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WIGLASZ, M. DRAHOŠOVÁ, M.
Originální název
Plastic Fitness Predictors Coevolved with Cartesian Programs
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Coevolution of fitness predictors, which are a small sample of all training data for a particular task, was successfully used to reduce the computational cost of the design performed by cartesian genetic programming. However, it is necessary to specify the most advantageous number of fitness cases in predictors, which differs from task to task. This paper proposes to introduce a new type of directly encoded fitness predictors inspired by the principles of phenotypic plasticity. The size of the coevolved fitness predictor is adapted in response to the phase of learning that the program evolution goes through. It is shown in 5 symbolic regression tasks that the proposed algorithm is able to adapt the number of fitness cases in predictors in response to the solved task and the program evolution flow.
Klíčová slova
fitness predictors, cartesian genetic programming, coevolution, phenotypic plasticity
Autoři
WIGLASZ, M.; DRAHOŠOVÁ, M.
Vydáno
30. 3. 2016
Nakladatel
Springer International Publishing
Místo
Berlin
ISBN
978-3-319-30667-4
Kniha
19th European Conference on Genetic programming
Edice
Lecture Notes in Computer Science
Strany od
164
Strany do
179
Strany počet
16
URL
https://www.fit.vut.cz/research/publication/11001/
BibTex
@inproceedings{BUT130922, author="Michal {Wiglasz} and Michaela {Drahošová}", title="Plastic Fitness Predictors Coevolved with Cartesian Programs", booktitle="19th European Conference on Genetic programming", year="2016", series="Lecture Notes in Computer Science", volume="9594", pages="164--179", publisher="Springer International Publishing", address="Berlin", doi="10.1007/978-3-319-30668-1\{_}11", isbn="978-3-319-30667-4", url="https://www.fit.vut.cz/research/publication/11001/" }