Using Numerical Simplification to Control Bloat in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{DBLP:conf/seal/KinzettZJ08,
  author =       "David Kinzett and Mengjie Zhang and Mark Johnston",
  title =        "Using Numerical Simplification to Control Bloat in
                 Genetic Programming",
  booktitle =    "Proceedings of the 7th International Conference on
                 Simulated Evolution And Learning (SEAL '08)",
  year =         "2008",
  editor =       "Xiaodong Li and Michael Kirley and Mengjie Zhang and 
                 David G. Green and Victor Ciesielski and 
                 Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and 
                 Kalyanmoy Deb and Kay Chen Tan and 
                 J{\"u}rgen Branke and Yuhui Shi",
  volume =       "5361",
  series =       "Lecture Notes in Computer Science",
  pages =        "493--502",
  address =      "Melbourne, Australia",
  month =        dec # " 7-10",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-89693-7",
  DOI =          "doi:10.1007/978-3-540-89694-4_50",
  abstract =     "In tree based genetic programming there is a tendency
                 for the size of the programs to increase from
                 generation to generation, a process known as bloat. It
                 is standard practice to place some form of control on
                 program size either by limiting the number of nodes or
                 the depth of the tree, or by adding a component to the
                 fitness function that rewards smaller programs
                 (parsimony pressure). Others have proposed directly
                 simplifying individual programs using algebraic
                 methods. In this paper, we add node-based numerical
                 simplification as a tree pruning criterion to control
                 program size. We show that simplification results in
                 reductions in expected program size, memory use and
                 computation time. We further show that numerical
                 simplification performs at least as well as algebraic
                 simplification alone, and in some cases will outperform
                 algebraic simplification.",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
}

Genetic Programming entries for David Kinzett Mengjie Zhang Mark Johnston

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