Lamarckian evolution of associative memory

Akira Imada, Keijiro Araki

研究成果: Contribution to conferencePaper査読

6 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ676-680
ページ数5
出版ステータス出版済み - 1 1 1996
イベントProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96 - Nagoya, Jpn
継続期間: 5 20 19965 22 1996

その他

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

All Science Journal Classification (ASJC) codes

  • 工学(全般)

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