Efficient tree traversal to reduce code growth in tree-based genetic programming

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@Article{Wyns:2009:JH,
  title =        "Efficient tree traversal to reduce code growth in
                 tree-based genetic programming",
  author =       "Bart Wyns and Luc Boullart",
  year =         "2009",
  journal =      "Journal of Heuristics",
  volume =       "15",
  number =       "1",
  pages =        "77--104",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Subtree
                 fitness, Tree traversal, Code growth, Local
                 optimization, Tree-based genetic programming,
                 Technology and Engineering",
  ISSN =         "1381-1231",
  DOI =          "doi:10.1007/s10732-007-9060-0",
  bibsource =    "OAI-PMH server at biblio.ugent.be",
  oai =          "oai:archive.ugent.be:662689",
  abstract =     "Genetic programming is an evolutionary optimization
                 method following the principle of program induction.
                 Genetic programming often uses variable-length tree
                 structures for representing candidate solutions. A
                 serious problem with variable-length representations is
                 code growth: during evolution these tree structures
                 tend to grow in size without a corresponding increase
                 in fitness. Many anti-bloat methods focus solely on
                 size reduction and forget about fitness improvement,
                 which is rather strange when using an
                 {"}optimization{"} method. This paper reports the
                 application of a semantically driven local search
                 operator to control code growth and improve best
                 fitness. Five examples, two theoretical benchmark
                 applications and three real-life test problems are used
                 to illustrate the obtained size reduction and fitness
                 improvement. Performance of the local search operator
                 is also compared with various other anti-bloat methods
                 such as size and depth delimiters, an expression
                 simplifier, linear and adaptive parsimony pressure,
                 automatically defined functions and Tarpeian bloat
                 control.",
}

Genetic Programming entries for Bart Wyns Luc Boullart

Citations