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

Display OmittedThe hybrid learning algorithm for FBBFNT model. A new hybrid learning algorithm is introduced to evolve the flexible beta basis function neural tree (FBBFNT) model.The Extended Genetic Programming (EGP) is used to optimize the structure of the FBBFNT.A new hybridization between Artificial Bee Colony (ABC) and Opposite-based Particle Swarm Optimization (OPSO) is proposed to optimize the parameters of FBBFNT.The proposed model is evaluated for benchmark problems drawn from time series prediction area. 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.