Abstract
This paper proposes an effective particle swarm optimization (PSO) based memetic algorithm (MA) for designing artificial neural network. In the proposed PSO-based MA (PSOMA), not only the evolutionary searching mechanism of PSO characterized by individual improvement plus population cooperation and competition is applied to perform the global search, but also several adaptive high-performance faster training algorithms are employed to enhance the local search, so that the exploration and exploitation abilities of PSOMA can be well balanced. Moreover, an effective adaptive Meta-Lamarckian learning strategy is employed to decide which local search method to be used so as to prevent the premature convergence and concentrate computing effort on promising neighbor solutions. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed PSOMA.
This work is supported by National Natural Science Foundation of China (Grant No. 60204008, 60374060 and 60574072) and National 973 Program (Grant No. 2002CB312200).
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Liu, B., Wang, L., Jin, Y., Huang, D. (2007). Designing Neural Networks Using PSO-Based Memetic Algorithm. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_28
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DOI: https://doi.org/10.1007/978-3-540-72395-0_28
Publisher Name: Springer, Berlin, Heidelberg
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