Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Electrical Engineering. Vol. 554
ISBN
978-3-030-14906-2
ISSN
1876-1100
e-ISSN
1876-1119
Number of pages
12
Pages from-to
294-302
Publisher name
Springer
Place of publication
Berlin
Event location
Ostrava
Event date
Sep 11, 2018
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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