Population Clustering in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{eurogp06:XieZhangAndreae,
-
author = "Huayang Xie and Mengjie Zhang and Peter Andreae",
-
title = "Population Clustering in Genetic Programming",
-
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
-
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
-
publisher = "Springer",
-
series = "Lecture Notes in Computer Science",
-
volume = "3905",
-
year = "2006",
-
address = "Budapest, Hungary",
-
month = "10 - 12 " # apr,
-
organisation = "EvoNet",
-
keywords = "genetic algorithms, genetic programming",
-
ISBN = "3-540-33143-3",
-
pages = "190--201",
-
DOI = "doi:10.1007/11729976_17",
-
bibsource = "DBLP, http://dblp.uni-trier.de",
-
abstract = "This paper proposes an approach to reducing the cost
of fitness evaluation whilst improving the
effectiveness in Genetic Programming (GP). In our
approach, the whole population is first clustered by a
heuristic called fitness-case-equivalence. Then a
cluster representative is selected for each cluster.
The fitness value of the representative is calculated
on all training cases. The fitness is then directly
assigned to other members in the same cluster.
Subsequently, a clustering tournament selection method
replaces the standard tournament selection method. A
series of experiments were conducted to solve a
symbolic regression problem, a binary classification
problem, and a multi-class classification problem. The
experiment results show that the new GP system
significantly outperforms the standard GP system on
these problems.",
-
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
- }
Genetic Programming entries for
Huayang Jason Xie
Mengjie Zhang
Peter Andreae
Citations