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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)