Why Simulation-Based Approaches with Combined Fitness are a Good Approach for Mining Spaces of Turing-equivalent Functions

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

@InProceedings{Teytaud:2006:CEC,
  author =       "Olivier Teytaud",
  title =        "Why Simulation-Based Approaches with Combined Fitness
                 are a Good Approach for Mining Spaces of
                 {Turing}-equivalent Functions",
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
                 Computation",
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "987--994",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9487-9",
  DOI =          "doi:10.1109/CEC.2006.1688320",
  size =         "8 pages",
  abstract =     "We show negative results about the automatic
                 generation of programs within bounded-time. Combining
                 recursion theory and statistics, we contrast these
                 negative results with positive computability results
                 for iterative approaches like genetic programming,
                 provided that the fitness combines e.g. fastness and
                 size. We then show that simulation-based approaches
                 (approaches evaluating only by simulation the quality
                 of programs) like GP are not too far from the minimal
                 time required for evaluating these combined
                 fitnesses.",
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",
}

Genetic Programming entries for Olivier Teytaud

Citations