A Comparision of Random Search versus Genetic Programming as Engines for Collective Adaptation

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

@InProceedings{Haynes:1998:CRS,
  author =       "Thomas Haynes",
  title =        "A Comparision of Random Search versus Genetic
                 Programming as Engines for Collective Adaptation",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and 
                 A. E. Eiben",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  volume =       "1447",
  series =       "LNCS",
  pages =        "683--692",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  organisation = "Natural Selection, Inc.",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  broken =       "http://www.cs.twsu.edu/~haynes/random.ps",
  doi =          "doi:10.1007/BFb0040819",
  size =         "10 pages",
  abstract =     "We have integrated the distributed search of genetic
                 programming (GP) based systems with collective memory
                 to form a collective adaptation search method. Such a
                 system significantly improves search as problem
                 complexity is increased. Since the pure GP approach
                 does not scale well with problem complexity, a natural
                 question is which of the two components is actually
                 contributing to the search process. We investigate a
                 collective memory search which uses a random search
                 engine and find that it significantly outperforms the
                 GP based search engine. We examine the solution space
                 and show that as problem complexity and search space
                 grow, a collective adaptive system will perform better
                 than a collective memory search employing random search
                 as an engine.",
  notes =        "EP-98.

                 {"}With collective adaptation{"}.... {"}A random search
                 engine is more effective than a GP based one, but only
                 at low problem complexity. As the complexity increases,
                 the competetiveness of the GP search engine is more
                 effective than the wide ranging exploration of random
                 search.{"} pages 10-11.",
}

Genetic Programming entries for Thomas D Haynes