Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis

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@Article{Pawlak:cgsgp:EC,
  author =       "Tomasz P. Pawlak and Krzysztof Krawiec",
  title =        "Competent Geometric Semantic Genetic Programming for
                 Symbolic Regression and Boolean Function Synthesis",
  journal =      "Evolutionary Computation",
  note =         "Forthcoming",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/EVCO_a_00205",
  size =         "37 pages",
  abstract =     "Program semantics is a promising recent research
                 thread in Genetic Programming (GP). Over a dozen of
                 semantic-aware search, selection, and initialization
                 operators for GP have been proposed to date. Some of
                 those operators are designed to exploit the geometric
                 properties of semantic space, while some others focus
                 on making offspring effective, i.e., semantically
                 different from their parents. Only a small fraction of
                 previous works aimed at addressing both these features
                 simultaneously. In this paper, we propose a suite of
                 competent operators that combine effectiveness with
                 geometry for population initialization, mate selection,
                 mutation and crossover. We present a theoretical
                 rationale behind these operators and compare them
                 experimentally to operators known from literature on
                 symbolic regression and Boolean function synthesis
                 benchmarks. We analyse each operator in isolation as
                 well as verify how they fare together in an
                 evolutionary run, concluding that the competent
                 operators are superior on a wide range of performance
                 indicators, including best-of-run fitness, test-set
                 fitness, and program size.",
}

Genetic Programming entries for Tomasz Pawlak Krzysztof Krawiec

Citations