A discipline of evolutionary programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Paul Vitanyi",
  title =        "A discipline of evolutionary programming",
  journal =      "Theoretical Computer Science",
  year =         "2000",
  volume =       "241",
  number =       "1--2",
  pages =        "3--23",
  month =        "28 " # jun,
  keywords =     "genetic algorithms, genetic programming, Neural and
                 Evolutionary Computing, Artificial Intelligence,
                 Computational Complexity, Data Structures and
                 Algorithms, Learning, Multiagent Systems",
  ISSN =         "0304-3975",
  CODEN =        "TCSCDI",
  bibdate =      "Tue Oct 31 11:38:29 MST 2000",
  URL =          "http://xxx.lanl.gov/abs/cs.NE/9902006",
  URL =          "http://homepages.cwi.nl/~paulv/papers/genetic.ps",
  URL =          "http://www.elsevier.nl/gej-ng/10/41/16/175/21/22/article.pdf",
  size =         "21 pages",
  abstract =     "Genetic fitness optimization using small populations
                 or small population updates across generations
                 generally suffers from randomly diverging evolutions.
                 We propose a notion of highly probable fitness
                 optimization through feasible evolutionary computing
                 runs on small size populations. Based on rapidly mixing
                 Markov chains, the approach pertains to most types of
                 evolutionary genetic algorithms, genetic programming
                 and the like. We establish that for systems having
                 associated rapidly mixing Markov chains and appropriate
                 stationary distributions the new method finds optimal
                 programs (individuals) with probability almost 1. To
                 make the method useful would require a structured
                 design methodology where the development of the program
                 and the guarantee of the rapidly mixing property go
                 hand in hand. We analyze a simple example to show that
                 the method is implementable. More significant examples
                 require theoretical advances, for example with respect
                 to the Metropolis filter.",
  notes =        "Update of \cite{alt96*67} Presented at Dagstuhl Feb
                 2004. Generic to evolutionary computation, rather than
                 specifically on GP.",

Genetic Programming entries for Paul M B Vitanyi