A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model

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@Article{Bouaziz:2013:Neurocomputing,

• author = "Souhir Bouaziz and Habib Dhahri and Adel M. Alimi and Ajith Abraham",
• title = "A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model",
• journal = "Neurocomputing",
• volume = "117",
• pages = "107--117",
• year = "2013",
• keywords = "genetic algorithms, genetic programming, Flexible Beta Basis Function Neural Tree Model, Opposite-based particle swarm optimization algorithm, Time-series forecasting, Control system",
• ISSN = "0925-2312",
• DOI = "doi:10.1016/j.neucom.2013.01.024",
• URL = "http://www.sciencedirect.com/science/article/pii/S0925231213001975",
• abstract = "In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimised based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimisation algorithm (OPSO). The performance of the proposed method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related methods.",

}

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

Citations