- 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}
http://www.cs.kuleuven.ac.be/conference/bnaic02/
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).",
