Influence of (p)RNGs onto GPA-ES behaviors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F17%3A39911967" target="_blank" >RIV/00216275:25530/17:39911967 - isvavai.cz</a>

Výsledek na webu

<a href="http://nnw.cz/doi/2017/NNW.2017.27.033.pdf" target="_blank" >http://nnw.cz/doi/2017/NNW.2017.27.033.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2017.27.033" target="_blank" >10.14311/NNW.2017.27.033</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Influence of (p)RNGs onto GPA-ES behaviors

Popis výsledku v původním jazyce

The main aim of this paper is to investigate if the evolutionary algorithms (EAs) can be influenced by Random Number Generators (RNGs) and pseudo Random Number Generators (pRNGs) and if different evolutionary operators applied within EAs requires different features of RNGs and pRNGs. Speaking both about RNGs and pRNGs, the abbreviation (p)RNGs will be used. This question is significant especially if genetic programming is applied to symbolic regression task with the aim to produce human expert comparable results because such task requires massive computations. Experiments were performed on GPA-ES algorithm combining genetic programming algorithm (GPA) for structure development and evolutionary strategy (ES) algorithm for parameter optimization. This algorithm is described bellow and it applies extended scale of different evolutionary operators (additional individuals generating, symmetric crossover, mutations, and one point crossover). These experiments solved problem of symbolic regression of dynamic system. The number of iterations needed for required quality of regression was used as the measure of (p)RNG influence. These experiments point that different (p)RNGs fit to different evolutionary operators, that some combinations (p)RNGs are better than others and that some theoretically excellent (p)RNGs produces poor results. Presented experiments point that the efficiency of evolutionary algorithms might be increased by application of more (p)RNGs in one algorithm optimised for each particular evolutionary operator.

Název v anglickém jazyce

Influence of (p)RNGs onto GPA-ES behaviors

Popis výsledku anglicky

The main aim of this paper is to investigate if the evolutionary algorithms (EAs) can be influenced by Random Number Generators (RNGs) and pseudo Random Number Generators (pRNGs) and if different evolutionary operators applied within EAs requires different features of RNGs and pRNGs. Speaking both about RNGs and pRNGs, the abbreviation (p)RNGs will be used. This question is significant especially if genetic programming is applied to symbolic regression task with the aim to produce human expert comparable results because such task requires massive computations. Experiments were performed on GPA-ES algorithm combining genetic programming algorithm (GPA) for structure development and evolutionary strategy (ES) algorithm for parameter optimization. This algorithm is described bellow and it applies extended scale of different evolutionary operators (additional individuals generating, symmetric crossover, mutations, and one point crossover). These experiments solved problem of symbolic regression of dynamic system. The number of iterations needed for required quality of regression was used as the measure of (p)RNG influence. These experiments point that different (p)RNGs fit to different evolutionary operators, that some combinations (p)RNGs are better than others and that some theoretically excellent (p)RNGs produces poor results. Presented experiments point that the efficiency of evolutionary algorithms might be increased by application of more (p)RNGs in one algorithm optimised for each particular evolutionary operator.

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

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

16

Strana od-do

593-606

Kód UT WoS článku

000423300700005

EID výsledku v databázi Scopus

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