GARS: An improved genetic algorithm with reserve selection for global optimization

Yang Chen, Jinglu Hu, Kotaro Hirasawa, Songnian Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

This paper investigates how genetic algorithms (GAs) can be improved to solve large-scale and complex problems more efficiently. First of all, we review premature convergence, one of the challenges confronted with when applying GAs to real-world problems. Next, some of the methods now available to prevent premature convergence and their intrinsic defects are discussed. A qualitative analysis is then done on the cause of premature convergence that is the loss of building blocks hosted in less-fit individuals during the course of evolution. Thus, we propose a new improver - GAs with Reserve Selection (GARS), where a reserved area is set up to save potential building blocks and a selection mechanism based on individual uniqueness is employed to activate the potentials. Finally, case studies are done in a few standard problems well known in the literature, where the experimental results demonstrate the effectiveness and robustness of GARS in suppressing premature convergence, and also an enhancement is found in global optimization capacity.

Original languageEnglish
Title of host publicationProceedings of GECCO 2007
Subtitle of host publicationGenetic and Evolutionary Computation Conference
Pages1173-1178
Number of pages6
DOIs
Publication statusPublished - 2007 Aug 27
Event9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom
Duration: 2007 Jul 72007 Jul 11

Publication series

NameProceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Conference

Conference9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
CountryUnited Kingdom
CityLondon
Period07/7/707/7/11

Keywords

  • Building block hypothesis
  • Evolutionary computation
  • Genetic algorithms
  • Population diversity
  • Premature convergence
  • Reserve selection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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  • Cite this

    Chen, Y., Hu, J., Hirasawa, K., & Yu, S. (2007). GARS: An improved genetic algorithm with reserve selection for global optimization. In Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference (pp. 1173-1178). (Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/1276958.1277188