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Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2723))

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Abstract

The aim of this paper is to identify Genetic Algorithms (GAs) which perform well over a range of continuous and smooth multimodal real-variable functions. In our study, we focus on testing GAs combining three classes of genetic operators: selection, crossover and replacement. The approach followed is time-constrained and thus our stopping criterion is a fixed number of generations. Results show that GAs with random selection of parents and crowding replacement are robust optimizers. By contrast, GAs with tournament selection of parents and random replacement perform poorly in comparison.

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Author information

Authors and Affiliations

  1. Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK

    Pedro J. Ballester & Jonathan N. Carter

Authors
  1. Pedro J. Ballester
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  2. Jonathan N. Carter
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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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Ballester, P.J., Carter, J.N. (2003). Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization. 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_86

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  • DOI: https://doi.org/10.1007/3-540-45105-6_86

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

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