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Genetic Programming Theory and Practice XII pp 167–179Cite as

Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System

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Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

We demonstrate the effectiveness and power of the distributed GP platform, EC-Star, by comparing the computational power needed for solving an 11-multiplexer function, both on a single machine using a full-fitness evaluation method, as well as using distributed, age-layered, partial-fitness evaluations and a Pitts-style representation. We study the impact of age-layering and show how the system scales with distribution and tends towards smaller solutions. We also consider the effect of pool size and the choice of fitness function on convergence and total computation.

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Acknowledgements

The authors wish to thank Sentient Technologies Holdings Limited for sponsoring this research and providing the processing capacity required for some of the experiments presented in this paper.

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Authors and Affiliations

  1. Sentient Technologies Holdings Limited, 160 Spear St. #1600, San Francisco, CA, USA

    Hormoz Shahrzad & Babak Hodjat

Authors
  1. Hormoz Shahrzad
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  2. Babak Hodjat
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Corresponding author

Correspondence to Hormoz Shahrzad .

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Editors and Affiliations

  1. Center for the Study of Complex Systems (CSCS), University of Michigan, Ann Arbor, Michigan, USA

    Rick Riolo

  2. Evolution Enterprises, Milan, Michigan, USA

    William P. Worzel

  3. Evolved Analytics, Midland, Michigan, USA

    Mark Kotanchek

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Shahrzad, H., Hodjat, B. (2015). Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System. In: Riolo, R., Worzel, W., Kotanchek, M. (eds) Genetic Programming Theory and Practice XII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-16030-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-16030-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16029-0

  • Online ISBN: 978-3-319-16030-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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