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《Journal of Tsinghua University(Science and Technology)》 2002-03
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Adaptive genetic algorithm based on population entropy estimating

JIANG Rui 1, LUO Yupin 1, HU Dongcheng 1, SZETO Kwok Yip 2(1. Department of Automation, Tsinghua University, Beijing 100084, China; 2. Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay Kowloon Hong Kong, Chin  
This paper presents a parameter adaptive genetic algorithm based on the entropy estimating to balance exploration and exploitation in the problem's solution space while doing optimization. In the algorithm, the new population in each generation consists of three sub populations: a preserved part, a reproduced part and a randomized part with corresponding parameters introduced to control the size of each part. The parameters can be adjusted adaptively by incorporating population entropy into the algorithm to provide a quantitative measure of the diversity of individuals in the population and by adopting a simple yet practical method to estimate the entropy of a given population. Experimental data show that the algorithm can effectively balance the exploration and exploitation and provides excellent performance with complex problems.
【CateGory Index】: TP18
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