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An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming

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Book cover Genetic Programming Theory and Practice XIV

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

Abstract

Premature convergence is a serious problem that plagues genetic programming, stifling its search performance. Several genetic diversity maintenance techniques have been proposed for combating premature convergence and improving search efficiency in genetic programming. Recent research has shown that while genetic diversity is important, focusing directly on sustaining behavioral diversity may be more beneficial. These two related areas have received a lot of attention, yet they have often been developed independently. We investigated the feasibility of hybrid genetic and behavioral diversity techniques on a suite of problems.

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Notes

  1. 1.

    https://github.com/burks-pub/gecco2015.

  2. 2.

    https://cs.gmu.edu/~sean/papers/gecco12benchmarks3.pdf.

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Acknowledgements

This material is based in part upon work supported by the National Science Foundation under Cooperative Agreement No. DBI-0939454. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work was supported in part by Michigan State University through computational resources provided by the Institute for Cyber-Enabled Research.

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  1. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA

    Armand R. Burks & William F. Punch

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  1. Armand R. Burks
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Correspondence to Armand R. Burks .

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  1. Center for the Study of Complex Sys, University of Michigan, Ann Arbor, MI, USA

    Rick Riolo

  2. Evolution Enterprises, Ann Arbor, MI, USA

    Bill Worzel

  3. Colorado State University, Fort Collins, CO, USA

    Brian Goldman

  4. Ann Arbor, MI, USA

    Bill Tozier

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Burks, A.R., Punch, W.F. (2018). An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming. In: Riolo, R., Worzel, B., Goldman, B., Tozier, B. (eds) Genetic Programming Theory and Practice XIV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-97088-2_2

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