Evolving better representations through selective genome growth

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Lee Altenberg",
  year =         "1994",
  pages =        "182--187",
  title =        "Evolving better representations through selective
                 genome growth",
  booktitle =    "Proceedings of the 1st IEEE Conference on Evolutionary
  publisher =    "IEEE",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher_address = "Piscataway, NJ, USA",
  volume =       "1",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dynamics.org/~altenber/PAPERS/EBR/",
  URL =          "http://dynamics.org/Altenberg/FILES/LeeEBR.pdf",
  abstract =     "The choice of how to represent the search space for a
                 genetic algorithm (GA) is critical to the GA's
                 performance. Representations are usually engineered by
                 hand and fixed for the duration of the GA run. Here a
                 new method is described in which the degrees of freedom
                 of the representation --- i.e. the genes -- are
                 increased incrementally. The phenotypic effects of the
                 new genes are randomly drawn from a space of different
                 functional effects. Only those genes that initially
                 increase fitness are kept. The genotype-phenotype map
                 that results from this selection during the
                 constructional of the genome allows better adaptation.
                 This effect is illustrated with the NK landscape model.
                 The resulting genotype-phenotype maps are much less
                 epistatic than generic maps would be. They have
                 extremely low values of ``K'' --- the number of fitness
                 components affected by each gene. Moreover, these maps
                 are exquisitely tuned to the specifics of the random
                 fitness functions, and achieve fitnesses many standard
                 deviations above generic NK landscapes with the same
                 \gp\ maps. The evolved maps create adaptive landscapes
                 that are much smoother than generic NK landscapes ever
                 are. Thus a caveat should be made when making arguments
                 about the applicability of generic properties of
                 complex systems to evolved systems. This method may
                 help to solve the problem of choice of representations
                 in genetic algorithms.

                 Copyright 1996 Lee Altenberg",
  notes =        "


Genetic Programming entries for Lee Altenberg