Evolving Connection Weights for Pattern Storage and Recall in Hopfield Model of Feedback Neural Networks Using a Genetic Algorithm

In this paper, implementation of a genetic algorithm has been described to store and later, recall of some prototype patterns in Hopfield neural network associative memory. Various operators of genetic algorithm (mutation, cross-over, elitism etc) are used to evolve the population of optimal weight matrices for the purpose of storing the patterns and then recalling of the patterns with induced noise was made, again using a genetic algorithm. The optimal weight matrices obtained during the training are used as seed for starting the GA in recalling, instead starting with random weight matrix. A detailed study of the comparison ofresults thus obtained with the earlier results has been done. It has been observed that for Hopfield neural networks, recall of patterns is more successful if evolution of weight matrices is applied for training purpose also.