Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artificial Neural Networks
Neural Network Training Using Genetic Algorithms
Neural Network Training Using Genetic Algorithms
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Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks in which the synaptic interactions are mediated via a nonlinear mechanism called shunting inhibition, which allows neurons to operate as adaptive nonlinear filters. In this article, The architecture of SIANNs is extended to form a generalized feedforward neural network (GFNN) classifier. Two training algorithms are developed based on stochastic search methods, namely genetic algorithms (GAs) and a randomized search method. The combination of stochastic training with the GFNN is applied to four benchmark classification problems: the XOR problem, the 3-bit even parity problem, a diabetes dataset and a heart disease dataset. Experimental results prove the potential of the proposed combination of GFNN and stochastic search training methods. The GFNN can learn difficult classification tasks with few hidden neurons; it solves perfectly the 3-bit parity problem using only one neuron.