Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134144" target="_blank" >RIV/00216305:26230/19:PU134144 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/11206/" target="_blank" >https://www.fit.vut.cz/research/publication/11206/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1162/evco_a_00229" target="_blank" >10.1162/evco_a_00229</a>
Alternative languages
Result language
angličtina
Original language name
Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
EVOLUTIONARY COMPUTATION
ISSN
1063-6560
e-ISSN
1530-9304
Volume of the periodical
27
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
27
Pages from-to
497-523
UT code for WoS article
000483650900005
EID of the result in the Scopus database
2-s2.0-85071745594