Practical performance models of algorithms in evolutionary program induction and other domains

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@Article{Graff20101254,
  author =       "Mario Graff and Riccardo Poli",
  title =        "Practical performance models of algorithms in
                 evolutionary program induction and other domains",
  journal =      "Artificial Intelligence",
  volume =       "174",
  number =       "15",
  pages =        "1254--1276",
  year =         "2010",
  ISSN =         "0004-3702",
  DOI =          "doi:10.1016/j.artint.2010.07.005",
  URL =          "http://www.sciencedirect.com/science/article/B6TYF-50KWG15-1/2/3fb87252c46b990fe9a47f5dbd261a82",
  keywords =     "genetic algorithms, genetic programming, Evolution
                 algorithms, Program induction, Performance prediction,
                 Algorithm taxonomies, Algorithm selection problem",
  abstract =     "Evolutionary computation techniques have seen a
                 considerable popularity as problem solving and
                 optimisation tools in recent years. Theoreticians have
                 developed a variety of both exact and approximate
                 models for evolutionary program induction algorithms.
                 However, these models are often criticised for being
                 only applicable to simplistic problems or algorithms
                 with unrealistic parameters. In this paper, we start
                 rectifying this situation in relation to what matters
                 the most to practitioners and users of program
                 induction systems: performance. That is, we introduce a
                 simple and practical model for the performance of
                 program-induction algorithms. To test our approach, we
                 consider two important classes of problems -- symbolic
                 regression and Boolean function induction -- and we
                 model different versions of genetic programming, gene
                 expression programming and stochastic iterated hill
                 climbing in program space. We illustrate the generality
                 of our technique by also accurately modelling the
                 performance of a training algorithm for artificial
                 neural networks and two heuristics for the off-line bin
                 packing problem.

                 We show that our models, besides performing accurate
                 predictions, can help in the analysis and comparison of
                 different algorithms and/or algorithms with different
                 parameters setting. We illustrate this via the
                 automatic construction of a taxonomy for the stochastic
                 program-induction algorithms considered in this study.
                 The taxonomy reveals important features of these
                 algorithms from the performance point of view, which
                 are not detected by ordinary experimentation.",
}

Genetic Programming entries for Mario Graff Guerrero Riccardo Poli

Citations