Speeding up Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{oai:CiteSeerPSU:336117,
  title =        "Speeding up Genetic Programming",
  author =       "Penousal Machado and Amilcar Cardoso",
  booktitle =    "Proceedings of the Second International Symposium on
                 Artificial Intelligence, Adaptive Systems (CIMAF -
                 99)",
  year =         "1999",
  address =      "Havana, Cuba",
  month =        mar # " 22-26",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://eden.dei.uc.pt/~machado/research/pdf/1999/cimaf99-fasteval.pdf",
  URL =          "http://eden.dei.uc.pt/~ernesto/EvoCo/papers/papers/1999/cimaf992.htm",
  URL =          "http://citeseer.ist.psu.edu/336117.html",
  citeseer-isreferencedby = "oai:CiteSeerPSU:41881;
                 oai:CiteSeerPSU:361360; oai:CiteSeerPSU:231399;
                 oai:CiteSeerPSU:560606",
  citeseer-references = "oai:CiteSeerPSU:276822;
                 oai:CiteSeerPSU:186935",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:336117",
  rights =       "unrestricted",
  abstract =     "One of the major drawbacks of Evolutionary Computation
                 is the need for great computational power. The set of
                 problems that can be solved, in practice, by
                 evolutionary approaches is highly connected with the
                 efficiency of the algorithm. In most Genetic
                 Programming applications the majority of time is spent
                 on the evaluation of the individuals. Accordingly, it
                 is desirable to optimise this step of the process. In
                 this paper we present two approaches through which
                 significant speed improvements can be achieved. The
                 first approach, T-functions, is effective in tasks,
                 such as symbolic regression, that require repeated
                 evaluation of the individuals. The second approach,
                 caching, resorts to the storage of the execution
                 results of individuals' sub-trees, thus avoiding the
                 recalculation of these sub-programs. Caching finds its
                 application when the function set includes complex,
                 time-consuming functions.",
}

Genetic Programming entries for Penousal Machado Amilcar Cardoso

Citations