Genetic programming: profiling reasonable parameter value windows with varying problem difficulty

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Alan Piszcz and Terence Soule",
  title =        "Genetic programming: profiling reasonable parameter
                 value windows with varying problem difficulty",
  journal =      "International Journal of Innovative Computing and
  year =         "2007",
  volume =       "1",
  number =       "2",
  pages =        "108--120",
  keywords =     "genetic algorithms, genetic programming, GP
                 algorithms, problem difficulty, mutation rates,
                 parameter values, population size",
  publisher =    "INDERSCIENCE",
  DOI =          "doi:10.1504/IJICA.2007.016792",
  abstract =     "Genetic Programming (GP) algorithms benefit from
                 careful consideration of parameter values, especially
                 for complex problems. We submit that determining the
                 optimal parameter value is not as important as finding
                 a window of reasonable parameter values. We test seven
                 problems to determine if windows of reasonable
                 parameter values for mutation rates and population size
                 exist. The results show narrowing, expanding and static
                 windows of effective mutation rates dependent upon the
                 problem type. The results for varying population sizes
                 show that less complex problems use more resources per
                 solution with increasing population size. Conversely as
                 the problem difficulty increases we see either no
                 significant change in solution effort as population
                 size increases, indicating constant efficiency or in
                 some cases we discover decreasing solution effort with
                 larger population sizes. This suggests that in general
                 as the instances of a problem increase in difficulty
                 increasing the population size will either have no
                 effect on efficiency or, for some problems, will lead
                 to relatively small increases in efficiency.",
  notes =        "Department of Computer Science, University of Idaho
                 Moscow, ID 83844, USA",

Genetic Programming entries for Alan Piszcz Terence Soule