Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Darrell Whitley and Marc Richards and 
                 Ross Beveridge and Andre' {da Motta Salles Barreto}",
  title =        "Alternative evolutionary algorithms for evolving
                 programs: evolution strategies and steady state {GP}",
  booktitle =    "{GECCO 2006:} Proceedings of the 8th annual conference
                 on Genetic and evolutionary computation",
  year =         "2006",
  editor =       "Maarten Keijzer and Mike Cattolico and Dirk Arnold and 
                 Vladan Babovic and Christian Blum and Peter Bosman and 
                 Martin V. Butz and Carlos {Coello Coello} and 
                 Dipankar Dasgupta and Sevan G. Ficici and James Foster and 
                 Arturo Hernandez-Aguirre and Greg Hornby and 
                 Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and 
                 Franz Rothlauf and Conor Ryan and Dirk Thierens",
  volume =       "1",
  ISBN =         "1-59593-186-4",
  pages =        "919--926",
  address =      "Seattle, Washington, USA",
  URL =          "",
  DOI =          "doi:10.1145/1143997.1144155",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "8-12 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, evolution
                 strategies, ES, steady-state genetic algorithms,
                 Automatic Programming, Program Synthesis",
  size =         "8 pages",
  abstract =     "In contrast with the diverse array of genetic
                 algorithms, the Genetic Programming (GP) paradigm is
                 usually applied in a relatively uniform manner.
                 Heuristics have developed over time as to which
                 replacement strategies and selection methods are best.
                 The question addressed in this paper is relatively
                 simple: since there are so many variants of
                 evolutionary algorithm, how well do some of the other
                 well known forms of evolutionary algorithm perform when
                 used to evolve programs trees using s-expressions as
                 the representation? Our results suggest a wide range of
                 evolutionary algorithms are all equally good at
                 evolving programs, including the simplest evolution
  notes =        "GECCO-2006 A joint meeting of the fifteenth
                 international conference on genetic algorithms
                 (ICGA-2006) and the eleventh annual genetic programming
                 conference (GP-2006).

                 ACM Order Number 910060

                 Winner best paper.",

Genetic Programming entries for L Darrell Whitley Marc D Richards J Ross Beveridge Andre da Motta Salles Barreto