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DRAHOŠOVÁ, M. HULVA, J. SEKANINA, L.
Original Title
Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs
Type
conference paper
Language
English
Original Abstract
We investigate coevolutionary Cartesian genetic programming that coevolves fitness predictors in order to diminish the number of target objective vector (TOV) evaluations, needed to obtain a satisfactory solution, to reduce the computational cost of evolution. This paper introduces the use of coevolution of fitness predictors in CGP with a new type of indirectly encoded predictors. Indirectly encoded predictors are operated using the CGP and provide a variable number of TOVs used for solution evaluation during the coevolution. It is shown in 5 symbolic regression problems that the proposed predictors are able to adapt the size of TOVs array in response to a particular training data set.
Keywords
coevolution, cartesian genetic programming, fitness prediction, symbolic regression
Authors
DRAHOŠOVÁ, M.; HULVA, J.; SEKANINA, L.
RIV year
2015
Released
15. 3. 2015
Publisher
Springer International Publishing
Location
Berlin
ISBN
978-3-319-16500-4
Book
Genetic Programming
Edition
Lecture Notes in Computer Science
Pages from
113
Pages to
125
Pages count
13
URL
http://dx.doi.org/10.1007/978-3-319-16501-1_10
BibTex
@inproceedings{BUT119803, author="Michaela {Drahošová} and Jiří {Hulva} and Lukáš {Sekanina}", title="Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs", booktitle="Genetic Programming", year="2015", series="Lecture Notes in Computer Science", volume="9025", pages="113--125", publisher="Springer International Publishing", address="Berlin", doi="10.1007/978-3-319-16501-1\{_}10", isbn="978-3-319-16500-4", url="http://dx.doi.org/10.1007/978-3-319-16501-1_10" }