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An evaluation of off-line calibration techniques for evolutionary algorithms

Authors:
Elizabeth Montero
Universidad Técnica Federico Santa María, Valparaíso, Chile
,
María-Cristina Riff
Universidad Técnica Federico Santa María, Valparaíso, Chile
,
Bertrand Neveu
École des Ponts ParisTech, Paris, France
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Published: 07 July 2010 Publication History

Abstract

Most metaheuristics define a set of parameters that must be tuned. A good setup of those parameter values can lead to take advantage of all the metaheuristic capabilities to solve the problem at hand. Tuning techniques are step by step methods based on multiple runs of the algorithm. In this study we compare three automated tuning methods: F-Race, Revac and ParamILS. We evaluate the performance of each method using a genetic algorithm for combinatorial optimization. The differences and advantages of each technique are discussed. Finally we establish some guidelines that might help to choose a tuning process to use.

References

[1]
F. Hutter, H. H. Hoos, and T.Stützle. Automatic algorithm configuration based on local search. In Proceedings of the AAAI, pages 1152--1157, 2007.
[2]
M.Birattari, T.Stützle, L.Paquete, and K.Varrentrapp. A racing algorithm for configuring metaheuristics. In Proceedings of the GECCO, pages 11--18, 2002.
[3]
V. Nannen and A.E. Eiben. Relevance estimation and value calibration of evolutionary algorithm parameters. In Proceedings of IJCAI, pages 975--980, 2007.

Cited By

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  • (2019)On automatic algorithm configuration of vehicle routing problem solversJournal on Vehicle Routing Algorithms10.1007/s41604-019-00010-9Online publication date: 22-Feb-2019
  • (2018)A Hybrid Evolutionary Algorithm for Maximizing Satisfiability in Temporal or Spatial Qualitative ConstraintsProceedings of the 10th Hellenic Conference on Artificial Intelligence10.1145/3200947.3201021(1-9)Online publication date: 9-Jul-2018
  • (2014)Automating the Parameter Selection in VRP: An Off-line Parameter Tuning Tool ComparisonModeling, Simulation and Optimization for Science and Technology10.1007/978-94-017-9054-3_11(191-209)Online publication date: 19-Jun-2014
  • Show More Cited By

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cover image ACM Conferences
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
July 2010
1520 pages
ISBN:9781450300728
DOI:10.1145/1830483

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2010

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Author Tags

  1. evolutionary algorithms
  2. parameter setting problem

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GECCO '10
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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2019)On automatic algorithm configuration of vehicle routing problem solversJournal on Vehicle Routing Algorithms10.1007/s41604-019-00010-9Online publication date: 22-Feb-2019
  • (2018)A Hybrid Evolutionary Algorithm for Maximizing Satisfiability in Temporal or Spatial Qualitative ConstraintsProceedings of the 10th Hellenic Conference on Artificial Intelligence10.1145/3200947.3201021(1-9)Online publication date: 9-Jul-2018
  • (2014)Automating the Parameter Selection in VRP: An Off-line Parameter Tuning Tool ComparisonModeling, Simulation and Optimization for Science and Technology10.1007/978-94-017-9054-3_11(191-209)Online publication date: 19-Jun-2014
  • (2013)The consultation timetabling problem at Danish high schoolsJournal of Heuristics10.1007/s10732-013-9219-919:3(465-495)Online publication date: 1-Jun-2013
  • (2011)Instance-based parameter tuning for evolutionary AI planningProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2002053(591-598)Online publication date: 12-Jul-2011
  • (2011)Instance-based parameter tuning for evolutionary AI planningProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2002004(259-260)Online publication date: 12-Jul-2011

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References

References

[1]
F. Hutter, H. H. Hoos, and T.Stützle. Automatic algorithm configuration based on local search. In Proceedings of the AAAI, pages 1152--1157, 2007.
[2]
M.Birattari, T.Stützle, L.Paquete, and K.Varrentrapp. A racing algorithm for configuring metaheuristics. In Proceedings of the GECCO, pages 11--18, 2002.
[3]
V. Nannen and A.E. Eiben. Relevance estimation and value calibration of evolutionary algorithm parameters. In Proceedings of IJCAI, pages 975--980, 2007.