Floating Data Window Movement Influence to Genetic Programming Algorithm Efficiency
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F19%3A39915177" target="_blank" >RIV/00216275:25530/19:39915177 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-31362-3" target="_blank" >http://dx.doi.org/10.1007/978-3-030-31362-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-31362-3" target="_blank" >10.1007/978-3-030-31362-3</a>
Alternative languages
Result language
angličtina
Original language name
Floating Data Window Movement Influence to Genetic Programming Algorithm Efficiency
Original language description
Presented paper deals with problem of large data series modeling by genetic programming algorithm. The need of repeated evaluation constraints size of training data set in standard Genetic Programming Algorithms (GPAs) because it causes unacceptable number of fitness function evaluations. Thus, the paper discusses possibility of floating data window use and brings results of tests on large training data vector containing 1 million rows. Used floating window is small and for each cycle of GPA it changes its position. This movement allows to incorporate information contained in large number of samples without the need to evaluate all data points contained in training data in each GPA cycle. Behaviors of this evaluation concept are demonstrated on symbolic regression of Lorenz attractor system equations from precomputed training data set calculated from original difference equations. As expected, presented results points that the algorithm is more efficient than evaluating of whole data set in each cycle of GPA.
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
Computational statistics and mathematical modeling methods in intelligent systems : proceedings of 3rd computational methods in systems and software 2019, Vol. 2
ISBN
978-3-030-31361-6
ISSN
2194-5357
e-ISSN
2194-5365
Number of pages
6
Pages from-to
24-30
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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