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| Cellular Automate and QPSO Based Neural Network Structure Design by Indirect Encoding |
| BAO Fang1,2, PAN Yong-Hui1,2, SUN Jun2, XU Wen-Bo2 |
1.School of Information Engineering, Jiangnan University, Wuxi 214122
2.Department of Computer Science, Jiangyin Polytechnic College, Jiangyin 214405 |
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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.
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Received: 20 August 2007
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2009, Vol. 22 
