[1128]

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 [679].
[ bib 
http ]

[1129]

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 [682].
[ bib 
http ]

[1130]

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 [2185].
[ bib ]

[1131]

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 [685].
[ bib 
http ]

[1132]

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 ]

[1133]

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 [684].
[ bib 
http ]

[1134]

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 [1480].
[ bib 
http ]

[1135]

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 ]

[1136]

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 [1505].
[ bib 
http ]

[1137]

Tianjun Liao, Daniel Molina, Marco A. Montes de Oca, and Thomas Stützle.
A Note on the Effects of Enforcing Bound Constraints on
Algorithm Comparisons using the IEEE CEC'05 Benchmark Function Suite.
Technical Report TR/IRIDIA/2011010, IRIDIA, Université Libre de
Bruxelles, Belgium, 2011.
Published in Evolutionary Computation [1427].
[ bib 
http ]

[1138]

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 ]

[1139]

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 Operational
Research [1506].
[ bib ]

[1140]

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 [1915].
[ bib ]

[1141]

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 ]

[1142]

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 ]

[1143]

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 ]

[1144]

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

[1145]

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 ]

[1146]

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 ]

[1147]

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 [249].
[ bib 
http ]

[1148]

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

[1149]

Leonardo C. T. Bezerra, Manuel LópezIbáñez, and Thomas
Stützle.
Automatically Designing StateoftheArt Multi and
ManyObjective Evolutionary Algorithms.
Technical Report TR/IRIDIA/2018001, IRIDIA, Université Libre de
Bruxelles, Belgium, January 2018.
Published as [245].
[ bib 
http ]

[1150]

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

[1151]

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

[1152]

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

[1153]

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

[1154]

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

[1155]

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

[1156]

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

[1157]

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

[1158]

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

[1159]

Christian Igel.
Multiobjective Model Selection for Support Vector Machines.
In C. A. Coello Coello, A. Hernández Aguirre, and E. Zitzler,
editors, Evolutionary Multicriterion Optimization, EMO 2005, volume
3410 of Lecture Notes in Computer Science, pp. 534–546. Springer,
Heidelberg, 2005.
[ bib 
DOI ]
Early work on multiobjective hyperparameter optimization
(AutoML)

[1160]

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

[1161]

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

[1162]

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

[1163]

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

[1164]

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

[1165]

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

[1166]

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

[1167]

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

[1168]

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

[1169]

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

[1170]

Stefan Irnich.
A Unified Modeling and Solution Framework for Vehicle Routing
and Local SearchBased Metaheuristics.
INFORMS Journal on Computing, 20(2):270–287, 2008.
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[1171]

Ekhine Irurozki, Borja Calvo, and José A. Lozano.
Sampling and Learning Mallows and Generalized Mallows Models
Under the Cayley Distance.
Methodology and Computing in Applied Probability, 20(1):1–35,
June 2016.
[ bib 
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[1172]

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

[1173]

Ekhine Irurozki, Jesus Lobo, Aritz Perez, and Javier Del Ser.
Rank aggregation for nonstationary data streams.
Arxiv preprint arXiv:1910.08795 [stat.ML], 2020.
[ bib 
http ]
Keywords: uborda

[1174]

Ekhine Irurozki and Manuel LópezIbáñez.
Unbalanced Mallows Models for Optimizing Expensive BlackBox
Permutation Problems.
In F. Chicano and K. Krawiec, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, GECCO 2021, pp. 225–233.
ACM Press, New York, NY, 2021.
[ bib 
DOI 
supplementary material ]
Expensive blackbox combinatorial optimization problems arise
in practice when the objective function is evaluated by means
of a simulator or a realworld experiment. Since each fitness
evaluation is expensive in terms of time or resources, only a
limited number of evaluations is possible, typically several
orders of magnitude smaller than in nonexpensive
problems. In this scenario, classical optimization methods
such as mixedinteger programming and local search are not
useful. In the continuous case, Bayesian optimization, in
particular using Gaussian processes, has proven very
effective under these conditions. Much less research is
available in the combinatorial case. In this paper, we
propose and analyze UMM, an estimationofdistribution (EDA)
algorithm based on a Mallows probabilistic model and
unbalanced rank aggregation (uBorda). Experimental results on
blackbox versions of LOP and PFSP show that UMM is able to
match, and sometimes surpass, the solutions obtained by CEGO,
a Bayesian optimization algorithm for combinatorial
optimization. Moreover, the computational complexity of UMM
increases linearly with both the number of function
evaluations and the permutation size.
Keywords: UMM, Permutation, Expensive, Blackbox

[1175]

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

[1176]

Hisao Ishibuchi, N. Akedo, and Y. Nojima.
Behavior of Multiobjective Evolutionary Algorithms on
ManyObjective Knapsack Problems.
IEEE Transactions on Evolutionary Computation, 19(2):264–283,
2015.
[ bib 
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[1177]

Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, and Yusuke Nojima.
How to specify a reference point in hypervolume calculation for
fair performance comparison.
Evolutionary Computation, 26(3):411–440, 2018.
[ bib ]

[1178]

Hisao Ishibuchi, Hiroyuki Masuda, and Yusuke Nojima.
A Study on Performance Evaluation Ability of a Modified Inverted
Generational Distance Indicator.
In S. Silva and A. I. EsparciaAlcázar, editors,
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2015, pp. 695–702. ACM Press, New York, NY, 2015.
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[1179]

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

[1180]

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

[1181]

Hisao Ishibuchi, Yu Setoguchi, Hiroyuki Masuda, and Yusuke Nojima.
Performance of decompositionbased manyobjective algorithms
strongly depends on Pareto front shapes.
IEEE Transactions on Evolutionary Computation, 21(2):169–190,
2017.
[ bib ]

[1182]

Hisao Ishibuchi, N. Tsukamoto, and Y. Nojima.
Evolutionary manyobjective optimization: A short review.
In Proceedings of the 2008 Congress on Evolutionary Computation
(CEC 2008), pp. 2419–2426, Piscataway, NJ, 2008. IEEE Press.
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[1183]

Srikanth K. Iyer and Barkha Saxena.
Improved genetic algorithm for the permutation flowshop
scheduling problem.
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[1184]

Christopher H. Jackson.
MultiState Models for Panel Data: The msm Package
for R.
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[1185]

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Guidelines for Reporting Results of Computational Experiments.
Report of the Ad Hoc Committee.
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Larry W. Jacobs and Michael J. Brusco.
A Local Search Heuristic for Large SetCovering Problems.
Naval Research Logistics, 42(7):1129–1140, 1995.
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[1187]

Sophie Jacquin, Laetitia Jourdan, and ElGhazali Talbi.
Dynamic Programming Based Metaheuristic for Energy Planning
Problems.
In A. I. EsparciaAlcázar and A. M. Mora, editors,
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in Computer Science, pp. 165–176. Springer, Heidelberg, 2014.
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[1188]

Warren G. Jackson, Ender Özcan, and Robert I. John.
Move acceptance in local search metaheuristics for crossdomain
search.
Expert Systems with Applications, 109:131–151, 2018.
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Daniel M Jaeggi, Geoffrey T Parks, Timoleon Kipouros, and P John Clarkson.
The development of a multiobjective Tabu Search algorithm for
continuous optimisation problems.
European Journal of Operational Research, 185(3):1192–1212,
2008.
[ bib ]

[1190]

Antonio López Jaimes, Carlos A. Coello Coello, and Debrup Chakraborty.
Objective reduction using a feature selection technique.
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Computation Conference, GECCO 2008, pp. 673–680. ACM Press, New York, NY,
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[ bib ]

[1191]

Antonio López Jaimes, Carlos A. Coello Coello, and Jesús E.
Urías Barrientos.
Online Objective Reduction to Deal with ManyObjective
Problems.
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, pp. 423–437,
Heidelberg, 2009. Springer.
[ bib ]
In this paper, we propose and analyze two schemes to
integrate an objective reduction technique into a
multiobjective evolutionary algorithm (moea) in order to
cope with manyobjective problems. One scheme reduces
periodically the number objectives during the search until
the required objective subset size has been reached and,
towards the end of the search, the original objective set is
used again. The second approach is a more conservative scheme
that alternately uses the reduced and the entire set of
objectives to carry out the search. Besides improving
computational efficiency by removing some objectives, the
experimental results showed that both objective reduction
schemes also considerably improve the convergence of a moea
in manyobjective problems.

[1192]

Satish Jajodia, Ioannis Minis, George Harhalakis, and JeanMarie Proth.
CLASS: computerized layout solutions using simulated
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Kevin G. Jamieson and Ameet Talwalkar.
Nonstochastic Best Arm Identification and Hyperparameter
Optimization.
In A. Gretton and C. C. Robert, editors, Proceedings of the 19th
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[1194]

Andrzej Jaszkiewicz.
Genetic local search for multiobjective combinatorial
optimization.
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[1195]

Andrzej Jaszkiewicz.
ManyObjective Pareto Local Search.
European Journal of Operational Research, 271(3):1001–1013,
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Andrzej Jaszkiewicz and Jürgen Branke.
Interactive Multiobjective Evolutionary Algorithms.
In J. Branke, K. Deb, K. Miettinen, and R. Slowiński, editors,
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volume 5252 of Lecture Notes in Computer Science, pp. 179–193.
Springer, Heidelberg, 2008.
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Andrzej Jaszkiewicz, Hisao Ishibuchi, and Qingfu Zhang.
Multiobjective memetic algorithms.
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Andrzej Jaszkiewicz and Thibaut Lust.
NDtreebased update: a fast algorithm for the dynamic
nondominance problem.
IEEE Transactions on Evolutionary Computation, 22(5):778–791,
2018.
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Andrzej Jaszkiewicz.
On the performance of multipleobjective genetic local search on
the 0/1 knapsack problem – A comparative experiment.
IEEE Transactions on Evolutionary Computation, 6(4):402–412,
2002.
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Frank Hutter and Steve Ramage.
Manual for SMAC.
University of British Columbia, 2015.
SMAC version 2.10.03.
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M. T. Jensen.
Reducing the runtime complexity of multiobjective EAs: The
NSGAII and other algorithms.
IEEE Transactions on Evolutionary Computation, 7(5):503–515,
2003.
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Mark Jerrum and Alistair Sinclair.
The Markov chain Monte Carlo method: an approach to
approximate counting and integration.
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Mark Jerrum and Gregory Sorkin.
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[1204]

Mark Jerrum.
Large cliques elude the Metropolis process.
Random Structures & Algorithms, 3(4):347–359, 1992.
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Alexandre D. Jesus, Arnaud Liefooghe, Bilel Derbel, and Luís Paquete.
Algorithm Selection of Anytime Algorithms.
In C. A. Coello Coello, editor, Proceedings of the Genetic and
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S. Jiang, Y. S. Ong, J. Zhang, and L. Feng.
Consistencies and Contradictions of Performance Metrics in
Multiobjective Optimization.
IEEE Transactions on Cybernetics, 44(12):2391–2404, 2014.
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Yaochu Jin.
A Comprehensive Survey of Fitness Approximation in Evolutionary
Computation.
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[1208]

Yaochu Jin.
SurrogateAssisted Evolutionary Computation: Recent Advances and
Future Challenges.
Swarm and Evolutionary Computation, 1(2):61–70, June 2011.
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Surrogateassisted, or metamodel based evolutionary
computation uses efficient computational models, often known
as surrogates or metamodels, for approximating the fitness
function in evolutionary algorithms. Research on
surrogateassisted evolutionary computation began over a
decade ago and has received considerably increasing interest
in recent years. Very interestingly, surrogateassisted
evolutionary computation has found successful applications
not only in solving computationally expensive single or
multiobjective optimization problems, but also in addressing
dynamic optimization problems, constrained optimization
problems and multimodal optimization problems. This paper
provides a concise overview of the history and recent
developments in surrogateassisted evolutionary computation
and suggests a few future trends in this research area.
Keywords: Evolutionary computation,Expensive optimization
problems,Machine learning,Metamodels,Model
management,Surrogates

[1209]

Yaochu Jin, Handing Wang, Tinkle Chugh, Dan Guo, and Kaisa Miettinen.
DataDriven Evolutionary Optimization: An Overview and Case
Studies.
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Huidong Jin and ManLeung Wong.
Adaptive, convergent, and diversified archiving strategy for
multiobjective evolutionary algorithms.
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Journal of Heuristics. Policies on Heuristic Search Research.
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David S. Johnson.
Optimal Two and Threestage Production Scheduling with Setup
Times Included.
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David S. Johnson, Cecilia R. Aragon, Lyle A. McGeoch, and Catherine Schevon.
Optimization by Simulated Annealing: An Experimental Evaluation:
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David S. Johnson, Cecilia R. Aragon, Lyle A. McGeoch, and Catherine Schevon.
Optimization by Simulated Annealing: An Experimental Evaluation:
Part II, Graph Coloring and Number Partitioning.
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Alan W. Johnson and Sheldon H. Jacobson.
On the Convergence of Generalized Hill Climbing Algorithms.
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David S. Johnson and Lyle A. McGeoch.
Experimental Analysis of Heuristics for the STSP.
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David S. Johnson and Lyle A. McGeoch.
The Traveling Salesman Problem: A Case Study in Local
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Mark E. Johnson, Leslie M. Moore, and Donald Ylvisaker.
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David S. Johnson, Christos H. Papadimitriou, and M. Yannakakis.
How Easy is Local Search?
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David S. Johnson.
Local Optimization and the Traveling Salesman Problem.
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David S. Johnson, Lyle A. McGeoch, C. Rego, and Fred Glover.
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David S. Johnson.
A Theoretician's Guide to the Experimental Analysis of
Algorithms.
In M. H. Goldwasser, D. S. Johnson, and C. C. McGeoch, editors,
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Kenneth A. De Jong.
Evolutionary computation: a unified approach.
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Terry Jones and Stephanie Forrest.
Fitness Distance Correlation as a Measure of Problem Difficulty
for Genetic Algorithms.
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Donald R. Jones, Matthias Schonlau, and William J. Welch.
Efficient Global Optimization of Expensive BlackBox Functions.
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Proposed EGO algorithm
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[1228]

Kenneth A. De Jong and William M. Spears.
A formal analysis of the role of multipoint crossover in
genetic algorithms.
Annals of Mathematics and Artificial Intelligence, 5(1):1–26,
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D. E. Joslin and D. P. Clements.
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P. W. Jowitt and G. Germanopoulos.
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118(4):406–422, 1992.
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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
of the linearized network equations and constraints
and in studying the optimized network operation is
described. An application of the method to an
existing network in the United Kingdom is presented,
showing that considerable savings are possible. The
method was found to be robust and with low
computationtime requirements, and is therefore
suitable for realtime implementation.

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Angel A. Juan, Javier Faulin, Scott E. Grasman, Markus Rabe, and Gonçalo
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Angel A. Juan, Helena R. Lourenço, Manuel Mateo, Rachel Luo, and Quim
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Using Iterated Local Search for Solving the Flowshop Problem:
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A SamplingBased Heuristic for Tree Search Applied to Grammar
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What Have You Done for Me Lately? Adapting Operator
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Elena A. Kabova, Jason C. Cole, Oliver Korb, Manuel LópezIbáñez,
Adrian C. Williams, and Kenneth Shankland.
Improved performance of crystal structure solution from powder
diffraction data through parameter tuning of a simulated annealing
algorithm.
Journal of Applied Crystallography, 50(5):1411–1420, October
2017.
[ bib 
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Significant gains in the performance of the simulated
annealing algorithm in the DASH software package have
been realized by using the irace automatic
configuration tool to optimize the values of three key
simulated annealing parameters. Specifically, the success
rate in finding the global minimum in intensity χ^{2}
space is improved by up to an order of magnitude. The general
applicability of these revised simulated annealing parameters
is demonstrated using the crystal structure determinations of
over 100 powder diffraction datasets.
Keywords: crystal structure determination, powder diffraction,
simulated annealing, parameter tuning, irace

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Serdar Kadioglu, Yuri Malitsky, Meinolf Sellmann, and Kevin Tierney.
ISAC: InstanceSpecific Algorithm Configuration.
In H. Coelho, R. Studer, and M. Wooldridge, editors, Proceedings
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Daniel Kahneman and Amos Tversky.
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Daniel Kahneman.
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H. Kaji, Kokolo Ikeda, and Hajime Kita.
Avoidance of constraint violation for experimentbased
evolutionary multiobjective optimization.
In Proceedings of the 2009 Congress on Evolutionary Computation
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[ bib 
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Keywords: evolutionary computation;constraint
violation;experimentbased evolutionary multiobjective
optimization;evolutionary algorithm;riskyconstraint
violation;Constraint optimization;Diesel
engines;Calibration;Evolutionary computation;Electric
breakdown;Optimization methods;Uncertainty;Computational
fluid dynamics;Cost function;Temperature

[1241]

Qinma Kang, Hong He, and Jun Wei.
An Effective Iterated Greedy Algorithm for Reliabilityoriented
Task Allocation in Distributed Computing Systems.
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[1242]

Korhan Karabulut.
A hybrid iterated greedy algorithm for total tardiness
minimization in permutation flowshops.
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Dervis Karaboga and Bahriye Akay.
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Giorgos Karafotias, Agoston E. Eiben, and Mark Hoogendoorn.
Generic parameter control with reinforcement learning.
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[1245]

Giorgos Karafotias, Mark Hoogendoorn, and Agoston E. Eiben.
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[1246]

Giorgos Karafotias, Mark Hoogendoorn, and Agoston E. Eiben.
Evaluating reward definitions for parameter control.
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[1247]

İbrahim Karahan and Murat Köksalan.
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Keywords: TDEA

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Zohar Karnin, Tomer Koren, and Oren Somekh.
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Daniel Karapetyan, Andrew J. Parkes, and Thomas Stützle.
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Korhan Karabulut and Fatih M. Tasgetiren.
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The team orienteering problem (TOP) involves
finding a set of paths from the starting point to
the ending point such that the total collected
reward received from visiting a subset of locations
is maximized and the length of each path is
restricted by a prespecified limit. In this paper,
an ant colony optimization (ACO) approach is
proposed for the team orienteering problem. Four
methods, i.e., the sequential,
deterministicconcurrent and randomconcurrent and
simultaneous methods, are proposed to construct
candidate solutions in the framework of ACO. We
compare these methods according to the results
obtained on wellknown problems from the
literature. Finally, we compare the algorithm with
several existing algorithms. The results show that
our algorithm is promising.
Keywords: Ant colony optimization, Ant system, Heuristics,
Team orienteering problem

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

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Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz,
Heike Trautmann, and Michael T. M. Emmerich.
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Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz,
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V. Khare, Xin Yao, and Kalyanmoy Deb.
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M. Khichane, P. Albert, and Christine Solnon.
Integration of ACO in a Constraint Programming Language.
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M. Khichane, P. Albert, and Christine Solnon.
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A. R. KhudaBukhsh, Lin Xu, Holger H. Hoos, and Kevin LeytonBrown.
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Philip Kilby and Tommaso Urli.
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Youngmin Kim, Richard Allmendinger, and Manuel LópezIbáñez.
Safe Learning and Optimization Techniques: Towards a Survey of
the State of the Art.
Arxiv preprint arXiv:2101.09505 [cs.LG], 2020.
[ bib 
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Safe learning and optimization deals with learning and
optimization problems that avoid, as much as possible, the
evaluation of nonsafe input points, which are solutions,
policies, or strategies that cause an irrecoverable loss
(e.g., breakage of a machine or equipment, or life
threat). Although a comprehensive survey of safe
reinforcement learning algorithms was published in 2015, a
number of new algorithms have been proposed thereafter, and
related works in active learning and in optimization were not
considered. This paper reviews those algorithms from a number
of domains including reinforcement learning, Gaussian process
regression and classification, evolutionary algorithms, and
active learning. We provide the fundamental concepts on which
the reviewed algorithms are based and a characterization of
the individual algorithms. We conclude by explaining how the
algorithms are connected and suggestions for future
research.

[1290]

Youngmin Kim, Richard Allmendinger, and Manuel LópezIbáñez.
Safe Learning and Optimization Techniques: Towards a Survey of
the State of the Art.
In F. Heintz, M. Milano, and B. O'Sullivan, editors, Trustworthy
AI – Integrating Learning, Optimization and Reasoning. TAILOR 2020, volume
12641 of Lecture Notes in Computer Science, pp. 123–139. Springer,
Cham, Switzerland, 2021.
[ bib 
DOI ]
Safe learning and optimization deals with learning and
optimization problems that avoid, as much as possible, the
evaluation of nonsafe input points, which are solutions,
policies, or strategies that cause an irrecoverable loss
(e.g., breakage of a machine or equipment, or life
threat). Although a comprehensive survey of safe
reinforcement learning algorithms was published in 2015, a
number of new algorithms have been proposed thereafter, and
related works in active learning and in optimization were not
considered. This paper reviews those algorithms from a number
of domains including reinforcement learning, Gaussian process
regression and classification, evolutionary computing, and
active learning. We provide the fundamental concepts on which
the reviewed algorithms are based and a characterization of
the individual algorithms. We conclude by explaining how the
algorithms are connected and suggestions for future
research.

[1291]

Youngmin Kim, Richard Allmendinger, and Manuel LópezIbáñez.
Are Evolutionary Algorithms Safe Optimizers?
In J. E. Fieldsend and M. Wagner, editors, Proceedings of the
Genetic and Evolutionary Computation Conference, pp. 814–822. ACM Press,
New York, NY, 2022.
[ bib 
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We consider a type of constrained optimization problem, where
the violation of a constraint leads to an irrevocable loss,
such as breakage of a valuable experimental resource/platform
or loss of human life. Such problems are referred to as safe
optimization problems (SafeOPs). While SafeOPs have received
attention in the machine learning community in recent years,
there was little interest in the evolutionary computation
(EC) community despite some early attempts between 2009 and
2011. Moreover, there is a lack of acceptable guidelines on
how to benchmark different algorithms for SafeOPs, an area
where the EC community has significant experience in. Driven
by the need for more eficient algorithms and benchmark
guidelines for SafeOPs, the objective of this paper is to
reignite the interest of the EC community in this problem
class. To achieve this we (i) provide a formal definition of
SafeOPs and contrast it to other types of optimization
problems that the EC community is familiar with, (ii)
investigate the impact of key SafeOP parameters on the
performance of selected safe optimization algorithms, (iii)
benchmark EC against stateoftheart safe optimization
algorithms from the machine learning community, and (iv)
provide an opensource Python framework to replicate and
extend our work.
Keywords: Bayesian optimization, constrained optimization,
benchmarking, safety constraints, safe optimization

[1292]

Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi.
Bayesian Optimization with Approximate Set Kernels.
Machine Learning, 2021.
[ bib 
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We propose a practical Bayesian optimization method over
sets, to minimize a blackbox function that takes a set as a
single input. Because set inputs are permutationinvariant,
traditional Gaussian processbased Bayesian optimization
strategies which assume vector inputs can fall short. To
address this, we develop a Bayesian optimization method with
set kernel that is used to build surrogate
functions. This kernel accumulates similarity over set
elements to enforce permutationinvariance, but this comes at
a greater computational cost. To reduce this burden, we
propose two key components: (i) a more efficient approximate
set kernel which is still positivedefinite and is an
unbiased estimator of the true set kernel with upperbounded
variance in terms of the number of subsamples, (ii) a
constrained acquisition function optimization over sets,
which uses symmetry of the feasible region that defines a set
input. Finally, we present several numerical experiments
which demonstrate that our method outperforms other methods.

[1293]

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

[1295]

Scott Kirkpatrick and G. Toulouse.
Configuration Space Analysis of Travelling Salesman Problems.
Journal de Physique, 46(8):1277–1292, 1985.
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[1296]

Scott Kirkpatrick.
Optimization by Simulated Annealing: Quantitative Studies.
Journal of Statistical Physics, 34(56):975–986, 1984.
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[1297]

Scott Kirkpatrick, C. D. Gelatt, and M. P. Vecchi.
Optimization by Simulated Annealing.
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[1298]

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

[1299]

Joshua D. Knowles.
A summaryattainmentsurface plotting method for visualizing the
performance of stochastic multiobjective optimizers.
In A. Abraham and M. Paprzycki, editors, Proceedings of the 5th
International Conference on Intelligent Systems Design and Applications,
pp. 552–557, 2005.
[ bib 
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epub 
supplementary material ]
When evaluating the performance of a stochastic optimizer it is sometimes desirable to express performance in terms of the quality attained in a certain fraction of sample runs. For example, the sample median quality is the best estimator of what one would expect to achieve in 50% of runs, and similarly for other quantiles. In multiobjective optimization, the notion still applies but the outcome of a run is measured not as a scalar (i.e. the cost of the best solution), but as an attainment surface in kdimensional space (where k is the number of objectives). In this paper we report an algorithm that can be conveniently used to plot summary attainment surfaces in any number of dimensions (though it is particularly suited for three). A summary attainment surface is defined as the union of all tightest goals that have been attained (independently) in precisely s of the runs of a sample of n runs, for any s ∈1...n, and for any k. We also discuss the computational complexity of the algorithm and give some examples of its use. C code for the algorithm is available from the author.

[1300]

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

[1301]

Joshua D. Knowles.
Closedloop evolutionary multiobjective optimization.
IEEE Computational Intelligence Magazine, 4:77–91, 2009.
[ bib 
DOI ]
Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closedloop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg's terminology), the “phenotypes” are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Wellknown early work on artificial evolutiondesign engineering problems in fluid dynamics, and chemical plant process optimizationwas carried out in this experimental mode. More recently, the closedloop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closedloop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work are described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) onchip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral resource constraints; expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, Design of Experiments, machine learning and global optimization are also relevant to aspects of the closedloop problem, and may inspire futher development of multiobjective EAs.

[1302]

Joshua D. Knowles and David Corne.
Approximating the Nondominated Front Using the Pareto Archived
Evolution Strategy.
Evolutionary Computation, 8(2):149–172, 2000.
[ bib 
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[1303]

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

[1304]

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

[1305]

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

[1306]

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

[1307]

Joshua D. Knowles and David Corne.
Bounded Pareto Archiving: Theory and Practice.
In X. Gandibleux, M. Sevaux, K. Sörensen, and V. T'Kindt,
editors, Metaheuristics for Multiobjective Optimisation, volume 535 of
Lecture Notes in Economics and Mathematical Systems, pp. 39–64.
Springer, Berlin, Germany, 2004.
[ bib 
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[1308]

Joshua D. Knowles and David Corne.
Memetic algorithms for multiobjective optimization: issues,
methods and prospects.
In H. W. E., S. J. E., and K. N., editors, Recent Advances in
Memetic Algorithms, volume 166 of Studies in Fuzziness and Soft
Computing, pp. 313–352. Springer, Berlin/Heidelberg, 2005.
[ bib 
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[1309]

Joshua D. Knowles and David Corne.
Quantifying the Effects of Objective Space Dimension in
Evolutionary Multiobjective Optimization.
In S. Obayashi et al., editors, Evolutionary Multicriterion
Optimization, EMO 2007, volume 4403 of Lecture Notes in Computer
Science, pp. 757–771. Springer, Heidelberg, 2007.
[ bib ]
The scalability of EMO algorithms is an issue of significant
concern for both algorithm developers and users. A key aspect
of the issue is scalability to objective space dimension,
other things being equal. Here, we make some observations
about the efficiency of search in discrete spaces as a
function of the number of objectives, considering both
uncorrelated and correlated objective values. Efficiency is
expressed in terms of a cardinalitybased
(scalingindependent) performance indicator. Considering
random sampling of the search space, we measure, empirically,
the fraction of the true PF covered after p iterations, as
the number of objectives grows, and for different
correlations. A general analytical expression for the
expected performance of random search is derived, and is
shown to agree with the empirical results. We postulate that
for even moderately large numbers of objectives, random
search will be competitive with an EMO algorithm and show
that this is the case empirically: on a function where each
objective is relatively easy for an EA to optimize (an
NKlandscape with K=2), random search compares favourably to
a wellknown EMO algorithm when objective space dimension is
ten, for a range of interobjective correlation values. The
analytical methods presented here may be useful for
benchmarking of other EMO algorithms.

[1310]

Joshua D. Knowles, David Corne, and Kalyanmoy Deb.
Introduction: Problem solving, EC and EMO.
In J. D. Knowles, D. Corne, K. Deb, and D. R. Chair, editors,
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[1311]

Joshua D. Knowles, David Corne, and Mark Fleischer.
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In Proceedings of the 2003 Congress on Evolutionary Computation
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[1312]

Joshua D. Knowles, David Corne, and Alan P. Reynolds.
Noisy Multiobjective Optimization on a Budget of 250
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In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.K. Hao, and
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Springer, Heidelberg, 2009.
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[1313]

Joshua D. Knowles, Lothar Thiele, and Eckart Zitzler.
A tutorial on the performance assessment of stochastic
multiobjective optimizers.
TIKReport 214, Computer Engineering and Networks Laboratory (TIK),
Swiss Federal Institute of Technology (ETH), Zürich, Switzerland,
February 2006.
Revised version.
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preventive exposures.
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Joshua D. Knowles, Richard A. Watson, and David Corne.
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Springer, Heidelberg, 2001.
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Proposed multiobjectivization

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

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Gary A. Kochenberger, JinKao Hao, Fred Glover, Mark Lewis, Zhipeng Lü,
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Murat Köksalan.
Multiobjective Combinatorial Optimization: Some Approaches.
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Murat Köksalan and İbrahim Karahan.
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Rainer Kolisch and Sönke Hartmann.
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This paper considers heuristics for the wellknown
resourceconstrained project scheduling problem
(RCPSP). It provides an update of our survey which
was published in 2000. We summarize and categorize a
large number of heuristics that have recently been
proposed in the literature. Most of these heuristics
are then evaluated in a computational study and
compared on the basis of our standardized
experimental design. Based on the computational
results we discuss features of good heuristics. The
paper closes with some remarks on our test design
and a summary of the recent developments in research
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Vladlen Koltun and Christos H. Papadimitriou.
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Joshua B. Kollat and Patrick M. Reed.
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Keywords: glyph plot

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Introduced the Quadratic Assignment Problem (QAP)

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Mario Koppen and Kaori Yoshida.
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In this paper we consider a simple sequential
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decision maker has to accept or reject a series of
multiattributed outcomes. We show that using very
simple programming techniques, a great deal of the
decision making can be automated. The method might
be applicable to situations in which a dealer is
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[1328]

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On the “dimensionality curse” and the “selfsimilarity
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IEEE Transactions on Knowledge and Data Engineering,
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Spatial queries in highdimensional spaces have been studied
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important in many settings, including spatial databases (Find
the k closest cities) and multimedia databases (Find the k
most similar images). Previous analyses have concluded that
nearestneighbor search is hopeless in high dimensions due to
the notorious "curse of dimensionality". We show that this
may be overpessimistic. We show that what determines the
search performance (at least for Rtreelike structures) is
the intrinsic dimensionality of the data set and not the
dimensionality of the address space (referred to as the
embedding dimensionality). The typical (and often implicit)
assumption in many previous studies is that the data is
uniformly distributed, with independence between
attributes. However, real data sets overwhelmingly disobey
these assumptions; rather, they typically are skewed and
exhibit intrinsic ("fractal") dimensionalities that are much
lower than their embedding dimension, e.g. due to subtle
dependencies between attributes. We show how the Hausdorff
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Bioinspired algorithms, such as evolutionary algorithms and
ant colony optimization, are widely used for different
combinatorial optimization problems. These algorithms rely
heavily on the use of randomness and are hard to understand
from a theoretical point of view. This paper contributes to
the theoretical analysis of ant colony optimization and
studies this type of algorithm on one of the most prominent
combinatorial optimization problems, namely the traveling
salesperson problem (TSP). We present a new construction
graph and show that it has a stronger local property than one
commonly used for constructing solutions of the TSP. The
rigorous runtime analysis for two ant colony optimization
algorithms, based on these two construction procedures, shows
that they lead to good approximation in expected polynomial
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situations our algorithms get trapped in local optima and
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Application of heuristic solution procedures to the
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use. Furthermore, since no categorization process
was developed, it is assumed that once a rule is
selected it must be used throughout the whole
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A versatile and practical method of searching a parameter
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only available information is noisedistributed samples of
the function. At present, its usefulness is restricted to
optimization with respect to one system parameter. The
observations are taken sequentially; but, as opposed to the
gradient method, the observation may be located anywhere on
the parameter interval. A sequence of estimates of the
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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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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.
Computational results for an automatically tuned CMAES with
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Tuning Parameters across Mixed Dimensional Instances: A
Performance Scalability Study of SepGCMAES.
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Workshop on Scaling Behaviours of Landscapes,
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[1432]

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

[1433]

Tianjun Liao and Thomas Stützle.
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2013 benchmark set for realparameter optimization.
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Tianjun Liao, Thomas Stützle, Marco A. Montes de Oca, and Marco Dorigo.
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[1436]

Tianjun Liao.
Populationbased Heuristic Algorithms for Continuous and Mixed
DiscreteContinuous Optimization Problem.
PhD thesis, IRIDIA, École polytechnique, Université Libre de
Bruxelles, Belgium, 2013.
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Arnaud Liefooghe, Fabio Daolio, Bilel Derbel, Sébastien Verel,
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Multiobjective Optimization.
IEEE Transactions on Evolutionary Computation,
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Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Hernán E. Aguirre, and
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Arnaud Liefooghe, Jérémie Humeau, Salma Mesmoudi, Laetitia Jourdan, and
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On dominancebased multiobjective local search: design,
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This paper discusses simple local search approaches for
approximating the efficient set of multiobjective
combinatorial optimization problems. We focus on algorithms
defined by a neighborhood structure and a dominance relation
that iteratively improve an archive of nondominated
solutions. Such methods are referred to as dominancebased
multiobjective local search. We first provide a concise
overview of existing algorithms, and we propose a model
trying to unify them through a finegrained
decomposition. The main problemindependent search components
of dominance relation, solution selection, neighborhood
exploration and archiving are largely discussed. Then, a
number of stateoftheart and original strategies are
experimented on solving a permutation flowshop scheduling
problem and a traveling salesman problem, both on a two and
a threeobjective formulation. Experimental results and a
statistical comparison are reported in the paper, and some
directions for future research are highlighted.

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Arnaud Liefooghe, Laetitia Jourdan, and ElGhazali Talbi.
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Arnaud Liefooghe, Manuel LópezIbáñez, Luís Paquete, and
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Arnaud Liefooghe, Luís Paquete, Marco Simoes, and José Rui
Figueira.
Connectedness and Local Search for Bicriteria Knapsack
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Arnaud Liefooghe, Sébastien Verel, Benjamin Lacroix, AlexandruCiprian
Zavoianu, and John McCall.
Landscape features and automated algorithm selection for
multiobjective interpolated continuous optimisation problems.
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Arnaud Liefooghe, Sébastien Verel, Luís Paquete, and JinKao Hao.
Experiments on Local Search for Biobjective Unconstrained
Binary Quadratic Programming.
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Springer, Heidelberg, 2015.
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This article reports an experimental analysis on stochastic
local search for approximating the Pareto set of biobjective
unconstrained binary quadratic programming problems. First,
we investigate two scalarizing strategies that iteratively
identify a highquality solution for a sequence of
subproblems. Each subproblem is based on a static or
adaptive definition of weightedsum aggregation coefficients,
and is addressed by means of a stateoftheart
singleobjective tabu search procedure. Next, we design a
Pareto local search that iteratively improves a set of
solutions based on a neighborhood structure and on the Pareto
dominance relation. At last, we hybridize both classes of
algorithms by combining a scalarizing and a Pareto local
search in a sequential way. A comprehensive experimental
analysis reveals the high performance of the proposed
approaches, which substantially improve upon previous
bestknown solutions. Moreover, the obtained results show the
superiority of the hybrid algorithm over nonhybrid ones in
terms of solution quality, while requiring a competitive
computational cost. In addition, a number of structural
properties of the problem instances allow us to explain the
main difficulties that the different classes of local search
algorithms have to face.

[1447]

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

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Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, and Torsten Schaub.
AutoFolio: Algorithm Configuration for Algorithm Selection.
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Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter, and Torsten Schaub.
AutoFolio: An Automatically Configured Algorithm Selector.
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S. Lin and B. W. Kernighan.
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W. Ling and H. Luo.
An Adaptive Parameter Control Strategy for Ant Colony
Optimization.
In CIS'07: Proceedings of the 2007 International Conference on
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2007. IEEE Computer Society.
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Marius Thomas Lindauer, Jan N. van Rijn, and Lars Kotthoff.
The algorithm selection competitions 2015 and 2017.
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Andrei Lissovoi and Carsten Witt.
Runtime Analysis of Ant Colony Optimization on Dynamic Shortest
Path Problems.
Theoretical Computer Science, 561(Part A):73–85, 2015.
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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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Shusen Liu, Dan Maljovec, Bei Wang, PeerTimo Bremer, and Valerio Pascucci.
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IEEE Transactions on Visualization and Computer Graphics,
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Jiyin Liu and Colin R. Reeves.
Constructive and Composite Heuristic Solutions to the
P//ΣCi Scheduling Problem.
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Innovation 24.
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Andrea Lodi, Silvano Martello, and Michele Monaci.
Twodimensional packing problems: A survey.
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Heuristic and metaheuristic approaches for a class of
twodimensional bin packing problems.
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Andrea Lodi and Andrea Tramontani.
Performance Variability in MixedInteger Programming.
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Jason D. Lohn, Gregory S. Hornby, and Derek S. Linden.
Humancompetitive Evolved Antennas.
Artificial Intelligence for Engineering Design, Analysis and
Manufacturing, 22(3):235–247, 2008.
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Evolutionary optimization of antennas for NASA

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PoLing Loh and Sebastian Nowozin.
Faster Hoeffding Racing: Bernstein Races via Jackknife
Estimates.
In S. Jain, R. Munos, F. Stephan, and T. Zeugmann, editors,
Proceedings of Algorithmic Learning Theory, volume 8139 of Lecture
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Manuel LópezIbáñez.
High Performance Ant Colony Optimisation of the Pump Scheduling
Problem.
In P. Alberigo, G. Erbacci, F. Garofalo, and S. Monfardini, editors,
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[1467]

Manuel LópezIbáñez and Christian Blum.
BeamACO Based on Stochastic Sampling: A Case Study on the
TSP with Time Windows.
Technical Report LSI0828, Department LSI, Universitat
Politècnica de Catalunya, 2008.
Extended version published in Computers & Operations
Research [1470].
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[1468]

Manuel LópezIbáñez, Christian Blum, Dhananjay Thiruvady,
Andreas T. Ernst, and Bernd Meyer.
BeamACO based on stochastic sampling for makespan
optimization concerning the TSP with time windows.
In C. Cotta and P. Cowling, editors, Proceedings of EvoCOP 2009
– 9th European Conference on Evolutionary Computation in Combinatorial
Optimization, volume 5482 of Lecture Notes in Computer Science, pp.
97–108. Springer, Heidelberg, 2009.
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[1469]

Manuel LópezIbáñez and Christian Blum.
BeamACO Based on Stochastic Sampling: A Case Study on the
TSP with Time Windows.
In T. Stützle, editor, Learning and Intelligent
Optimization, Third International Conference, LION 3, volume 5851 of
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[1470]

Manuel LópezIbáñez and Christian Blum.
BeamACO for the travelling salesman problem with time
windows.
Computers & Operations Research, 37(9):1570–1583, 2010.
[ bib 
DOI ]
The travelling salesman problem with time windows is
a difficult optimization problem that arises, for
example, in logistics. This paper deals with the
minimization of the travelcost. For solving this
problem, this paper proposes a BeamACO algorithm,
which is a hybrid method combining ant colony
optimization with beam search. In general, BeamACO
algorithms heavily rely on accurate and
computationally inexpensive bounding information for
differentiating between partial solutions. This work
uses stochastic sampling as a useful alternative. An
extensive experimental evaluation on seven benchmark
sets from the literature shows that the proposed
BeamACO algorithm is currently a stateoftheart
technique for the travelling salesman problem with
time windows when travelcost optimization is
concerned.
Keywords: Ant colony optimization, Travelling salesman problem with
time windows, Hybridization

[1471]

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.
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[1472]

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

[1473]

Manuel LópezIbáñez, Jürgen Branke, and Luís Paquete.
Reproducibility in Evolutionary Computation.
ACM Transactions on Evolutionary Learning and Optimization,
1(4):1–21, 2021.
[ bib 
DOI 
epub ]
Experimental studies are prevalent in Evolutionary
Computation (EC), and concerns about the reproducibility and
replicability of such studies have increased in recent times,
reflecting similar concerns in other scientific fields. In
this article, we suggest a classification of different types
of reproducibility that refines the badge system of the
Association of Computing Machinery (ACM) adopted by TELO. We
discuss, within the context of EC, the different types of
reproducibility as well as the concepts of artifact and
measurement, which are crucial for claiming
reproducibility. We identify cultural and technical obstacles
to reproducibility in the EC field. Finally, we provide
guidelines and suggest tools that may help to overcome some
of these reproducibility obstacles.
Keywords: Evolutionary Computation, Reproducibility, Empirical study,
Benchmarking

[1474]

Manuel LópezIbáñez, Francisco Chicano, and Rodrigo GilMerino.
The Asteroid Routing Problem: A Benchmark for Expensive
BlackBox Permutation Optimization.
In J. L. Jiménez Laredo et al., editors, EvoApplications
2022: Applications of Evolutionary Computation, volume 13224 of Lecture
Notes in Computer Science, pp. 124–140. Springer Nature, Switzerland,
2022.
[ bib 
DOI 
epub 
supplementary material ]
Inspired by the recent 11th Global Trajectory Optimisation
Competition, this paper presents the asteroid routing problem
(ARP) as a realistic benchmark of algorithms for expensive
boundconstrained blackbox optimization in permutation
space. Given a set of asteroids' orbits and a departure
epoch, the goal of the ARP is to find the optimal sequence
for visiting the asteroids, starting from Earth's orbit, in
order to minimize both the cost, measured as the sum of the
magnitude of velocity changes required to complete the trip,
and the time, measured as the time elapsed from the departure
epoch until visiting the last asteroid. We provide
opensource code for generating instances of arbitrary sizes
and evaluating solutions to the problem. As a preliminary
analysis, we compare the results of two methods for expensive
blackbox optimization in permutation spaces, namely,
Combinatorial Efficient Global Optimization (CEGO), a
Bayesian optimizer based on Gaussian processes, and
Unbalanced Mallows Model (UMM), an estimationofdistribution
algorithm based on probabilistic Mallows models. We
investigate the best permutation representation for each
algorithm, either rankbased or orderbased. Moreover, we
analyze the effect of providing a good initial solution,
generated by a greedy nearest neighbor heuristic, on the
performance of the algorithms. The results suggest directions
for improvements in the algorithms being compared.
Keywords: Spacecraft Trajectory Optimization, Unbalanced Mallows Model,
Combinatorial Efficient Global Optimization, Estimation of
Distribution Algorithms, Bayesian Optimization

[1475]

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 ]

[1476]

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 ]

[1477]

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 [1475].
[ bib 
http ]

[1478]

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

[1479]

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, pp. 295–309.
Springer, Heidelberg, 2015.
[ bib 
DOI ]
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.

[1480]

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, pp. 46–60. Springer, Heidelberg, 2011.
[ bib 
DOI ]
In this paper, we examine the problem of maintaining
an approximation of the set of nondominated points
visited during a multiobjective optimization, a
problem commonly known as archiving. Most of the
currently available archiving algorithms are
reviewed, and what is known about their convergence
and approximation properties is summarized. The main
scenario considered is the restricted case where the
archive must be updated online as points are
generated one by one, and at most a fixed number of
points are to be stored in the archive at any one
time. In this scenario, the 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/link/IridiaTr2011001.pdf

[1481]

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, Part I, volume 7491 of Lecture
Notes in Computer Science, pp. 357–366. Springer, Heidelberg, 2012.
[ bib 
DOI ]

[1482]

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

[1483]

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, Parallel Problem Solving from Nature – PPSN XIII, volume
8672 of Lecture Notes in Computer Science, pp. 621–630. Springer,
Heidelberg, 2014.
[ bib 
DOI ]

[1484]

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), pp. 1–6, Gent, Belgium, 2013.
[ bib 
epub ]

[1485]

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, pp. 214–225. Springer, Heidelberg,
2004.
[ bib 
DOI ]

[1486]

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 [1487].
[ bib ]

[1487]

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 ]
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.

[1488]

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, pp. 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.

[1489]

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” [1488].
[ bib ]
Please cite the book chapter, not this.

[1490]

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 ]

[1491]

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

[1492]

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 ]

[1493]

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 
epub ]
Reducing energy consumption of water distribution
networks has never had more significance than today. The greatest
energy savings can be obtained by careful scheduling of operation of
pumps. Schedules can be defined either implicitly, in terms of other
elements of the network such as tank levels, or explicitly by
specifying the time during which each pump is on/off. The
traditional representation of explicit schedules is a string of
binary values with each bit representing pump on/off status during a
particular time interval. In this paper a new explicit
representation is presented. It is based on time controlled
triggers, where the maximum number of pump switches is specified
beforehand. In this representation a pump schedule is divided into a
series of integers with each integer representing the number of
hours for which a pump is active/inactive. This reduces the number
of potential schedules (search space) compared to the binary
representation. Ant colony optimization (ACO) is a stochastic
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.

[1494]

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.

[1495]

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, p. 176. ACM
Press, New York, NY, 2007.
[ bib 
DOI ]

[1496]

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, pp. 435–442. IEEE Press, Piscataway, NJ, September
2005.
[ bib 
DOI ]

[1497]

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, pp. 117–122,
University of Exeter, UK, September 2005.
[ bib ]

[1498]

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, pp. 134–145. Springer, Heidelberg, 2010.
[ bib 
DOI ]

[1499]

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, pp. 95–106. Springer, Heidelberg, 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.

[1500]

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, pp. 71–78. ACM Press,
New York, NY, 2010.
[ bib 
DOI ]
Over the last few years, there have been a number of
proposals of ant colony optimization (ACO)
algorithms for tackling multiobjective combinatorial
optimization problems. These proposals adapt ACO
concepts in various ways, for example, some use
multiple pheromone matrices and multiple heuristic
matrices and others use multiple ant colonies.
In
this article, we carefully examine several of the
most prominent of these proposals. In particular, we
identify commonalities among the approaches by
recasting the original formulation of the algorithms
in different terms. For example, several proposals
described in terms of multiple colonies can be cast
equivalently using a single ant colony, where ants
use different weights for aggregating the pheromone
and/or the heuristic information. We study
algorithmic choices for the various proposals and we
identify previously undetected tradeoffs in their
performance.

[1501]

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 [1500].
[ bib ]

[1502]

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

[1503]

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 ]

[1504]

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 ]

[1505]

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.

[1506]

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 
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.

[1507]

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, pp. 371–407. Springer International Publishing,
2018.
[ bib 
DOI 
supplementary material ]

[1508]

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 ]

[1509]

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

[1510]

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

[1511]

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, Part I, volume 7491 of Lecture
Notes in Computer Science, pp. 296–305. Springer, Heidelberg, 2012.
[ bib 
DOI ]

[1512]

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

[1513]

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 ]

[1514]

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

[1515]

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

[1516]

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

[1517]

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 ]

[1518]

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

[1519]

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

[1520]

Manuel Lozano, Fred Glover, Carlos GarcíaMartínez, Francisco J.
Rodríguez, and Rafael Martí.
Tabu Search with Strategic Oscillation for the Quadratic Minimum
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Manuel Lozano, Daniel Molina, and Carlos GarcíaMartínez.
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Andrew Lucas.
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Scott M. Lundberg and SuIn Lee.
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M. Lundy and A. Mees.
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Thibaut Lust and Jacques Teghem.
Twophase Pareto local search for the biobjective traveling
salesman problem.
Journal of Heuristics, 16(3):475–510, 2010.
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In this work, we present a method, called TwoPhase
Pareto Local Search, to find a good approximation of the
efficient set of the biobjective traveling salesman
problem. In the first phase of the method, an initial
population composed of a good approximation of the extreme
supported efficient solutions is generated. We use as second
phase a Pareto Local Search method applied to each solution
of the initial population. We show that using the combination
of these two techniques: good initial population generation
plus Pareto Local Search gives better results than
stateoftheart algorithms. Two other points are introduced:
the notion of ideal set and a simple way to produce
nearefficient solutions of multiobjective problems, by using
an efficient singleobjective solver with a data perturbation
technique.

[1527]

Thibaut Lust and Jacques Teghem.
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new approach.
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Thibaut Lust and Jacques Teghem.
The multiobjective multidimensional knapsack problem: a survey
and a new approach.
Arxiv preprint arXiv:1007.4063, 2010.
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Published as [1529]

[1529]

Thibaut Lust and Jacques Teghem.
The multiobjective multidimensional knapsack problem: a survey
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Thibaut Lust and Andrzej Jaszkiewicz.
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metaheuristics, TSP, Local search, Speedup techniques

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C. von Lücken, Benjamín Barán, and Carlos Brizuela.
A survey on multiobjective evolutionary algorithms for
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Robert John Lygoe.
Complexity reduction in highdimensional multiobjective
optimisation.
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Qingfu Zhang.
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Laurens van der Maaten and Geoffrey Hinton.
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Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew
Hausknecht, and Michael Bowling.
Revisiting the Arcade Learning Environment: Evaluation
Protocols and Open Problems for General Agents.
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January 2018.
[ bib ]
The Arcade Learning Environment (ALE) is an evaluation
platform that poses the challenge of building AI agents with
general competency across dozens of Atari 2600 games. It
supports a variety of different problem settings and it has
been receiving increasing attention from the scientific
community, leading to some highpro_le success stories such
as the much publicized Deep QNetworks (DQN). In this article
we take a big picture look at how the ALE is being used by
the research community. We show how diverse the evaluation
methodologies in the ALE have become with time, and highlight
some key concerns when evaluating agents in the ALE. We use
this discussion to present some methodological best practices
and provide new benchmark results using these best
practices. To further the progress in the field, we introduce
a new version of the ALE that supports multiple game modes
and provides a form of stochasticity we call sticky
actions. We conclude this big picture look by revisiting
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stateoftheart in various problems and highlighting
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Gunther Mäckle, Dragan A. Savic, and Godfrey A. Walters.
Application of Genetic Algorithms to Pump Scheduling for Water
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A simple Genetic Algorithm has been applied to the
scheduling of multiple pumping units in a water
supply system with the objective of minimising the
overall cost of the pumping operation, taking
advantage of storage capacity in the system and the
availability of off peak electricity tariffs. A
simple example shows that the method is easy to
apply and has produced encouraging preliminary
results

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Nateri K. Madavan.
Multiobjective optimization using a Pareto differential
evolution approach.
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M. Mahdavi, M. Fesanghary, and E. Damangir.
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This paper develops an Improved harmony search (IHS)
algorithm for solving optimization problems. IHS employs a
novel method for generating new solution vectors that
enhances accuracy and convergence rate of harmony search (HS)
algorithm. In this paper the impacts of constant parameters
on harmony search algorithm are discussed and a strategy for
tuning these parameters is presented. The IHS algorithm has
been successfully applied to various benchmarking and
standard engineering optimization problems. Numerical results
reveal that the proposed algorithm can find better solutions
when compared to HS and other heuristic or deterministic
methods and is a powerful search algorithm for various
engineering optimization problems.
Keywords: Global optimization, Heuristics, Harmony search algorithm,
Mathematical programming

[1540]

Guilherme B. Mainieri and Débora P. Ronconi.
New heuristics for total tardiness minimization in a flexible
flowshop.
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Satoshi Matsubara, Motomu Takatsu, Toshiyuki Miyazawa, Takayuki Shibasaki,
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A Digital Annealer (DA) is a dedicated architecture for
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used to express a wide variety of combinatorial optimization
problems. The DA uses Markov Chain Monte Carlo as a basic
search mechanism, accelerated by the hardware implementation
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A certifying algorithm is an algorithm that produces, with
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output y and the certificate w, and then checks, either
manually or by use of a program, that w proves that y is a
correct output for input x. In this way, he/she can be sure
of the correctness of the output without having to trust the
algorithm. We put forward the thesis that certifying
algorithms are much superior to noncertifying algorithms,
and that for complex algorithmic tasks, only certifying
algorithms are satisfactory. Acceptance of this thesis would
lead to a change of how algorithms are taught and how
algorithms are researched. The widespread use of certifying
algorithms would greatly enhance the reliability of
algorithmic software. We survey the state of the art in
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The scheduling of pumps for clean water distribution
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paper typically produces costs within 1% of a
linear programbased solution, and can incorporate
realistic nonlinear costs that may be hard to
incorporate in linear programming
formulations. These costs include pump switching and
maximum demand charges. A simplified model is
derived from a standard hydraulic simulator. An
initial schedule is produced by a descent
method. Twostage simulated annealing then produces
solutions in a few minutes. Iterative recalibration
ensures that the solution agrees closely with the
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James McDermott.
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Although experimental studies have been widely applied to the
investigation of algorithm performance, very little attention
has been given to experimental method in this area. This is
unfortunate, since much can be done to improve the quality of
the data obtained; often, much improvement may be needed for
the data to be useful. This paper gives a tutorial discussion
of two aspects of good experimental technique: the use of
variance reduction techniques and simulation speedups in
algorithm studies. In an illustrative study, application of
variance reduction techniques produces a decrease in variance
by a factor 1000 in one case, giving a dramatic improvement
in the precision of experimental results. Furthermore, the
complexity of the simulation program is improved from
Θ(m n/H_{n}) to Θ(m + n logn) (where m is
typically much larger than n), giving a much faster
simulation program and therefore more data per unit of
computation time. The general application of variance
reduction techniques is also discussed for a variety of
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selforganizing sequential search, statistical analysis of
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Catherine C. McGeoch.
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Two types of sampling plans are examined as alternatives to
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Nonlinear Multiobjective Optimization provides an extensive,
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(deterministic) multiobjective optimization, its methods, its
theory and its background. This book is intended for both
researchers and students in the areas of (applied)
mathematics, engineering, economics, operations research and
management science; it is meant for both professionals and
practitioners in many different fields of application. The
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We give an overview of interactive methods developed
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solution. In interactive methods, steps of an
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pheromone trail, which is a central element of this
metaheuristic. Many versions of the algorithm are
found in literature, the main distinction among them
being the management of the pheromone
trail. Nevertheless, few of them seek to perfect
learning by modifying the internal structure of the
trail. In this paper, a new pheromone trail
structure is proposed that is specifically adapted
to the type of constraints in the carsequencing
problem. The quality of the results obtained when
solving three sets of benchmark problems is superior
to that of the best solutions found in literature
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[1767]

Vesa Ojalehto, Dmitry Podkopaev, and Kaisa Miettinen.
Towards Automatic Testing of Reference Point Based Interactive
Methods.
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In this research, we proposed to build an automated framework
for testing interactive multiobjective optimization methods,
without utilizing a value function to represent the DM's
preferences. This was achieved by replacing the human DM with
an artificial DM constructed from two distinct parts: the
steady part and the current context. With the steady part the
artificial DM tries to maintain the search towards its
preferences, while at the same time the current context
allows changing the direction as well as ending the solution
process prematurely, mimicking actions of a human DM.
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Multiple objective programming provides a means of
aiding decision makers facing complex decisions where
tradeoffs among conflicting objectives must be
reconciled. Interactive multiobjective programming provides a
means for decision makers to learn what these tradeoffs
involve, while the mathematical program generates solutions
that seek improvement of the implied utility of the decision
maker. A variety of multiobjective programming techniques
have been presented in the multicriteria decisionmaking
literature. This study reviews published studies with human
subjects where some of these techniques were applied. While
all of the techniques have the ability to support decision
makers under conditions of multiple objectives, a number of
features in applying these systems have been tested by these
studies. A general evolution of techniques is traced,
starting with methods relying upon linear combinations of
value, to more recent methods capable of reflecting nonlinear
tradeoffs of value. Support of nonlinear utility and
enhancing decisionmaker learning are considered.
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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
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Federico Pagnozzi and Thomas Stützle.
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Federico Pagnozzi and Thomas Stützle.
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Automatic Design of Hybrid Stochastic Local Search Algorithms
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Federico Pagnozzi and Thomas Stützle.
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for permutation flowshop problems with additional constraints.
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Federico Pagnozzi and Thomas Stützle.
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algorithm design.
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Federico Pagnozzi and Thomas Stützle.
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for permutation flowshop problems with additional constraints.
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Federico Pagnozzi.
Automatic Design of Hybrid Stochastic Local Search Algorithms.
PhD thesis, IRIDIA, École polytechnique, Université Libre de
Bruxelles, Belgium, 2019.
[ bib ]
Supervised by Thomas Stützle

[1795]

Daniel Palhazi Cuervo, Peter Goos, Kenneth Sörensen, and Emely
Arráiz.
An Iterated Local Search Algorithm for the Vehicle Routing
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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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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
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QuanKe Pan, Ling Wang, and BaoHua Zhao.
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Sinno Jialin Pan and Qiang Yang.
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Christos H. Papadimitriou and M. Yannakakis.
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[1805]

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.

[1806]

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

[1807]

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

[1808]

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 [221].
[ 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

[1809]

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

[1810]

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 ]

[1811]

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, pp. 69–77. Springer, Berlin, Germany, 2009.
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[1812]

Luís Paquete and Thomas Stützle.
Design and analysis of stochastic local search for the
multiobjective traveling salesman problem.
Computers & Operations Research, 36(9):2619–2631, 2009.
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[1813]

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

[1814]

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, pp. 479–493. Springer,
Heidelberg, 2003.
[ bib ]

[1815]

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.

[1816]

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), pp. 739–746, Vienna, Austria, 2005.
[ bib ]
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.

[1817]

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, pp. 325–344. Springer, New York, NY, 2007.
[ bib 
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Stochastic Local Search (SLS) algorithms can be seen as being
composed of several algorithmic components, each playing some
specific role with respect to overall performance. This
article explores the application of experimental design
techniques to analyze the effect of components of SLS
algorithms for Multiobjective Combinatorial Optimization
problems, in particular for the Biobjective Quadratic
Assignment Problem. The analysis shows that there exists a
strong dependence between the choices for these components
and various instance features, such as the structure of the
input data and the correlation between the objectives.
PostConference Proceedings of the 6th Metaheuristics
International Conference (MIC 2005)

[1818]

S. N. Parragh, Karl F. Doerner, Richard F. Hartl, and Xavier Gandibleux.
A heuristic twophase solution approach for the multiobjective
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Rebecca Parsons and Mark Johnson.
A Case Study in Experimental Design Applied to Genetic
Algorithms with Applications to DNA Sequence Assembly.
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MoonWon Park and YeongDae Kim.
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R. S. Parpinelli, H. S. Lopes, and A. A. Freitas.
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R. O. Parreiras and J. A. Vascocelos.
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Gerald Paul.
Comparative performance of tabu search and simulated annealing
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J Paulli.
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Lucas Marcondes Pavelski, Myriam Regattieri Delgado, and MarieEléonore
Kessaci.
MetaLearning on Flowshop Using Fitness Landscape Analysis.
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Glen S. Peace.
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AddisonWesley, 1993.
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Judea Pearl and Elias Bareinboim.
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Judea Pearl.
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Judea Pearl.
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Juan A. Pedraza, Carlos GarcíaMartínez, Alberto Cano, and Sebastián
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Martín Pedemonte, Sergio Nesmachnow, and Héctor Cancela.
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Luciana R. Pedro and Ricardo H. C. Takahashi.
DecisionMaker Preference Modeling in Interactive Multiobjective
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Paola Pellegrini and Mauro Birattari.
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Paola Pellegrini, Mauro Birattari, and Thomas Stützle.
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Paola Pellegrini, L. Castelli, and R. Pesenti.
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Paola Pellegrini, D. Favaretto, and E. Moretti.
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