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On GA-AIS Hybrids for Constrained Optimization Problems in Engineering

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Constraint-Handling in Evolutionary Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 198))

Abstract

A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired by the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The resulting GA-AIS hybrid is tested in a suite of constrained optimization problems with continuous variables, as well as structural and mixed integer reliability engineering optimization problems. In order to improve the diversity of the population, a variant of the algorithm is developed with the inclusion of a clearing procedure. The performance of the GA-AIS hybrids is compared with that of alternative techniques, such as the Adaptive Penalty Method, and the Stochastic Ranking technique, which represent two different types of constraint handling techniques that have been shown to provide good results in the literature.

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Author information

Authors and Affiliations

  1. Universidade Federal de Juiz de Fora, Brazil

    Heder S. Bernardino & Afonso C. C. Lemonge

  2. Laboratório Nacional de Computação Científica, Av. Getúlio Vargas 333, 25651-075, Petrópolis, RJ, Brazil

    Helio J. C. Barbosa & Leonardo G. Fonseca

Authors
  1. Heder S. Bernardino
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  2. Helio J. C. Barbosa
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  3. Afonso C. C. Lemonge
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  4. Leonardo G. Fonseca
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Editors and Affiliations

  1. National Laboratory on Advanced Informatics (LANIA A.C.) Rébsamen 80, CENTRO, 91000, Xalapa, Veracruz, Mexico

    Efrén Mezura-Montes

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Bernardino, H.S., Barbosa, H.J.C., Lemonge, A.C.C., Fonseca, L.G. (2009). On GA-AIS Hybrids for Constrained Optimization Problems in Engineering. In: Mezura-Montes, E. (eds) Constraint-Handling in Evolutionary Optimization. Studies in Computational Intelligence, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00619-7_8

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  • DOI: https://doi.org/10.1007/978-3-642-00619-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00618-0

  • Online ISBN: 978-3-642-00619-7

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