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Studying parallel evolutionary algorithms: The cellular programming case

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Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

Parallel evolutionary algorithms, studied to some extent over the past few years, have proven empirically worthwhile—though there seems to be lacking a better understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, presenting a number of statistical measures, both at the genotypic and phenotypic levels. We demonstrate the application and utility of these measures on a specific example, that of the cellular programming evolutionary algorithm, when used to evolve solutions to a hard problem in the cellular-automata domain, known as synchronization.

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Authors and Affiliations

  1. Logic Systems Laboratory, Swiss Federal Institute of Technology, 1015, Lausanne, Switzerland

    Mathieu Capcarrère & Moshe Sipper

  2. Department of Computer Science, University of Milano, Via Comelico 39/41, 20135, Milano, Italy

    Andrea Tettamanzi

  3. Institute of Computer Science, University of Lausanne, 1015, Lausanne, Switzerland

    Marco Tomassini

Authors
  1. Mathieu Capcarrère
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  2. Andrea Tettamanzi
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  3. Marco Tomassini
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  4. Moshe Sipper
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Capcarrère, M., Tettamanzi, A., Tomassini, M., Sipper, M. (1998). Studying parallel evolutionary algorithms: The cellular programming case. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056899

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  • DOI: https://doi.org/10.1007/BFb0056899

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

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