Multi groups cooperation based symbiotic evolution for TSK-type neuro-fuzzy systems design
Expert Systems with Applications: An International Journal
Efficient reinforcement hybrid evolutionary learning for recurrent wavelet-based neuro-fuzzy systems
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
A hybrid evolutionary learning algorithm for TSK-type fuzzy model design
Mathematical and Computer Modelling: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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A method for pattern classification using genetic algorithms (GAs) has been recently described in Pal, Bandyopadhyay and Murthy (1998), where the class boundaries of a data set are approximated by a fixed number H of hyperplanes. As a consequence of fixing H a priori, the classifier suffered from the limitation of overfitting (or underfitting) the training data with an associated loss of its generalization capability. In this paper, we propose a scheme for evolving the value of H automatically using the concept of variable length strings/chromosomes. The crossover and mutation operators are newly defined in order to handle variable string lengths. The fitness function ensures primarily the minimization of the number of misclassified samples, and also the reduction of the number of hyperplanes. Based on an analogy between the classification principles of the genetic classifier and multilayer perceptron (with hard limiting neurons), a method for automatically determining the architecture and the connection weights of the latter is described.