Genetic Programs and Co-Evolution Developing robust general purpose controllers using local mating in two dimensional populations

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Ronge:1996:GPce,
  author =       "Andreas Ronge and Mats G. Nordahl",
  title =        "Genetic Programs and Co-Evolution Developing robust
                 general purpose controllers using local mating in two
                 dimensional populations",
  booktitle =    "Parallel Problem Solving from Nature IV, Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  editor =       "Hans-Michael Voigt and Werner Ebeling and 
                 Ingo Rechenberg and Hans-Paul Schwefel",
  series =       "LNCS",
  volume =       "1141",
  pages =        "81--90",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61723-X",
  doi =          "doi:10.1007/3-540-61723-X_972",
  size =         "10 pages",
  abstract =     "

                 A co-evolutionary approach for developing programs for
                 controlling a very simple {"}robot-like{"} simulated
                 vehicle is presented. The main goal is to find programs
                 that can generalize and solve other similar problems.
                 Good results are achieved by co-evolving the test cases
                 and the simulated vehicles and using locality in both
                 the reproduction and evaluation phases. The fitness of
                 a controller is determined by its performance in
                 competition with its neighbours in the test case
                 population. The fitness of a test case is similarly
                 determined through competition with its neighbours in
                 the controller population. The co-evolved controllers
                 are more robust and general than a simple hand-designed
                 algorithm or controllers evolved using a fixed training
                 set.",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 256
                 indexed memory cells, single ADF, depth restriction.
                 Mentions {"}guided crossover{"} ?how directed? {"}The
                 introduction of geographic separation tends to improve
                 population diversity{"}",
}

Genetic Programming entries for Andreas Ronge Mats G Nordahl