Lamarckian evolution of associative memory

Akira Imada, Keijiro Araki

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)

Abstract

There have been a lot of researches which apply evolutionary techniques to layered neural networks. However, their applications to Hopfield neural networks remain few so far. We are applying genetic algorithms to fully connected associative memory model of Hopfield. In an earlier paper, we reported that random weight matrices were evolved to store some number of patterns only by means of a simple genetic algorithm. In this paper we present that the storage capacity can be enlarged by incorporating Lamarckian inheritance to the genetic algorithm.

Original languageEnglish
Pages676-680
Number of pages5
Publication statusPublished - Jan 1 1996
EventProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96 - Nagoya, Jpn
Duration: May 20 1996May 22 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96
CityNagoya, Jpn
Period5/20/965/22/96

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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