- author = "Jean-Baptiste Hoock and Olivier Teytaud",
- title = "Progress Rate in Noisy Genetic Programming for Choosing lambda",
- booktitle = "Artificial Evolution",
- year = "2011",
- editor = "Jin-Kao Hao and Pierrick Legrand and Pierre Collet and Nicolas Monmarch and Evelyne Lutton and Marc Schoenauer",
- pages = "494--505",
- address = "Angers, France",
- month = "24-26 " # oct,
- organisation = "Association Evolution Artificielle",
- keywords = "genetic algorithms, genetic programming, game theory",
-
URL = "
http://www.info.univ-angers.fr/ea2011/doc/EA2011_ProceedingsWeb.pdf",
-
URL = "
http://hal.inria.fr/inria-00622150",
-
URL = "http://hal.inria.fr/docs/00/62/21/50/PDF/ea2011.pdf",
- size = "12 pages",
- abstract = "Recently, it has been proposed to use Bernstein races for implementing non-regression testing in noisy genetic programming. We study the population size of such a (1+lambda) evolutionary algorithm applied to a noisy fitness function optimisation by a progress rate analysis and experiment it on a policy search application.",
-
notes = "
See also Chapter 4 in \cite{Hoock:thesis}
1 TAO (Inria), LRI, UMR 8623(CNRS - Univ. Paris-Sud), bat 490 Univ. Paris-Sud 91405 Orsay, France, 2 Dept. of Computer Science and Information Engineering, National University of Tainan, Taiwan EA'11",
- language = "ENG",
- oai = "oai:hal.inria.fr:inria-00622150",
