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

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

doi:10.1016/j.asoc.2016.03.006

Cited by 19 publications

(18 citation statements)

References 31 publications

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“…To improve classification accuracy of FNT, Yang et al [37] proposed a hybridization of FNT with a further-division-of-partition-space method. In [38], authors illustrated crossover and mutation operators for evolving FNT using GP and optimized the tree parameters using PSO algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In crossover operation, randomly selected sub-trees of two parent trees were swapped. The swapping includes the exchange of activation-nodes, weights, and inputs as it is described in [38,64,68].…”

Section: Tree-constructionmentioning

confidence: 99%

“…The mutation of a selected individual from mating pool took place in the following manner [38,64,68]: 1) A randomly selected terminal node is replaced by a newly generated terminal node.…”

Section: Tree-constructionmentioning

confidence: 99%

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Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ojha

Abraham

Snel

2017

Applied Soft Computing

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Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features.MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Tree-constructionmentioning

confidence: 99%

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Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ojha

Abraham

Snel

2017

Applied Soft Computing

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“…The FNT topology is adapted in our work using the Beta function as a transfer function (FBBFNT). Although the FBBFNTs could solve complex problems [12], this model suffers from high time-cost. In addition, real problems include a large set of features/inputs which increase the time complexity.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent architecture for Multi‐objective optimization of Flexible Neural Tree

et al. 2016

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“…It showed a great performance in several success researches like classification [1], [3] (pattern recognition), prediction [12], [13], [31]- [35]. The network structure evolution and the parameter optimization are the two mainly issues that influence on the BBF neural network's performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent evolutionary design of Flexible Beta Basis Function Neural Tree

Ammar

Bouaziz

Alimi

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Multi-Agent System (MAS) is a very active field that ensures global coherence between agents'interactions in a distributed way and implicit global control. Under the awareness of its power, the application of MAS was no more limited to very specific problems, but to almost application area: optimization, neural network, robotics, fuzzy system, etc. In the other side, a complex system of Artificial Neural Network called Flexible Beta Basis Function Neural Tree (FBBFNT) has reached a great level in the prediction search domain. In the purpose of enlarging the application of the algorithm to complex applications of the real problems, a new architecture of MAS was designed and applied to the FBBFNT process. This new multi-agent system based on communications and negotiations allowed the resolution of more complex prediction problems and the acceleration of the global convergence speed.

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

Cited by 19 publications

References 31 publications

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Multi-agent architecture for Multi‐objective optimization of Flexible Neural Tree

Multi-agent evolutionary design of Flexible Beta Basis Function Neural Tree

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