Convergence Rates for the Distribution of Program Outputs

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

@InProceedings{langdon:2002:crlp,
  title =        "Convergence Rates for the Distribution of Program
                 Outputs",
  author =       "W. B. Langdon",
  pages =        "812--819",
  year =         "2002",
  publisher =    "Morgan Kaufmann Publishers",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and 
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and 
                 V. Honavar and G. Rudolph and J. Wegener and 
                 L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and 
                 E. Burke and N. Jonoska",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  keywords =     "genetic algorithms, genetic programming, Fitness
                 Landscapes, Markov analysis, Mutation convergence time,
                 Total Variation Distance, Markov Minorization, Random
                 walk eigenvalues, Average computer",
  ISBN =         "1-55860-878-8",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_gecco2002.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_gecco2002.ps.gz",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/gp103.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
  size =         "8 pages",
  abstract =     "Fitness distributions (landscapes) of programs tend to
                 a limit as they get bigger. Markov chain convergence
                 theorems give general upper bounds on the linear
                 program sizes needed for convergence. Tight bounds
                 (exponential in N, N log N, and smaller) are given for
                 five computer models (any, average, cyclic, bit flip
                 and Boolean). Mutation randomizes a genetic algorithm
                 population in 0.25 (l+1)(log(l)+4) generations. Results
                 for a genetic programming (GP) like model are confirmed
                 by experiment.",
  notes =        "GECCO-2002 A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002) Part of
                 \cite{langdon:2002:GECCO}",
}

Genetic Programming entries for William B Langdon

Citations