Multi-agent architecture for Multi-objective optimization of Flexible Neural Tree
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099095" target="_blank" >RIV/61989100:27240/16:86099095 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.neucom.2016.06.019" target="_blank" >http://dx.doi.org/10.1016/j.neucom.2016.06.019</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2016.06.019" target="_blank" >10.1016/j.neucom.2016.06.019</a>
Alternative languages
Result language
angličtina
Original language name
Multi-agent architecture for Multi-objective optimization of Flexible Neural Tree
Original language description
In this paper, a multi-agent system is introduced to parallelize the Flexible Beta Basis Function Neural Network (FBBFNT)' training as a response to the time cost challenge. Different agents are formed; a Structure Agent is designed for the FBBFNT structure optimization and a variable set of Parameter Agents is used for the FBBFNT parameter optimization. The main objectives of the FBBFNT learning process were the accuracy and the structure complexity. With the proposed multi-agent system, the main purpose is to reach a good balance between these objectives. For that, a multi-objective context was adopted which based on Pareto dominance. The agents use two algorithms: the Pareto dominance Extended Genetic Programming (PEGP) and the Pareto Multi-Dimensional Particle Swarm Optimization (PMD_PSO) algorithms for the structure and parameter optimization, respectively. The proposed system is called Pareto Multi-Agent Flexible Neural Tree (PMA_FNT).To assess the effectiveness of . PMA_FNT, four benchmark real datasets of classification are tested. The results compared with some classifiers published in the literature. (C) 2016 Elsevier B.V.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurocomputing
ISSN
0925-2312
e-ISSN
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Volume of the periodical
214
Issue of the periodical within the volume
NOV
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
307-316
UT code for WoS article
000386741300029
EID of the result in the Scopus database
2-s2.0-84978763693