A cooperative coevolution algorithm of RBFNN for classification

Authors:
Jin Tian;Minqiang Li;Fuzan Chen
Affiliations:
School of Management, Tianjin University, Tianjin, P.R. China;School of Management, Tianjin University, Tianjin, P.R. China;School of Management, Tianjin University, Tianjin, P.R. China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

Citing 12
Cited 1

Using Model Trees for Classification

Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging

Machine Learning
A real-coded genetic algorithm for training recurrent neural networks

Neural Networks
The constraint based decomposition (CBD) training architecture

Neural Networks
Using Correspondence Analysis to Combine Classifiers

Machine Learning
Generating Accurate Rule Sets Without Global Optimization

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Radial basis function neural networks for nonlinear Fisher discrimination and Neyman—Pearson classification

Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents

Evolutionary Computation
Evolutionary ensembles with negative correlation learning

IEEE Transactions on Evolutionary Computation
Inducing oblique decision trees with evolutionary algorithms

IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms

IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification

IEEE Transactions on Evolutionary Computation

Designing RBFNNs using prototype selection

MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article presents a new learning algorithm, CO-RBFNN, for complex classifications, which attempts to construct the radial basis function neural network (RBFNN) models by using a cooperative coevolutionary algorithm (Co-CEA). The Co-CEA utilizes a divide-and-cooperative mechanism by which subpopulations are coevolved in separate populations of evolutionary algorithms executing in parallel. A modified K-means method is employed to divide the initial hidden nodes into modules that are represented as subpopulation of the Co-CEA. Collaborations among the modules are formed to obtain complete solutions. The algorithm adopts a matrix-form mixed encoding to represent the RBFNN hidden layer structure, the optimum of which is achieved by coevolving all parameters. Experimental results on eight UCI datasets illustrate that CO-RBFNN is able to produce a higher accuracy of classification with a much simpler network structure in fewer evolutionary trials when compared with other alternative standard algorithms.