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Fitness First

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Book cover Genetic Programming Theory and Practice XVIII

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

Abstract

With side effect free terminals and functions it is possible to evaluate the fitness of genetic programming trees from their parents without creating them. This allows selection before forming the next generation. Thus avoiding unfit runt Genetic Algorithm individuals, which will themselves have no children. In highly diverse GA populations with strong selection, more than 50% of children need not be created. Even with two parent crossover, in converged populations, \(e^{-2}\) = 13.5% can be saved. Eliminating bachelors and spinsters and extracting the smaller genetic material of each mating before crossover, reduces storage in an N multi-threaded implementation for a population M to \(\le \)0.63M+N, compared to the usual M+2N. Memory efficient crossover achieves 692 billion GP operations per second, 692 giga GPops, at runtime on a 16 core 3.8 GHz desktop.

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Acknowledgements

I would like to thank Stephan Winkler, Sara Silva, Bill Tozier, other people at GPTP and anonymous reviewers. This work was inspired by conversations at Dagstuhl Seminar 18052 on Genetic Improvement of Software [26]. Funded by EPSRC grant EP/P005888/1.

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Authors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    W. B. Langdon

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  1. W. B. Langdon
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Correspondence to W. B. Langdon .

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Editors and Affiliations

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

    Wolfgang Banzhaf

  2. Tecnológico Nacional de México/IT de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan Winkler

  4. Evolution Enterprise, Ann Arbor, MI, USA

    Bill Worzel

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Langdon, W.B. (2022). Fitness First. In: Banzhaf, W., Trujillo, L., Winkler, S., Worzel, B. (eds) Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-16-8113-4_8

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  • DOI: https://doi.org/10.1007/978-981-16-8113-4_8

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