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Evolving flexible beta basis function neural tree using extended genetic programming & Hybrid Artificial Bee Colony
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DOI：10.1016/j.asoc.2016.03.006.png)
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Abstract
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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. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Flexible beta basis function neural tree model
Extended Genetic Programming
Hybrid Artificial Bee Colony algorithm
Time-series forecasting
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