Adaptive genetic algorithm based on a new entropy measurement
Qiang Ma, Jiang-Chuan Chen, Xiaoyan Xu, Yabin Shao · 2014
In this paper, we propose an adaptive genetic algorithm based on a new entropy measurement, and deduce the limit of the selection probabilities of individuals under the entropy measurement. The theoretical analysis and a comparative experiment show that the new selection strategy based on the new entropy measurement can adjust dynamically the selection intensity according to the population state. The proposed method shifts dynamically the balance between the exploitation and exploration performance of genetic algorithms to enhance global optimal performance of algorithm.