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Lexicase Selection Beyond Genetic Programming

  • Blossom Metevier
  • Anil Kumar Saini
  • Lee SpectorEmail author
Chapter
Part of the Genetic and Evolutionary Computation book series (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.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Blossom Metevier
    • 1
  • Anil Kumar Saini
    • 1
  • Lee Spector
    • 1
    • 2
    Email author
  1. 1.College of Information and Computer SciencesUniversity of MassachusettsAmherstUSA
  2. 2.School of Cognitive ScienceHampshire CollegeAmherstUSA

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