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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Bouaziz:2016:ASC,

• author = "Souhir Bouaziz and Habib Dhahri and Adel M. Alimi and Ajith Abraham",
• title = "Evolving flexible beta basis function neural tree using extended genetic programmin \& Hybrid Artificial Bee Colony",
• journal = "Applied Soft Computing",
• year = "2016",
• volume = "47",
• pages = "653--668",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2016.03.006",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494616301156",
• keywords = "genetic algorithms, genetic programming, Flexible beta basis function neural tree model, Hybrid Artificial Bee Colony algorithm, Time-series forecasting",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc47.html#BouazizDAA16",
• abstract = "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.",
• notes = "also known as \cite{journals/asc/BouazizDAA16}",

}

Genetic Programming entries for Souhir Bouaziz Habib Dhahri Adel M Alimi Ajith Abraham

Citations