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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Soft Computing

ISSN

1568-4946

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

Oct

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

653-668

Kód UT WoS článku

000380935400048

EID výsledku v databázi Scopus

2-s2.0-84967110128