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GOMGE: Gene-Pool Optimal Mixing on Grammatical Evolution

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Book cover Parallel Problem Solving from Nature – PPSN XV (PPSN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11101))

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Abstract

Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recent Evolutionary Algorithm (EA) in which the interactions among parts of the solution (i.e., the linkage) are learned and exploited in a novel variation operator. We present GOMGE, the extension of GOMEA to Grammatical Evolution (GE), a popular EA based on an indirect representation which may be applied to any problem whose solutions can be described using a context-free grammar (CFG). GE is a general approach that does not require the user to tune the internals of the EA to fit the problem at hand: there is hence the opportunity for benefiting from the potential of GOMEA to automatically learn and exploit the linkage. We apply the proposed approach to three variants of GE differing in the representation (original GE, SGE, and WHGE) and incorporate in GOMGE two specific improvements aimed at coping with the high degeneracy of those representations. We experimentally assess GOMGE and show that, when coupled with WHGE and SGE, it is clearly beneficial to both effectiveness and efficiency, whereas it delivers mixed results with the original GE.

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Notes

  1. 1.

    https://github.com/ericmedvet/evolved-ge.

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Author information

Authors and Affiliations

  1. Department of Engineering and Architecture, University of Trieste, Trieste, Italy

    Eric Medvet, Alberto Bartoli, Andrea De Lorenzo & Fabiano Tarlao

Authors
  1. Eric Medvet
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  2. Alberto Bartoli
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  3. Andrea De Lorenzo
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  4. Fabiano Tarlao
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Corresponding author

Correspondence to Eric Medvet .

Editor information

Editors and Affiliations

  1. Inria Saclay, Palaiseau, France

    Anne Auger

  2. University of Coimbra, Coimbra, Portugal

    Carlos M. Fonseca

  3. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  4. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  5. University of Coimbra, Coimbra, Portugal

    Luís Paquete

  6. Colorado State University, Fort Collins, Colorado, USA

    Darrell Whitley

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Medvet, E., Bartoli, A., De Lorenzo, A., Tarlao, F. (2018). GOMGE: Gene-Pool Optimal Mixing on Grammatical Evolution. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-99253-2_18

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