Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@InProceedings{langdon:2002:crlp2p,
author = "W. B. Langdon",
title = "Size of Random Programs to ensure Uniformity",
booktitle = "Proceedings of the Fourteenth Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'02)",
year = "2002",
editor = "Hendrik Blockeel and Marc Denecker",
pages = "459--460",
address = "Leuven, Belgium",
month = "21-22 " # oct,
organisation = "BNVKI, Dutch and the Belgian AI Association",
keywords = "genetic algorithms, genetic programming",
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_bnaic2002.pdf",
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_bnaic2002.ps.gz",
size = "2 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 in
\cite{langdon:2002:crlp} for the outputs of five
computer models (any, average, cyclic, bit flip and
Boolean). Mutation randomises a genetic algorithm
population in 0.25(l+1)(log(l)+4) generations. While
\cite{langdon:2002:foga} considers convergence of
functions. We restate the results 0.5N(log(m)+4) and
O(N)-O(N^{3/2}) for a genetic programming (GP) like
model.",
notes = "2 page summary of \cite{langdon:2002:crlp}
Katholieke Universiteit Leuven and Universite Libre de
Bruxelles in collaboration with PharmaDM and under the
auspices of BNVKI/AIABN (the Belgian-Dutch Association
for Artificial Intelligence), SIKS (School for
Information and Knowledge Systems), and SNN (the
Foundation for Neural Networks).",
}
Genetic Programming entries for William B Langdon