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Lexicase Selection with Weighted Shuffle

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

Abstract

Semantic-aware methods in genetic programming take into account information about programs’ performances across a set of test cases. Lexicase parent selection, a semantic-aware selection, randomly shuffles the list of test cases and places more emphasis on those test cases that randomly appear earlier in the ordering than those that appear later in the ordering. In this work, we explore methods for weighting this shuffling of test cases to give some test cases more influence over selection than others. We design and test a variety of weighted shuffle algorithms and methods for weighting test cases. In experiments on two program synthesis benchmark problems, we find that none of these methods significantly outperform regular lexicase selection. We analyze these results by examining how each method affects population diversity, and find that those methods that perform much worse also have significantly lower diversity.

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Fig. 6.1
Fig. 6.2
Fig. 6.3

Notes

  1. 1.

    https://github.com/lspector/Clojush.

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Acknowledgements

Hammad Ahmad, Lee Spector, and Nicholas Freitag McPhee shared interesting discussions that were very helpful in conducting this work.

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Correspondence to Thomas Helmuth .

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Troise, S.A., Helmuth, T. (2018). Lexicase Selection with Weighted Shuffle. In: Banzhaf, W., Olson, R., Tozier, W., Riolo, R. (eds) Genetic Programming Theory and Practice XV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-90512-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-90512-9_6

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