Genetic Programming Experiments with Standard and Homologous Crossover Methods
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{Hansen:2003:GPEM,
-
author = "James V. Hansen",
-
title = "Genetic Programming Experiments with Standard and
Homologous Crossover Methods",
-
journal = "Genetic Programming and Evolvable Machines",
-
year = "2003",
-
volume = "4",
-
number = "1",
-
pages = "53--66",
-
month = mar,
-
keywords = "genetic algorithms, genetic programming, homologous
crossover, regression, classifications",
-
ISSN = "1389-2576",
-
DOI = "doi:10.1023/A:1021825110329",
-
size = "14 pages",
-
abstract = "While successful applications have been reported using
standard GP crossover, limitations of this approach
have been identified by several investigators. Among
the most compelling alternatives to standard GP
crossover are those that use some form of homologous
crossover, where code segments that are exchanged are
structurally or syntactically aligned in order to
preserve context and worth. This paper reports the
results of an empirical comparison of GP using standard
crossover methods with GP using homologous crossover
methods. Ten problems are tested, five each of pattern
recognition and regression.
Results suggest that in terms of generalisation
accuracy, homologous crossover does generate
consistently better performance. In addition, there is
a consistently lower fraction of introns that are
generated in the solution code.",
-
notes = "Article ID: 5113072",
- }
Genetic Programming entries for
James V Hansen
Citations