Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "M. Affenzeller and S. Wagner",
  title =        "Offspring Selection: A New Self-Adaptive Selection
                 Scheme for Genetic Algorithms",
  booktitle =    "Proceedings of the seventh International Conference
                 Adaptive and Natural Computing Algorithms",
  year =         "2005",
  editor =       "Bernardete Ribeiro and Rudolf F. Albrecht and 
                 Andrej Dobnikar and David W. Pearson and Nigel C. Steele",
  pages =        "218--221",
  address =      "Coimbra, Portugal",
  month =        "21-23 " # mar,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, OS-GP",
  isbn13 =       "978-3-211-24934-5",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/3-211-27389-1_52",
  abstract =     "In terms of goal orientedness, selection is the
                 driving force of Genetic Algorithms (GAs). In contrast
                 to crossover and mutation, selection is completely
                 generic, i.e. independent of the actually employed
                 problem and its representation. GA-selection is usually
                 implemented as selection for reproduction (parent
                 selection). In this paper we propose a second selection
                 step after reproduction which is also absolutely
                 problem independent. This self-adaptive selection
                 mechanism, which will be referred to as offspring
                 selection, is closely related to the general selection
                 model of population genetics. As the problem- and
                 representation-specific implementation of reproduction
                 in GAs (crossover) is often critical in terms of
                 preservation of essential genetic information,
                 offspring selection has proven to be very suited for
                 improving the global solution quality and robustness
                 concerning parameter settings and operators of GAs in
                 various fields of applications. The experimental part
                 of the paper discusses the potential of the new
                 selection model exemplarily on the basis of
                 standardized real-valued test functions in high
  notes =        "\cite{DBLP:conf/eurocast/BurlacuAKKW17} gives this as
                 reference for OS-GP


Genetic Programming entries for Michael Affenzeller Stefan Wagner