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Lexicase Selection Beyond Genetic Programming

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Genetic Programming Theory and Practice XVI

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

Abstract

Lexicase selection is a selection method that was developed for parent selection in genetic programming. In this chapter, we present a study of lexicase selection in a non-genetic-programming context, conducted to investigate the broader applicability of the technique. Specifically, we present a framework for solving Boolean constraint satisfaction problems using a traditional genetic algorithm, with linear genomes of fixed length. We present results of experiments in this framework using three parent selection algorithms: lexicase selection, tournament selection (with several tournament sizes), and fitness-proportionate selection. The results show that when lexicase selection is used, more solutions are found, fewer generations are required to find those solutions, and more diverse populations are maintained. We discuss the implications of these results for the utility of lexicase selection more generally.

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Notes

  1. 1.

    It is also possible to limit the initial pools in various ways. When the initial pool contains the entire population, which is the best-studied setting, we refer to the algorithm more specifically as “global pool” lexicase selection.

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Acknowledgements

We thank other members of the Hampshire College Institute for Computational Intelligence, along with other participants in the Genetic Programming Theory and Practice workshop, for helpful feedback and stimulating discussions.

This material is based upon work supported by the National Science Foundation under Grant No. 1617087. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA

    Blossom Metevier, Anil Kumar Saini & Lee Spector

  2. School of Cognitive Science, Hampshire College, Amherst, MA, USA

    Lee Spector

Authors
  1. Blossom Metevier
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  2. Anil Kumar Saini
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  3. Lee Spector
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Correspondence to Lee Spector .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Cognitive Science, Hampshire College, Amherst, MA, USA

    Lee Spector

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Leigh Sheneman

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Metevier, B., Saini, A.K., Spector, L. (2019). Lexicase Selection Beyond Genetic Programming. In: Banzhaf, W., Spector, L., Sheneman, L. (eds) Genetic Programming Theory and Practice XVI. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-04735-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-04735-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04734-4

  • Online ISBN: 978-3-030-04735-1

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