Abstract：An algorithm for neural network structure design is proposed. The algorithm introduces indirect encoding schema to represent the structure of neural network and the cell in the 2-dimension cellular automate system to represent the existence of connection in neural network. By separately evolving the coordinate and value of the cell, the growing and pruning of the network structure are achieved. The coordinate of the cell is created and evolved by binary quantum particle swarm optimization (BQPSO). The value of the cell is evolved by using properly-designed neighboring evolving rule of cellular system, and the current network is trained by float-point QPSO. Thus, the final stable structure is found. The experimental results show that the proposed algorithm has stable complexity and convergent capability with different scales of neural network structure design.
包芳，潘永惠，孙俊，须文波. 基于细胞自动机和QPSO的间接编码神经网络结构设计算法*[J]. 模式识别与人工智能, 2009, 22(1): 148-155.
BAO Fang, PAN Yong-Hui, SUN Jun, XU Wen-Bo. Cellular Automate and QPSO Based Neural Network Structure Design by Indirect Encoding. , 2009, 22(1): 148-155.
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2009, Vol. 22 
