Cooperative Co-evolution Inspired Operators for Classical GP Schemes
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Aichour:2007:NICSO,
-
author = "Malek Aichour and Evelyne Lutton",
-
title = "Cooperative Co-evolution Inspired Operators for
Classical GP Schemes",
-
booktitle = "Proceedings of International Workshop on Nature
Inspired Cooperative Strategies for Optimization (NICSO
'07)",
-
year = "2007",
-
pages = "169--178",
-
editor = "Natalio Krasnogor and Giuseppe Nicosia and
Mario Pavone and David Pelta",
-
volume = "129",
-
series = "Studies in Computational Intelligence",
-
address = "Acireale, Italy",
-
month = "8-10 " # nov,
-
publisher = "Springer",
-
keywords = "genetic algorithms, genetic programming",
-
isbn13 = "978-3-540-78986-4",
-
DOI = "doi:10.1007/978-3-540-78987-1_16",
-
abstract = "This work is a first step toward the design of a
cooperative-coevolution GP for symbolic regression,
which first output is a selective mutation operator for
classical GP. Cooperative co-evolution techniques rely
on the imitation of cooperative capabilities of natural
populations and have been successfully applied in
various domains to solve very complex optimisation
problems. It has been proved on several applications
that the use of two fitness measures (local and global)
within an evolving population allow to design more
efficient optimization schemes. We currently
investigate the use of a two-level fitness measurement
for the design of operators, and present in this paper
a selective mutation operator. Experimental analysis on
a symbolic regression problem give evidence of the
efficiency of this operator in comparison to classical
subtree mutation",
-
notes = "http://www.dmi.unict.it/nicso2007/
http://www.dmi.unict.it/nicso2007/NICSO2007-program.pdf",
- }
Genetic Programming entries for
Malek Aichour
Evelyne Lutton
Citations