Structural Evolution of Neural Networks Having Arbitrary Connections by a Genetic Method

Tomoharu NAGAO  Takeshi AGUI  Hiroshi NAGAHASHI  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E76-D   No.6   pp.689-697
Publication Date: 1993/06/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Bio-Cybernetics
Keyword: 
neural network,  Genetic Algorithms,  optimization,  

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Summary: 
A genetic method to generate a neural network which has both structure and connection weights adequate for a given task is proposed. A neural network having arbitrary connections is regarded as a virtual living thing which has genes representing its connections among neural units. Effectiveness of the network is estimated from its time sequential input and output signals. Excellent individuals, namely appropriate neural networks, are generated through generation iterations. The basic principle of the method and its applications are described. As an example of evolution from randomly generated networks to feedforward networks, an XOR problem is dealt with, and an action control problem is used for making networks containing feedback and mutual connections. The proposed method is available for designing a neural network whose adequate structure is unknown.