Proceedings of the seventh international conference (1990) on Machine learning
Introduction to artificial neural systems
Introduction to artificial neural systems
Feature and memory-selective error correction in neural associative memory
Associative neural memories
An introduction to genetic algorithms
An introduction to genetic algorithms
Learning and statistical inference
The handbook of brain theory and neural networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithm Enlarges the Capacity of Associative Memory
Proceedings of the 6th International Conference on Genetic Algorithms
Representation of Associated Data by Matrix Operators
IEEE Transactions on Computers
International Journal of Hybrid Intelligent Systems
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This paper describes the implementation of a genetic algorithm to evolve the population of weight matrices for storing and recalling the patterns in a Hopfield type neural network model. In the Hopfield type neural network of associative memory, the appropriate arrangement of synaptic weights provides an associative function in the network. The energy function associated with the stable state of this model represents the appropriate storage of the input patterns. The aim is to obtain the optimal weight matrix for efficient recall of any prototype input pattern. For this, we explore the population generation technique (mutation and elitism), crossover and the fitness evaluation function for generating the new population of the weight matrices. This process continues until the selection of the last weight matrix or matrices has been performed. The experiments incorporate a neural network trained with multiple numbers of patterns using the Hebbian learning rule. In most cases, the recalling of patterns using a genetic algorithm seems to give better results than the conventional recalling with the Hebbian rule. The simulated results suggest that the genetic algorithm is the better searching technique for recalling noisy prototype input patterns.