Evolving flexible beta basis function neural tree using extended genetic programming & Hybrid Artificial Bee Colony
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- 作者：Souhir Bouaziza ; souhir.bouaziz@ieee.org" class="auth_mail" title="E-mail the corresponding author ; souhir.bouaziz@gmail.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Habib Dhahria ; habib.dhahri@ieee.org" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Adel M. Alimia ; adel.alimi@ieee.org" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Ajith Abrahamb ; c ; ajith.abraham@ieee.org" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Flexible beta basis function neural tree model ; Extended Genetic Programming ; Hybrid Artificial Bee Colony algorithm ; Time-series forecasting
- 刊名：Applied Soft Computing
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：47
- 期：Complete
- 页码：653-668
- 全文大小：4175 K
文摘
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A new hybrid learning algorithm is introduced to evolve the flexible beta basis function neural tree (FBBFNT) model.
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The Extended Genetic Programming (EGP) is used to optimize the structure of the FBBFNT.
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A new hybridization between Artificial Bee Colony (ABC) and Opposite-based Particle Swarm Optimization (OPSO) is proposed to optimize the parameters of FBBFNT.
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The proposed model is evaluated for benchmark problems drawn from time series prediction area.