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SKGP: The Way of the Combinator

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Genetic Programming Theory and Practice XII

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

Abstract

Genetic Programming (GP) is a machine learning technique that evolves programs using natural selection and populations dynamics. Much of the functionality of GP depends on the representation of programs in the population and how to handle illegal or type incoherent expressions that arise from crossover and mutation within a population of programs. The SKGP is a GP system that uses graphs of combinators to represent functions and a strong type system to inform the crossover and mutation operations during evolution. This produces a powerful, flexible system that has many benefits over more conventional systems. This paper describes the implementation of this system, gives some examples of successful applications constructed using the SKGP and describes future directions that may offer a more powerful GP system capable of producing more complex programs.

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Authors and Affiliations

  1. Evolution Enterprises, Milan, 214 W. Main St., 48160, MI, USA

    William P. Worzel

  2. Google Inc, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA

    Duncan MacLean

Authors
  1. William P. Worzel
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  2. Duncan MacLean
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Correspondence to William P. Worzel .

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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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Worzel, W., MacLean, D. (2015). SKGP: The Way of the Combinator. 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_4

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  • DOI: https://doi.org/10.1007/978-3-319-16030-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16029-0

  • Online ISBN: 978-3-319-16030-6

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