Small Population Effects and Hybridization

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

  author =       "Daniel A. Ashlock and Kenneth M. Bryden and 
                 Steven Corns",
  title =        "Small Population Effects and Hybridization",
  booktitle =    "2008 IEEE World Congress on Computational
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "2637--2643",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0599.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631152",
  abstract =     "This paper examines the confluence of two lines of
                 research that seek to improve the performance of
                 evolutionary computation systems through management of
                 information flow. The first is hybridisation; the
                 second is using small population effects. Hybridisation
                 consists of restarting evolutionary algorithms with
                 copies of bestof- population individuals drawn from
                 many populations. Small population effects occur when
                 an evolutionary algorithm's performance, either speed
                 or probability of premature convergence, is improved by
                 use of a very small population. This paper presents a
                 structure for evolutionary computation called a blender
                 which performs hybridisation of many small populations.
                 The blender algorithm is tested on the PORS and
                 Tartarus tasks. Substantial and significant effects
                 result from varying the size of the small populations
                 used and from varying the frequency with which
                 hybridisation is performed. The major effect results
                 from changing the frequency of hybridization; the
                 impact of population size is more modest. The parameter
                 settings which yield best performance of the blender
                 algorithm are remarkably consistent across all seven
                 sets of experiments performed. Blender performance is
                 found to be superior to other algorithms for six cases
                 of the PORS problem. For Tartarus, blender performs
                 well, but not as well as the previous hybridization
                 experiments that motivated its development.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",

Genetic Programming entries for Daniel Ashlock Kenneth M Bryden Steven M Corns