GPA-ES Algorithm Modification for Large Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F19%3A39915138" target="_blank" >RIV/00216275:25530/19:39915138 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-30329-7_9" target="_blank" >http://dx.doi.org/10.1007/978-3-030-30329-7_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-30329-7_9" target="_blank" >10.1007/978-3-030-30329-7_9</a>
Alternative languages
Result language
angličtina
Original language name
GPA-ES Algorithm Modification for Large Data
Original language description
This paper discusses improvement of Genetic Programming Algorithm to large data sets with respect to future extension to big data applications. On the beginning it summarizes requirements on evolutionary system to be applicable in the area of big data and ways of their satisfaction. Then GPAs and especially their improvements by solution constant optimization (so called hierarchical and hybrid genetic programming algorithms) are discussed in this paper. After a discussion of few experiment results of introduced novel evaluation scheme approach with floating data window is presented. Novel evaluation scheme applies floating data window to fitness function evaluation. After one evaluation step of GPA including tuning of parameters (solution constants) by embedded Evolutionary Strategy algorithm data window moves to new position. Presented results demonstrate that this strategy can be faster and more efficient than evolution of whole training data set in each evolutionary step of GPA algorithm. This modification can be starting point of future applications of GPA in the field of large and big data analytic.
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
<a href="/en/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Intelligent systems applications in software engineering : proceedings of 3rd computational methods in systems and software 2019, Vol. 1
ISBN
978-3-030-30328-0
ISSN
2194-5357
e-ISSN
2194-5365
Number of pages
9
Pages from-to
98-106
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Zlín
Event date
Sep 10, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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