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Grammatical Development of Evolutionary Modular Neural Networks

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Simulated Evolution and Learning (SEAL 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1585))

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Abstract

Evolutionary algorithms have shown a great potential to develop the optimal neural networks that can change the architectures and learning rules according to the environments. In order to boost up the scalability and utilization, grammatical development has been considered as a promising encoding scheme of the network architecture in the evolutionary process. This paper presents a preliminary result to apply a grammatical development method called L-system to determine the structure of a modular neural network that was previously proposed by the authors. Simulation result with the recognition problem of handwritten digits indicates that the evolved neural network has reproduced some of the characteristics of natural visual system, such as the organization of coarse and fine processing of stimuli in separate pathways.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul, 120-749, Korea

    Sung-Bae Cho & Sung-Bae Cho

  2. ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-02, Japan

    Katsunori Shimohara

Authors
  1. Sung-Bae Cho
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  2. Katsunori Shimohara
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Editor information

Editors and Affiliations

  1. School of Computer Science, University College, UNSW Australian Defence Force Academy, Canberra, ACT, Australia, 2600

    Bob McKay , Xin Yao  & Charles S. Newton ,  & 

  2. Department of Electrical Engineering Korea Advanced Institute of Science and Technology, 373-1, Kusung-dong, Yusung-gu, Taejon-shi, 305-701, Korea

    Jong-Hwan Kim

  3. Department of Information Electronics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

    Takeshi Furuhashi

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© 1999 Springer-Verlag Berlin Heidelberg

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Cho, SB., Shimohara, K. (1999). Grammatical Development of Evolutionary Modular Neural Networks. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_53

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  • DOI: https://doi.org/10.1007/3-540-48873-1_53

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65907-5

  • Online ISBN: 978-3-540-48873-6

  • eBook Packages: Springer Book Archive

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