Abstract
In this paper, we propose a new, Genetically Optimized Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimize its structure for classification. We introduce new crossover and mutation operations which differ from a normal Genetic programming life-cycle to reduce the destructive nature of these operations. We use the GONN algorithm to classify breast cancer tumors as benign or malignant. Accurate classification of a breast cancer tumor is an important task in medical diagnosis. Our algorithm gives better classification accuracy of almost 4% and 2% more than a Back Propagation neural network and a Support Vector Machine respectively.
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