Parallel distributed processing: explorations in the microstructure of cognition, vol. 2: psychological and biological models
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Selection of optimal set of weights in a layered network using genetic algorithms
Information Sciences—Intelligent Systems: An International Journal
Pattern classification with genetic algorithms
Pattern Recognition Letters - Special issue on genetic algorithms
Pattern classification using genetic algorithm: determination of H
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Pattern Recognition
Genetic Algorithms for Pattern Recognition
IEEE Transactions on Neural Networks
Neural network design using Voronoi diagrams
IEEE Transactions on Neural Networks
Genetic evolution of the topology and weight distribution of neural networks
IEEE Transactions on Neural Networks
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An analogy between a genetic algorithm based pattern classification scheme (where hyperplanes are used to approximate the class boundaries through searching) and multilayer perceptron (MLP) based classifier is established. Based on this, a method for determining the MLP architecture automatically is described. It is shown that the architecture would need atmost two hidden layers, the neurons of which are responsible for generating hyperplanes and regions. The neurons in the second hidden and output layers perform the AND & OR functions respectively. The methodology also includes a post processing step which automatically removes any redundant neuron in the hidden/output layer. An extensive comparative study of the performance of the MLP, thus derived using the proposed method, with those of several other conventional MLPs is presented for different data sets.