Detail publikačního výsledku

Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

DRAHOŠOVÁ, M.; SEKANINA, L.; WIGLASZ, M.

Originální název

Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

Anglický název

Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

Druh

Článek WoS

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Cartesian genetic programming, coevolutionary algorithms, fitness prediction, symbolic regression, evolutionary design, image processing.

Klíčová slova v angličtině

Cartesian genetic programming, coevolutionary algorithms, fitness prediction, symbolic regression, evolutionary design, image processing.

Autoři

DRAHOŠOVÁ, M.; SEKANINA, L.; WIGLASZ, M.

Rok RIV

2020

Vydáno

03.09.2019

ISSN

1063-6560

Periodikum

EVOLUTIONARY COMPUTATION

Svazek

27

Číslo

3

Stát

Spojené státy americké

Strany od

497

Strany do

523

Strany počet

27

URL

https://www.fit.vut.cz/research/publication/11206/

BibTex

@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/"
}

Dokumenty

DRAHOSOVA-Q1
ec19