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Performance Evaluation and Population Reduction for a Self Adaptive Hybrid Genetic Algorithm (SAHGA)

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

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Abstract

This paper examines the effects of local search on hybrid genetic algorithm performance and population sizing. It compares the performance of a self-adaptive hybrid genetic algorithm (SAHGA) to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on eight different test functions, including unimodal, multimodal and constrained optimization problems. The results show that the hybrid genetic algorithm substantially reduces required population sizes because of the reduction in population variance. The adaptive nature of the SAHGA algorithm together with the reduction in population size allow for faster solution of the test problems without sacrificing solution quality.

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Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Illinois, 205 N. Matthews Ave, Urbana, IL, 61801

    Felipe P. Espinoza & Barbara S. Minsker

  2. Department of General Engineering, University of Illinois, 104 N. Matthews Ave, Urbana, IL, 61801

    David E. Goldberg

Authors
  1. Felipe P. Espinoza
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  2. Barbara S. Minsker
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  3. David E. Goldberg
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Espinoza, F.P., Minsker, B.S., Goldberg, D.E. (2003). Performance Evaluation and Population Reduction for a Self Adaptive Hybrid Genetic Algorithm (SAHGA). In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_104

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  • DOI: https://doi.org/10.1007/3-540-45105-6_104

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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