Extended immune programming and opposite-based PSO for evolving flexible beta basis function neural tree

Author

Bouaziz, Souhir ; Alimi, Adel M. ; Abraham, Ajith

Author_Institution

Res. Group on Intell. Machines (REGIM), Univ. of Sfax, Sfax, Tunisia

fYear

2013

fDate

13-15 June 2013

Firstpage

13

Lastpage

18

Abstract

In this paper, a new hybrid learning algorithm based on the global optimization techniques, is introduced to evolve the Flexible Beta Basis Function Neural Tree (FBBFNT). The structure is developed using the Extended Immune Programming (EIP) and the Beta parameters and connected weights are optimized using the Opposite-based Particle Swarm Optimization (OPSO) algorithm. The performance of the proposed method is evaluated for time series prediction area and is compared with those of associated methods.

Keywords

artificial immune systems; learning (artificial intelligence); neural nets; particle swarm optimisation; time series; Beta parameter optimization; EIP; FBBFNT; connected weight optimization; evolving flexible beta basis function neural tree; extended immune programming; global optimization techniques; hybrid learning algorithm; opposite-based PSO; opposite-based particle swarm optimization algorithm; time series prediction; Algorithm design and analysis; Optimization; Sociology; Testing; Time series analysis; Training; Extended Immune Programming; Flexible Beta Basis Function Neural Tree; Opposite-based Particle Swarm Optimization; Time series prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetics (CYBCONF), 2013 IEEE International Conference on

Conference_Location

Lausanne

Type

conf

DOI

10.1109/CYBConf.2013.6617425

Filename

6617425