Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F20%3A39914577" target="_blank" >RIV/00216275:25530/20:39914577 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-14907-9_29" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-14907-9_29</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-14907-9_29" target="_blank" >10.1007/978-3-030-14907-9_29</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength

Popis výsledku v původním jazyce

Abstract. In this work, the relation between number of ES iterations and convergence of the whole GPA-ES hybrid algorithm will be studied due to increasing needs to analyze and model large data sets. Evolutionary algorithms are applicable in the areas which are not covered by neural networks and deep learning like search of algebraic model of data. The difference between time and algorithmic complexity will be also mentioned as well as the problems of multitasking implementation of GPA, where external influences complicate increasing of GPA efficiency via Pseudo Random Number Generator (PRNG) choice optimization. Hybrid evolutionary algorithms like GPA-ES uses GPA for solution structure development and Evolutionary Strategy (ES) for parameters identification are controlled by many parameters. The most significant are sizes of GPA popu- lation and sizes of ES populations related to each particular individual in GPA population. There is also limit of ES algorithm evolutionary cycles. This limit plays two contradictory roles. On one side bigger number of ES iterations means less chance to omit good solution for wrongly identified parameters, on the opposite side large number of ES iterations significantly increases computational time and thus limits application domain of GPA-ES algorithm.

Název v anglickém jazyce

Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength

Popis výsledku anglicky

Abstract. In this work, the relation between number of ES iterations and convergence of the whole GPA-ES hybrid algorithm will be studied due to increasing needs to analyze and model large data sets. Evolutionary algorithms are applicable in the areas which are not covered by neural networks and deep learning like search of algebraic model of data. The difference between time and algorithmic complexity will be also mentioned as well as the problems of multitasking implementation of GPA, where external influences complicate increasing of GPA efficiency via Pseudo Random Number Generator (PRNG) choice optimization. Hybrid evolutionary algorithms like GPA-ES uses GPA for solution structure development and Evolutionary Strategy (ES) for parameters identification are controlled by many parameters. The most significant are sizes of GPA popu- lation and sizes of ES populations related to each particular individual in GPA population. There is also limit of ES algorithm evolutionary cycles. This limit plays two contradictory roles. On one side bigger number of ES iterations means less chance to omit good solution for wrongly identified parameters, on the opposite side large number of ES iterations significantly increases computational time and thus limits application domain of GPA-ES algorithm.

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

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Electrical Engineering. Vol. 554

ISBN

978-3-030-14906-2

ISSN

1876-1100

e-ISSN

1876-1119

Počet stran výsledku

12

Strana od-do

294-302

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Ostrava

Datum konání akce

11. 9. 2018

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—