Application of an evolution strategy to the Hopfield model of associative memory

Akira Imada, Keijiro Araki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

We apply evolutionary computations to Hopfield's neural network model of associative memory. In the Hopfield model, almost infinite number of combinations of synaptic weights give a network a function of associative memory. Furthermore, there is a trade-off between the storage capacity and size of basin of attraction. Therefore, the model can be thought of as a test suite of multi-modal and/or multi-objective function optimization. As preliminary stages, we investigate the basic behaviors of associative memory under simple evolutionary processes. In this paper, we present some experiments using an evolution strategy.

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation, ICEC
Editors Anon
PublisherIEEE
Pages679-683
Number of pages5
Publication statusPublished - 1997
Externally publishedYes
EventProceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97 - Indianapolis, IN, USA
Duration: Apr 13 1997Apr 16 1997

Other

OtherProceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97
CityIndianapolis, IN, USA
Period4/13/974/16/97

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

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  • Cite this

    Imada, A., & Araki, K. (1997). Application of an evolution strategy to the Hopfield model of associative memory. In Anon (Ed.), Proceedings of the IEEE Conference on Evolutionary Computation, ICEC (pp. 679-683). IEEE.