Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

EVOLUTIONARY COMPUTATION

ISSN

1063-6560

e-ISSN

1530-9304

Svazek periodika

27

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

27

Strana od-do

497-523

Kód UT WoS článku

000483650900005

EID výsledku v databázi Scopus

2-s2.0-85071745594