Abstract
Artificial neural networks (ANNs) have powerful function approximation and pattern classification capabilities, but their performance is greatly affected by structural design and parameter selection. Traditional training methods have drawbacks including long training time, over-fitting, premature convergence, etc. Evolutionary optimization algorithms have provided an effective tool for ANN parameter optimization, but simultaneously optimizing ANN structure and parameters remains a difficult problem. This paper adapts a relatively new evolutionary algorithm, water wave optimization (WWO), for both structure design and parameter selection for ANNs. The algorithm uses a variable-dimensional solution representation, and designs new propagation, refraction, and breaking operators to effectively evolve solutions towards the optimum or near-optima. Computational experiments show that the WWO algorithm exhibits significant performance advantages over other popular evolutionary algorithms including genetic algorithm, particle swarm optimization, and biogeography-based optimization, for ANN structure and parameter optimization.
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Acknowledgements
This work is supported by National Natural Science Foundation (Grant No. 61473263) and Zhejiang Provincial Natural Science Foundation (Grant No. LY14F030011) of China.
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Zhou, XH., Xu, ZG., Zhang, MX., Zheng, YJ. (2018). Water Wave Optimization for Artificial Neural Network Parameter and Structure Optimization. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_30
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DOI: https://doi.org/10.1007/978-981-13-2826-8_30
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