Backward-chaining genetic programming

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

  author =       "Riccardo Poli and William B. Langdon",
  title =        "Backward-chaining genetic programming",
  booktitle =    "{GECCO 2005}: Proceedings of the 2005 conference on
                 Genetic and evolutionary computation",
  year =         "2005",
  editor =       "Hans-Georg Beyer and Una-May O'Reilly and 
                 Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and 
                 Eric W. Bonabeau and Erick Cantu-Paz and 
                 Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and 
                 Edwin D. {de Jong} and Hod Lipson and Xavier Llora and 
                 Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and 
                 Terence Soule and Andy M. Tyrrell and 
                 Jean-Paul Watson and Eckart Zitzler",
  volume =       "2",
  ISBN =         "1-59593-010-8",
  pages =        "1777--1778",
  address =      "Washington DC, USA",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/1068009.1068306",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "25-29 " # jun,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Poster,
                 backward chaining, performance, tournament selection,
                 Selection, Speedup technique",
  size =         "2 pages",
  abstract =     "Tournament selection is the most frequently used form
                 of selection in Genetic Programming (GP). Tournament
                 selection chooses individuals uniformly at random from
                 the population. As noted in [6], even if this process
                 is repeated many times in each generation, there is
                 always a non-zero probability that some of the
                 individuals in the population will not be involved in
                 any tournament. In certain conditions, typical in GP,
                 the number of individuals in this category can be
                 large. Because these individuals have no influence on
                 future generations, it is possible to avoid creating
                 and evaluating them without altering in any significant
                 way the course of a run. [6] proposed an algorithm, the
                 backward chaining EA (BC-EA), to realised this, but
                 provided limited empirical evidence as to the
                 obtainable savings and experimentation was restricted
                 to fixed-length genetic algorithms. In this paper we
                 provide a genetic programming implementation of BC-EA
                 and empirically investigate the efficiency in terms of
                 fitness evaluations and memory use and effectiveness in
                 terms of ability to solve problems of BC-GP. Our
                 results indicate that the efficiency gains obtainable
                 with this approach can be big.",
  notes =        "GECCO-2005 A joint meeting of the fourteenth
                 international conference on genetic algorithms
                 (ICGA-2005) and the tenth annual genetic programming
                 conference (GP-2005).

                 ACM Order Number 910052 ACM gecco2005.bib key

                 See also \cite{CSM-425}",

Genetic Programming entries for Riccardo Poli William B Langdon