Publication Details
Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Wiglasz Michal, Ing.
Cartesian genetic programming, coevolutionary algorithms, fitness
prediction, symbolic regression, evolutionary design, image processing.
In genetic programming (GP), computer programs are often coevolved with training
data subsets that are known as fitness predictors. In order to maximize
performance of GP, it is important to find the most suitable parameters of
coevolution, particularly the fitness predictor size. This is a very time
consuming process as the predictor size depends on a given application and many
experiments have to be performed to find its suitable size. A new method is
proposed which enables us to automatically adapt the predictor and its size for
a given problem and thus to reduce not only the time of evolution, but also the
time needed to tune the evolutionary algorithm. The method was implemented in the
context of Cartesian genetic programming and evaluated using five symbolic
regression problems and three image filter design problems. In comparison with
three different CGP implementations, the time required by CGP search was reduced
while the quality of results remained unaffected.
@article{BUT159961,
author="Michaela {Drahošová} and Lukáš {Sekanina} and Michal {Wiglasz}",
title="Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming",
journal="EVOLUTIONARY COMPUTATION",
year="2019",
volume="27",
number="3",
pages="497--523",
doi="10.1162/evco\{_}a\{_}00229",
issn="1063-6560",
url="https://www.fit.vut.cz/research/publication/11206/"
}