A parallel evolving algorithm for flexible neural tree

Peng, Lizhi; Yang, Bo; Zhang, Lei; Chen, Yuehui

doi:10.1016/j.parco.2011.06.001

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…To improve the efficiency in terms of computation, Peng et al [42] proposed a parallel evolving algorithm for FNT, where the parallelization took place in both tree-structure and parameter vector populations. In another parallel approach, Wang et al [43] used gene expression programming (GEP) [44] for evolving FNT and used PSO for parameter optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

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%

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ojha

Abraham

Snel

2017

Applied Soft Computing

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“…It was observed that embedding beta-basis function at FNT nodes has advantages over other two parametric activation function. A parallel evolution of FNT using MPI programming and GPU programming respectively were proposed in [274] and in [275].…”

Section: Combination Of Fnn Components Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

465

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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“…Flexible neural tree (FNT) [3] is a hierarchical neural network, which is automatically created in order to solve given problem. Its structure is usually determined using some adaptive mechanism and it is intended to adapt to the problem and data under investigation [11,10,4]. Due to this property of the FNTs, it is not necessary to setup some generic static network structure not related to the problem domain beforehand.…”

Section: Flexible Neural Treementioning

confidence: 99%

“…Finding an optimal or near-optimal flexible neural tree can be accomplished by various evolutionary and bio-inspired algorithms [11,10,4]. The general learning procedure for constructing the FNT model can be described in high level as follows [3]:…”

Section: Flexible Neural Treementioning

confidence: 99%

Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter

Krömer

Novosád

Snåšel

et al. 2013

Advances in Intelligent Systems and Computing

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This multidisciplinary study presents the application of two well known soft computing methods-flexible neural trees, and evolutionary fuzzy rules-for the prediction of the error parameter between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.

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A parallel evolving algorithm for flexible neural tree

Cited by 10 publications

References 25 publications

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Metaheuristic design of feedforward neural networks: A review of two decades of research

Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter

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