Evolving flexible beta basis function neural tree using extended genetic programming & Hybrid Artificial Bee Colony

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099077" target="_blank" >RIV/61989100:27240/16:86099077 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.asoc.2016.03.006" target="_blank" >http://dx.doi.org/10.1016/j.asoc.2016.03.006</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.asoc.2016.03.006" target="_blank" >10.1016/j.asoc.2016.03.006</a>

Result language
angličtina
Original language name
Evolving flexible beta basis function neural tree using extended genetic programming & Hybrid Artificial Bee Colony
Original language description
In this paper, a new hybrid learning algorithm is introduced to evolve the flexible beta basis function neural tree (FBBFNT). The structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Hybrid Artificial Bee Colony algorithm. This hybridization is essentially based on replacing the random Artificial Bee Colony (ABC) position with the guided Opposite -based Particle Swarm Optimization (OPSO) position. Such modification can minimize the delay which might be lead by the random position, in reaching the global solution. The performance of the proposed model is evaluated for benchmark problems drawn from time series prediction area and is compared with those of related methods.
Czech name
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Czech description
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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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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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