Dynamic Subset Selection Based on a Fitness Case Topology

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

  author =       "Christian W. G. Lasarczyk and Peter Dittrich and 
                 Wolfgang Banzhaf",
  title =        "Dynamic Subset Selection Based on a Fitness Case
  journal =      "Evolutionary Computation",
  year =         "2004",
  volume =       "12",
  number =       "2",
  pages =        "223--242",
  month =        "Summer",
  keywords =     "genetic algorithms, genetic programming, search space,
                 topology, diversity",
  URL =          "http://ls11-www.cs.uni-dortmund.de/people/lasar/publication/LasarDittBanz_TBS_2004/LasarDittBanz_TBS_2004.pdf",
  DOI =          "doi:10.1162/106365604773955157",
  abstract =     "A large training set of fitness cases can critically
                 slow down genetic programming, if no appropriate subset
                 selection method is applied. Such a method allows an
                 individual to be evaluated on a smaller subset of
                 fitness cases. we suggest a subset selection method
                 that takes the problem structure into account, while
                 being problem independent at the same time. In order to
                 achieve this, information about the problem structure
                 is acquired during evolutionary search by creating a
                 topology (relationship) on the set of fitness cases.
                 The topology is induced by individuals of the evolving
                 population. This is done by increasing the strength of
                 the relation between two fitness cases, if an
                 individual of the population is able to solve both of
                 them. Our new topology based subset selection method
                 chooses a subset, such that fitness cases in this
                 subset are as distantly related as is possible with
                 respect to the induced topology. We compare topology
                 based selection of fitness cases with dynamic subset
                 selection and stochastic subset sampling on four
                 different problems. On average, runs with topology
                 based selection show faster progress than the others.",
  notes =        "preprint at

Genetic Programming entries for Christian W G Lasarczyk Peter Dittrich Wolfgang Banzhaf