Improving Genetic Programming with Behavioral Consistency Measure

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Krzysztof Krawiec and Armando Solar-Lezama",
  title =        "Improving Genetic Programming with Behavioral
                 Consistency Measure",
  booktitle =    "13th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2014",
  editor =       "Thomas Bartz-Beielstein and Juergen Branke and 
                 Bogdan Filipic and Jim Smith",
  publisher =    "Springer",
  isbn13 =       "978-3-319-10761-5",
  pages =        "434--443",
  series =       "Lecture Notes in Computer Science",
  address =      "Ljubljana, Slovenia",
  month =        "13-17 " # sep,
  volume =       "8672",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-319-10762-2_43",
  abstract =     "Program synthesis tasks usually specify only the
                 desired output of a program and do not state any
                 expectations about its internal behaviour. The
                 intermediate execution states reached by a running
                 program can be nonetheless deemed as more or less
                 preferred according to their information content with
                 respect to the desired output. In this paper, a
                 consistency measure is proposed that implements this
                 observation. When used as an additional search
                 objective in a typical genetic programming setting,
                 this measure improves the success rate on a suite of 35
                 benchmarks in a statistically significant way.",
  notes =        "",

Genetic Programming entries for Krzysztof Krawiec Armando Solar-Lezama