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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2005: Evolutionary Multi-Criterion Optimization pp 265–279Cite as

Recombination of Similar Parents in EMO Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3410))

Abstract

This paper examines the effect of crossover operations on the performance of EMO algorithms through computational experiments on knapsack problems and flowshop scheduling problems using the NSGA-II algorithm. We focus on the relation between the performance of the NSGA-II algorithm and the similarity of recombined parent solutions. First we show the necessity of crossover operations through computational experiments with various specifications of crossover and mutation probabilities. Next we examine the relation between the performance of the NSGA-II algorithm and the similarity of recombined parent solutions. It is shown that the quality of obtained solution sets is improved by recombining similar parents. Then we examine the effect of increasing the selection pressure (i.e., increasing the tournament size) on the similarity of recombined parent solutions. An interesting observation is that the increase in the tournament size leads to the recombination of dissimilar parents, improves the diversity of solutions, and degrades the convergence performance of the NSGA-II algorithm.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka, 599-8531, Japan

    Hisao Ishibuchi & Kaname Narukawa

Authors
  1. Hisao Ishibuchi
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  2. Kaname Narukawa
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Editor information

Editors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), 07300, México, D.F., México

    Carlos A. Coello Coello

  2. Centro de Investigación en Matemáticas (CIMAT), A.P. 402, Guanajuato, 36000, Mexico

    Arturo Hernández Aguirre

  3. Computer Engineering and Networks Laboratory, ETH, Zurich, Switzerland

    Eckart Zitzler

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© 2005 Springer-Verlag Berlin Heidelberg

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Ishibuchi, H., Narukawa, K. (2005). Recombination of Similar Parents in EMO Algorithms. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_19

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  • DOI: https://doi.org/10.1007/978-3-540-31880-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24983-2

  • Online ISBN: 978-3-540-31880-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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