A New Mind Evolutionary Algorithm Based on Information Entropy

A new self-adaptive mind evolutionary algorithm based on information entropy is proposed to improve the algorithmic convergence especially in the late evolutionary time. As studied, the evolution of a population is a process with entropy reducing and the population entropy can be used to reflecting the evolution state. Thus the self-adaptive strategy can be realized by building population entropy computing module to estimate the region including global optimal solution. In this way, the exploring of the algorithm is more purposeful and sufficiently and the performance is improved. The experimental results show the algorithm is valid and advanced.