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The Parallel Genetic Cellular Automata: Application to Global Function Optimization

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Artificial Neural Nets and Genetic Algorithms

Abstract

This work describes massively parallel genetic algorithms inspired by cellular automata models and by large, spatially distributed populations of individuals, as suggested by biological analogies. Models with strict locality and a variety of pseudo-diffusion models are presented. The models are applied to the the global optimization problem of multiextremal multimodal functions. They are tested on a suite of hard standard test functions. Results are then discussed for the various models taking into account the unusual population sizes, their diversity and the role of individual’s migration.

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References

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Author information

Authors and Affiliations

  1. Centro Svizzero di Calcolo Scientifico, Via Cantonale, CH-6928, Manno, Switzerland

    Marco Tomassini

Authors
  1. Marco Tomassini
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Editor information

Editors and Affiliations

  1. Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

    Rudolf F. Albrecht

  2. Division of Statistics and Operational Research, UK

    Colin R. Reeves

  3. Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

    Nigel C. Steele

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© 1993 Springer-Verlag/Wien

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Tomassini, M. (1993). The Parallel Genetic Cellular Automata: Application to Global Function Optimization. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_56

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  • DOI: https://doi.org/10.1007/978-3-7091-7533-0_56

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82459-7

  • Online ISBN: 978-3-7091-7533-0

  • eBook Packages: Springer Book Archive

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