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%2F19%3A39914633" target="_blank" >RIV/00216275:25530/19:39914633 - isvavai.cz</a>

Výsledek na webu

<a href="http://jaec.vn/index.php/JAEC/article/view/226/99" target="_blank" >http://jaec.vn/index.php/JAEC/article/view/226/99</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.25073/jaec.201931.226" target="_blank" >10.25073/jaec.201931.226</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

In herein presented 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 other artificial intelligence or soft computing techniques like 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 population 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

In herein presented 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 other artificial intelligence or soft computing techniques like 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 population 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

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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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/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Spolupráce Univerzity Pardubice a aplikační sféry v aplikačně orientovaném výzkumu lokačních, detekčních a simulačních systémů pro dopravní a přepravní procesy (PosiTrans)</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

Journal of Advanced Engineering and Computation

ISSN

1859-2244

e-ISSN

—

Svazek periodika

3

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

VN - Vietnamská socialistická republika

Počet stran výsledku

8

Strana od-do

304-311

Kód UT WoS článku

—

EID výsledku v databázi Scopus

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