Lamarckian evolution of associative memory

Akira Imada; Keijiro Araki

Lamarckian evolution of associative memory

Akira Imada, Keijiro Araki

Advanced Software Engineering

Research output: Contribution to conference › Paper › peer-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 language	English
Pages	676-680
Number of pages	5
Publication status	Published - Jan 1 1996
Event	Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96 - Nagoya, Jpn Duration: May 20 1996 → May 22 1996

Other

Other	Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96
City	Nagoya, Jpn
Period	5/20/96 → 5/22/96

All Science Journal Classification (ASJC) codes

Engineering(all)

Access to Document

Fingerprint Dive into the research topics of 'Lamarckian evolution of associative memory'. Together they form a unique fingerprint.

Cite this

@conference{b215d6645e9d4c75874ad66de590f495,

title = "Lamarckian evolution of associative memory",

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.",

author = "Akira Imada and Keijiro Araki",

year = "1996",

month = jan,

day = "1",

language = "English",

pages = "676--680",

note = "Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96 ; Conference date: 20-05-1996 Through 22-05-1996",

}

TY - CONF

T1 - Lamarckian evolution of associative memory

AU - Imada, Akira

AU - Araki, Keijiro

PY - 1996/1/1

Y1 - 1996/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0029712943&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029712943&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0029712943

SP - 676

EP - 680

T2 - Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96

Y2 - 20 May 1996 through 22 May 1996

ER -