DESIGNING HOPFIELD TYPE NETWORKS USING GENETIC ALGORITHMS AND ITS COMPARISON WITH SIMULATED ANNEALING

摘要

An application of Genetic Algorithms (GAs) to evolve Hopfield type optimum neural network architectures for object extraction problem is demonstrated. Different optimizing functions involving minimization of energy value of the network, maximization of percentage of correct classification of pixels (pcc), minimization of number of connections of the network (noc), and a combination of pcc and noc have been considered. The noc value of the evolved (sub)optimal architectures is seen to be reduced to two-third of that required for the fully connected version. The performance of GA is seen to be better than that of Simulated Annealing for this problem.