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Evolution of associative memory using diploid chromosomes (1997).
| Content Provider | CiteSeerX |
|---|---|
| Author | Chromosomes, Diploid Imada, Akira Araki, Keijiro |
| Abstract | . We apply genetic algorithms to the Hopfield's neural network model of associative memory. Previously, we reported that random synaptic weights of the network evolved to create the fixed point attractors exactly at the locations of given patterns. Furthermore, we reported that the genetic algorithm can evolve the Hebbian synaptic weights to enlarge the storage capacity. In those experiments, the genetic algorithm pruned a certain fraction of the synaptic connections adaptively with using haploid chromosomes. In this paper, we present the evolution can also be made by using diploid chromosomes. 1 Introduction Associative memory is a dynamical system which has a number of stable states with a domain of attraction around them [1]. If the system starts at any state in the domain, it will converge to the stable state. In 1982, Hopfield [2] proposed a fully connected neural network model of associative memory in which we can store information by distributing it among neurons, and we can re... |
| File Format | |
| Publisher Date | 1997-01-01 |
| Access Restriction | Open |
| Subject Keyword | Associative Memory Diploid Chromosome Genetic Algorithm Neural Network Model Stable State Random Synaptic Weight Haploid Chromosome Storage Capacity Synaptic Connection Fixed Point Attractor Introduction Associative Memory Hebbian Synaptic Weight Certain Fraction Dynamical System |
| Content Type | Text |
