Searching for a Practical Evidence for the No Free Lunch Theorems

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

@InProceedings{oltean_bioadit_springer2004,
  author =       "Mihai Oltean",
  title =        "Searching for a Practical Evidence for the No Free
                 Lunch Theorems",
  booktitle =    "Biologically Inspired Approaches to Advanced
                 Information Technology: First International Workshop,
                 BioADIT 2004",
  year =         "2004",
  editor =       "Auke Jan Ijspeert and Masayuki Murata and 
                 Naoki Wakamiya",
  volume =       "3141",
  series =       "LNCS",
  pages =        "472--483",
  address =      "Lausanne, Switzerland",
  month =        "29-30 " # jan,
  publisher =    "Springer-Verlag",
  note =         "Revised Selected Papers",
  email =        "moltean@cs.ubbcluj.ro",
  keywords =     "genetic algorithms, genetic programming, No Free
                 Lunch, NFL, pairs of algorithms",
  ISBN =         "3-540-23339-3",
  ISSN =         "0302-9743",
  URL =          "http://www.cs.ubbcluj.ro/~moltean/oltean_bioadit_springer2004.pdf",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3141&spage=472",
  DOI =          "doi:10.1007/b101281",
  size =         "12 pages",
  abstract =     "According to the No Free Lunch (NFL) theorems all
                 blackbox algorithms perform equally well when compared
                 over the entire set of optimisation problems. An
                 important problem related to NFL is finding a test
                 problem for which a given algorithm is better than
                 another given algorithm. Of high interest is finding a
                 function for which Random Search is better than another
                 standard evolutionary algorithm. In this paper we
                 propose an evolutionary approach for solving this
                 problem: we will evolve test functions for which a
                 given algorithm A is better than another given
                 algorithm B. Two ways for representing the evolved
                 functions are employed: as GP trees and as binary
                 strings. Several numerical experiments involving
                 NFL-style Evolutionary Algorithms for function
                 optimization are performed. The results show the
                 effectiveness of the proposed approach. Several test
                 functions for which Random Search performs better than
                 all other considered algorithms have been evolved.",
}

Genetic Programming entries for Mihai Oltean

Citations