Optimization with auxiliary criteria using evolutionary algorithms and reinforcement learning

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

In this paper, an optimization method EA + RL based on an evolutionary algorithm controlled by reinforcement learning is proposed. Reinforcement learning is used to choose the most effective fitness function at each generation of the evolutionary algorithm. The method can be applied in scalar optimization with auxiliary criteria to speed up the optimization process. Experimental results for a model problem H-IFF are given. Applying of the method doubles mean fitness obtained with evolution strategy. A comparison with other evolutionary optimization methods is performed. The proposed method outperforms all the considered scalar optimization methods and most of the multicriteria ones.

Original languageEnglish
Title of host publicationMENDEL 2012 - 18th International Conference on Soft Computing
Subtitle of host publicationEvolutionary Computation, Genetic Programming, Swarm Intelligence, Fuzzy Logic, Neural Networks, Fractals, Bayesian Methods
PublisherBrno University of Technology
Pages58-63
Number of pages6
ISBN (Print)9788021445406
Publication statusPublished - 2012
Externally publishedYes
Event18th International Conference on Soft Computing, MENDEL 2012 - Brno, Czech Republic
Duration: 27 Jun 201229 Jun 2012

Publication series

NameMendel
ISSN (Print)1803-3814

Conference

Conference18th International Conference on Soft Computing, MENDEL 2012
Country/TerritoryCzech Republic
CityBrno
Period27 Jun 201229 Jun 2012

Keywords

  • Evolutionary algorithms
  • Fitness function
  • H-IFF
  • Multicriteria optimization
  • Reinforcement learning
  • Scalar optimization

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