Influence of (p)RNGs onto GPA-ES behaviors

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Influence of (p)RNGs onto GPA-ES behaviors

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

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

Name of the periodical

Neural Network World

ISSN

1210-0552

e-ISSN

—

Volume of the periodical

27

Issue of the periodical within the volume

6

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

16

Pages from-to

593-606

UT code for WoS article

000423300700005

EID of the result in the Scopus database

—