Evolutionary Learning of Modular Neural Networks withGenetic Programming

Authors:
Sung-Bae Cho;Katsunori Shimohara
Affiliations:
Department of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Korea/ and ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Sor ...;ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan
Venue:
Applied Intelligence
Year:
1998

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Abstract

Evolutionary design of neural networks has shown a great potential asa powerful optimization tool. However, most evolutionary neural networkshave not taken advantage of the fact that they can evolve from modules.This paper presents a hybrid method of modular neural networks andgenetic programming as a promising model for evolutionary learning.This paper describes the concepts and methodologies for the evolvablemodel of modular neural networks, which might not only develop newfunctionality spontaneously, but also grow and evolve its own structureautonomously. We show the potential of the method by applying an evolvedmodular network to a visual categorization task with handwritten digits.Sophisticated network architectures as well as functional subsystemsemerge from an initial set of randomly-connected networks. Moreover,the evolved neural network has reproduced some of the characteristicsof natural visual system, such as the organization of coarse and fineprocessing of stimuli in separate pathways.