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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2007: Evolutionary Multi-Criterion Optimization pp 126–140Cite as

Design Issues in a Multiobjective Cellular Genetic Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4403))

Abstract

In this paper we study a number of issues related to the design of a cellular genetic algorithm (cGA) for multiobjective optimization. We take as an starting point an algorithm following the canonical cGA model, i.e., each individual interacts with those ones belonging to its neighborhood, so that a new individual is obtained using the typical selection, crossover, and mutation operators within this neighborhood. An external archive is used to store the non-dominated solutions found during the evolution process. With this basic model in mind, there are many different design issues that can be faced. Among them, we focus here on the synchronous/asynchronous feature of the cGA, the feedback of the search experience contained in the archive into the algorithm, and two different replacement strategies. We evaluate the resulting algorithms using a benchmark of problems and compare the best of them against two state-of-the-art genetic algorithms for multiobjective optimization. The obtained results indicate that the cGA model is a promising approach to solve this kind of problem.

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Authors and Affiliations

  1. Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga (Spain),  

    Antonio J. Nebro, Juan J. Durillo, Francisco Luna, Bernabé Dorronsoro & Enrique Alba

Authors
  1. Antonio J. Nebro
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  2. Juan J. Durillo
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  3. Francisco Luna
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  4. Bernabé Dorronsoro
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  5. Enrique Alba
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Editor information

Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

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© 2007 Springer Berlin Heidelberg

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Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E. (2007). Design Issues in a Multiobjective Cellular Genetic Algorithm. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_13

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  • DOI: https://doi.org/10.1007/978-3-540-70928-2_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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