Publication Details
Plastic Fitness Predictors Coevolved with Cartesian Programs
Drahošová Michaela, Ing., Ph.D. (DCSY)
fitness predictors, cartesian genetic programming, coevolution, phenotypic
plasticity
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.
@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/"
}