Balancing Learning And Evolution
نویسندگان
چکیده
Finding the right coupling of learning and evolution in a hybrid algorithm is an open problem. In this article, we present a strategy to adjust the time spent on learning during evolutionary optimization of neural networks. The proposed adaptation scheme leads to a significant improvement in performance. It is empirically shown that suitable learning strategies strongly depend on the problem and that it is advantageous to adapt the time spent on learning during evolution. Published in: W. B. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska (editors), GECCO 2002: Genetic and Evolutionary Computation Conference, pp. 391-398, Morgan Kaufmann Publishers, 2002
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تاریخ انتشار 2002