Using Co-solvability to Model and Exploit Synergetic Effects in Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Krzysztof Krawiec and Pawel Lichocki",
  title =        "Using Co-solvability to Model and Exploit Synergetic
                 Effects in Evolution",
  booktitle =    "PPSN 2010 11th International Conference on Parallel
                 Problem Solving From Nature",
  pages =        "492--501",
  year =         "2010",
  volume =       "6239",
  editor =       "Robert Schaefer and Carlos Cotta and 
                 Joanna Kolodziej and Guenter Rudolph",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  isbn13 =       "978-3-642-15870-4",
  address =      "Krakow, Poland",
  month =        "11-15 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-642-15871-1_50",
  abstract =     "We introduce, analyse, and experimentally examine
                 co-solvability, an ability of a solution to solve a
                 pair of fitness cases (tests). Based on this concept,
                 we devise a co-solvability fitness function that makes
                 solutions compete for rewards granted for solving pairs
                 of tests, in a way analogous to implicit fitness
                 sharing. We prove that co-solvability fitness function
                 is by definition synergistic and imposes selection
                 pressure which is qualitatively different from that of
                 standard fitness function or implicit fitness sharing.
                 The results of experimental verification on eight
                 genetic programming tasks demonstrate that evolutionary
                 runs driven by co-solvability fitness function usually
                 converge faster to well-performing solutions and are
                 more likely to reach global optima.",

Genetic Programming entries for Krzysztof Krawiec Pawel Lichocki