Abstract
Lexicase selection is a selection method for evolutionary computation in which individuals are selected by filtering the population according to performance on test cases, considered in random order. When used as the parent selection method in genetic programming, lexicase selection has been shown to provide significant improvements in problem-solving power. In this chapter we investigate the reasons for the success of lexicase selection, focusing on measures of population diversity. We present data from eight program synthesis problems and compare lexicase selection to tournament selection and selection based on implicit fitness sharing. We conclude that lexicase selection does indeed produce more diverse populations, which helps to explain the utility of lexicase selection for program synthesis.
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Notes
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For some of these problems, each test case generates multiple error values because we apply more than one error function.
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References
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Acknowledgements
Thanks to the members of the Hampshire College Computational Intelligence Lab for discussions that helped to improve the work described in this chapter, to Josiah Erikson for systems support, and to Hampshire College for support for the Hampshire College Institute for Computational Intelligence. This material is based upon work supported by the National Science Foundation under Grants No. 1017817, 1129139, and 1331283. 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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Helmuth, T., McPhee, N.F., Spector, L. (2016). Lexicase Selection for Program Synthesis: A Diversity Analysis. In: Riolo, R., Worzel, W., Kotanchek, M., Kordon, A. (eds) Genetic Programming Theory and Practice XIII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-34223-8_9
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