Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming

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

@InProceedings{dolin:ppsn2002:pp142,
  author =       "Brad Dolin and Maribel Garcia Arenas and 
                 Juan J. Merelo Guervos",
  title =        "Opposites Attract: Complementary Phenotype Selection
                 for Crossover in Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "142--152",
  year =         "2002",
  editor =       "Juan J. Merelo-Guervos and Panagiotis Adamidis and 
                 Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and 
                 Hans-Paul Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computing, Selection",
  ISBN =         "3-540-44139-5",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=142",
  DOI =          "doi:10.1007/3-540-45712-7_14",
  abstract =     "Standard crossover in genetic programming (GP) selects
                 two parents independently, based on fitness, and swaps
                 randomly chosen portions of genetic material
                 (subtrees). The mechanism by which the crossover
                 operator achieves success in GP, and even whether
                 crossover does in fact exhibit relative success
                 compared to other operators such as mutation, is
                 anything but clear [14]. An intuitive explanation for
                 successful crossover would be that the operator
                 produces fit offspring by combining the 'strengths' of
                 each parent. However, standard selection schemes choose
                 each parent independently of the other, and with regard
                 to overall fitness rather than more specific phenotypic
                 traits. We present an algorithm for choosing parents
                 which have complementary performance on a set of
                 fitness cases, with an eye toward enabling the
                 crossover operator to produce offspring which combine
                 the distinct strengths of each parent. We test
                 Complementary Phenotype Selection in three genetic
                 programming domains: Boolean 6-Multiplexer, Intertwined
                 Spirals Classification, and Sunspot Prediction. We
                 demonstrate significant performance gains over the
                 control methods in all of them and present a
                 preliminary analysis of these results.",
}

Genetic Programming entries for Brad Dolin Maribel Garcia Arenas Juan Julian Merelo

Citations