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Examining the Effect of Elitism in Cellular Genetic Algorithms Using Two Neighborhood Structures

  • Hisao Ishibuchi
  • Noritaka Tsukamoto
  • Yusuke Nojima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

Elitism has a large effect on the search ability of evolutionary algorithms. Many studies, however, did not discuss its different implementations in cellular algorithms. Usually a replacement policy called “replace-if-better” is applied to each cell in cellular algorithms as a kind of elitism. In this paper, we examine three implementations of elitism. One is global elitism where a prespecified number of the best individuals in the entire population are viewed as being the elite. The replace-if-better policy is applied only to the globally best individuals. Another scheme is local elitism where an individual is viewed as being the elite if it is the best among its neighbors. The replace-if-better policy is applied only to the locally best individuals. The other scheme is cell-wise elitism where the replace-if-better policy is applied to all individuals. Effects of elitism are examined through computational experiments using a cellular genetic algorithm with two neighborhood structures. One is for local competition among neighbors. This competition neighborhood is used in the local elitism to determine the locally best individuals. The other is for local selection of parents. This selection neighborhood is also called the mating neighborhood. Since we have the two neighborhood structures, we can specify the size of the competition neighborhood for the implementation of the local elitism independent of the selection neighborhood for mating. Experimental results show that the use of the replace-if-better policy at all cells is not always the best choice.

Keywords

Knapsack Problem Neighborhood Structure Good Individual Local Selection Local Competition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hisao Ishibuchi
    • 1
  • Noritaka Tsukamoto
    • 1
  • Yusuke Nojima
    • 1
  1. 1.Department of Computer Science and Intelligent Systems, Graduate School of EngineeringOsaka Prefecture UniversityOsakaJapan

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