Resource-limited genetic programming: the dynamic approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Sara Silva and Ernesto Costa",
  title =        "Resource-limited genetic programming: the dynamic
  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 =        "1673--1680",
  address =      "Washington DC, USA",
  URL =          "",
  DOI =          "doi:10.1145/1068009.1068290",
  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, bloat, code
                 bloat, code growth, evolutionary computation, dynamic
                 limits, experimentation, limited resources,
  abstract =     "Resource-Limited Genetic Programming is a bloat
                 control technique that imposes a single limit on the
                 total amount of resources available to the entire
                 population, where resources are tree nodes or code
                 lines. We elaborate on this recent concept, introducing
                 a dynamic approach to managing the amount of resources
                 available for each generation. Initially low, this
                 amount is increased only if it results in better
                 population fitness. We compare the dynamic approach to
                 the static method where a constant amount of resources
                 is available throughout the run, and with the most
                 traditional usage of a depth limit at the individual
                 level. The dynamic approach does not impair performance
                 on the Symbolic Regression of the quartic polynomial,
                 and achieves excellent results on the Santa Fe
                 Artificial Ant problem, obtaining the same fitness with
                 only a small percentage of the computational effort
                 demanded by the other techniques.",
  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",

Genetic Programming entries for Sara Silva Ernesto Costa