Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU132058" target="_blank" >RIV/00216305:26230/19:PU132058 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.fit.vut.cz/research/publication/11859/" target="_blank" >https://www.fit.vut.cz/research/publication/11859/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-16670-0_7" target="_blank" >10.1007/978-3-030-16670-0_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming

Popis výsledku v původním jazyce

In Geometric Semantic Genetic Programming (GSGP), genetic operators directly work at the level of semantics rather than syntax. It provides many advantages, including much higher quality of resulting individuals (in terms of error) in comparison with a common genetic programming. However, GSGP produces extremely huge solutions that could be difficult to apply in systems with limited resources such as embedded systems. We propose Subtree Cartesian Genetic Programming (SCGP) -- a method capable of reducing the number of nodes in the trees generated by GSGP. SCGP executes a common Cartesian Genetic Programming (CGP) on all elementary subtrees created by GSGP and on various compositions of these optimized subtrees in order to create one compact representation of the original program. SCGP does not guarantee the (exact) semantic equivalence between the CGP individuals and the GSGP subtrees, but the user can define conditions when a particular CGP individual is acceptable. We evaluated SCGP on four common symbolic regression benchmark problems and the obtained node reduction is from 92.4% to 99.9%.

Název v anglickém jazyce

Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming

Popis výsledku anglicky

In Geometric Semantic Genetic Programming (GSGP), genetic operators directly work at the level of semantics rather than syntax. It provides many advantages, including much higher quality of resulting individuals (in terms of error) in comparison with a common genetic programming. However, GSGP produces extremely huge solutions that could be difficult to apply in systems with limited resources such as embedded systems. We propose Subtree Cartesian Genetic Programming (SCGP) -- a method capable of reducing the number of nodes in the trees generated by GSGP. SCGP executes a common Cartesian Genetic Programming (CGP) on all elementary subtrees created by GSGP and on various compositions of these optimized subtrees in order to create one compact representation of the original program. SCGP does not guarantee the (exact) semantic equivalence between the CGP individuals and the GSGP subtrees, but the user can define conditions when a particular CGP individual is acceptable. We evaluated SCGP on four common symbolic regression benchmark problems and the obtained node reduction is from 92.4% to 99.9%.

Druh

D - Stať ve sborníku

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/LTC18053" target="_blank" >LTC18053: Pokročilé metody Nature-Inspired optimalizačních algoritmů a HPC implementace pro řešení reálných aplikací</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 statě ve sborníku

Genetic Programming 22nd European Conference, EuroGP 2019

ISBN

978-3-030-16669-4

ISSN

—

e-ISSN

—

Počet stran výsledku

16

Strana od-do

98-113

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Leipzig

Datum konání akce

24. 4. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—