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Recombination of Similar Parents in SMS-EMOA on Many-Objective 0/1 Knapsack Problems

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Parallel Problem Solving from Nature - PPSN XII (PPSN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7492))

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Abstract

In the evolutionary multiobjective optimization (EMO) community, indicator-based evolutionary algorithms (IBEAs) have rapidly increased their popularity in the last few years thanks to their theoretical background and high search ability. Hypervolume has often been used as an indicator to measure the quality of solution sets in IBEAs. It has been reported in the literature that IBEAs work well on a wide range of multiobjective problems including many-objective problems on which traditional Pareto dominance-based EMO algorithms such as NSGA-II and SPEA2 do not always work well. In this paper, we examine the behavior of SMS-EMOA, which is a frequently-used representative IBEA with a hypervolume indicator function, through computational experiments on many-objective 0/1 knapsack problems. We focus on the effect of two mating strategies on the performance of SMS-EMOA: One is to select extreme parents far from other solutions in the objective space, and the other is to recombine similar parents. Experimental results show that the recombination of similar parents improves the performance of SMS-EMOA on many-objective problems whereas the selection of extreme parents is effective only for a two-objective problem. For comparison, we also examine the effect of these mating strategies on the performance of NSGA-II.

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Authors and Affiliations

  1. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka, 599-8531, Japan

    Hisao Ishibuchi, Naoya Akedo & Yusuke Nojima

Authors
  1. Hisao Ishibuchi
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  2. Naoya Akedo
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  3. Yusuke Nojima
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Editor information

Editors and Affiliations

  1. Departmento de Computation, Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Av. IPN No. 2508, Col. San Pedro Zacatenco, 0360, Mexico, D.F., Mexico

    Carlos A. Coello Coello

  2. Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, 95125, Catania, Italy

    Vincenzo Cutello  & Mario Pavone  & 

  3. Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology, Kanpur, Kanpur, India

    Kalyanmoy Deb

  4. Department of Computer Science, University of New Mexico, USA

    Stephanie Forrest

  5. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia

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Ishibuchi, H., Akedo, N., Nojima, Y. (2012). Recombination of Similar Parents in SMS-EMOA on Many-Objective 0/1 Knapsack Problems. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_14

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  • DOI: https://doi.org/10.1007/978-3-642-32964-7_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32963-0

  • Online ISBN: 978-3-642-32964-7

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