Analysis of Semantic Modularity for Genetic Programming

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@Article{Krawiec:2009:FCDS,
  author =       "Krzysztof Krawiec and Bartosz Wieloch",
  title =        "Analysis of Semantic Modularity for Genetic
                 Programming",
  journal =      "Foundations of Computing and Decision Sciences",
  year =         "2009",
  volume =       "34",
  number =       "4",
  pages =        "265--285",
  publisher =    "Poznan University of Technology",
  keywords =     "genetic algorithms, genetic programming, semantics,
                 modularity, problem decomposition",
  ISSN =         "0867-6356",
  URL =          "http://fcds.cs.put.poznan.pl/FCDS2/ArticleDetails.aspx?articleId=219",
  size =         "21 pages",
  abstract =     "In this paper we analyze the properties of functional
                 modularity, a concept introduced in
                 \cite{DBLP:conf/gecco/KrawiecW09} for detecting and
                 measuring modularity in problems of automatic program
                 synthesis, in particular by means of genetic
                 programming. The basic components of functional
                 modularity approach are subgoals -- entities that
                 embody module's semantic -- and monotonicity, a measure
                 for assessing subgoals' potential utility for searching
                 for good modules. For a given subgoal and a sample of
                 solutions decomposed into parts and contexts according
                 to module definition, monotonicity measures the
                 correlation of distance between semantics of solution's
                 part and the fitness of the solution. The central tenet
                 of this approach is that highly monotonous subgoals can
                 be used to decompose the task and improve search
                 convergence. In the experimental part we investigate
                 the properties of functional modularity using eight
                 instances of problems of Boolean function synthesis.
                 The results show that monotonicity varies depending on
                 problem's structure of modularity and correctly
                 identifies good subgoals, potentially enabling
                 automatic program decomposition.",
}

Genetic Programming entries for Krzysztof Krawiec Bartosz Wieloch

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