Evolving modular neural networks which generalise well

Yong LIU, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

In dealing with complex problems, a monolithic neural network often becomes too large and complex to design and manage. The only practical way is to design modular neural network systems consisting of simple modules. While there has been a lot of work on combining different modules in a modular system in the fields of neural networks, statistics and machine learning, little work has been done on how to design those modules automatically and how to exploit the interaction between individual module design and module combination. This paper proposes an evolutionary approach to designing modular neural networks. The approach addresses the issue of automatic determination of the number of individual modules and the exploitation of the interaction between individual module design and module combination. The relationship among different modules is considered during the module design. This is quite different from the conventional approach where the module design is separated from the module combination. Experimental results on some benchmark problems are presented and discussed in this paper.
Original languageEnglish
Title of host publicationProceedings of 1997 IEEE International Conference on Evolutionary Computation, ICEC '97
PublisherIEEE
Pages605-610
Number of pages6
ISBN (Print)0780339495
DOIs
Publication statusPublished - 1997
Externally publishedYes
Event1997 IEEE International Conference on Evolutionary Computation, ICEC ‘97 - Indianapolis, United States
Duration: 13 Apr 199716 Apr 1997

Conference

Conference1997 IEEE International Conference on Evolutionary Computation, ICEC ‘97
Country/TerritoryUnited States
CityIndianapolis
Period13/04/9716/04/97

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