GOMGE: Gene-pool Optimal Mixing on Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Eric Medvet and Alberto Bartoli and 
                 Andrea {De Lorenzo} and Fabiano Tarlao",
  title =        "{GOMGE}: Gene-pool Optimal Mixing on Grammatical
  booktitle =    "15th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2018",
  editor =       "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and 
                 Penousal Machado and Luis Paquete and Darrell Whitley",
  volume =       "11101",
  series =       "LNCS",
  pages =        "223--235",
  address =      "Coimbra, Portugal",
  month =        "8-12 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Linkage,
                 Family of Subsets, Representation",
  isbn13 =       "978-3-319-99252-5",
  URL =          "https://www.springer.com/gp/book/9783319992587",
  DOI =          "doi:10.1007/978-3-319-99253-2_18",
  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.",
  notes =        "PPSN2018 http://ppsn2018.dei.uc.pt

                 This two-volume set LNCS 11101 and 11102 constitutes
                 the refereed proceedings of the 15th International
                 Conference on Parallel Problem Solving from Nature,
                 PPSN 2018",

Genetic Programming entries for Eric Medvet Alberto Bartoli Andrea De Lorenzo Fabiano Tarlao