Evolutionary Programming of Near-Optimal Neural Networks
A genetic algorithm (GA) method that evolves both the topology and training parameters of backpropagation-trained, fully-connected, feed-forward neural networks is presented. The GA uses a weak encoding scheme with real-valued alleles. One contribution of the proposed approach is to replace the needed but potentially slow evolution of final weights by the more efficient evolution of a single weight spread parameter used to set the initial weights only. In addition, the co-evolution of an input mask effects a form of automatic feature selection. Preliminary experiments suggest that the resulting system is able to produce networks that perform well under backpropagation.
KeywordsNeural Network Genetic Algorithm Hide Layer Optimal Topology Initial Weight
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