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We propose a genetic algorithm for mutually connected neural networks to obtain a higher capacity of associative memory. In Hopeld network as an associative memory system, the memory capacity is at most 15% of the number of neurons. Here we applied our method to the Hopeld network, and obtained the capacity of 33%. We conjectured that this is due to both… (More)

We apply evolutionary algorithms to Hopeld model of as-sociative memory. Previously we reported that a genetic algorithm using ternary chromosomes evolves the Hebb-rule associative memory to enhance its storage capacity by pruning some connections. This paper describes a genetic algorithm using real-encoded chromosomes which successfully evolves overloaded… (More)

| There have been a lot of researches which apply evolutionary techniques to layered neural networks. However, their applications to Hopeld neu-ral networks remain few so far. We have been applying Genetic Algorithms to fully connected associa-tive memory model of Hopeld, and reported elsewhere that the network can store some number of patterns only by… (More)

We apply evolutionary computations to Hopeld model of associative memory. Although there have been a lot of researches which apply evolutionary techniques to layered neural networks, their applications to Hopeld neural networks remain few so far. Previously we reported that a genetic algorithm using discrete encoding chromosomes evolves the Hebb-rule… (More)