An Improved Genetic Algorithm Based on k-means

Abstract

The traditional genetic algorithm has the disadvantage of slow convergence speed and prematurity. In order to optimize the algorithm from the perspective of spatial analysis, a multi-granular genetic algorithm proposes a spatial partitioning method based on a completely random tree to improve the genetic algorithm. However, the accurate analysis of space by completely random trees is time-consuming. Therefore, an improved genetic algorithm based on k-mean is proposed in this paper. The individuals obtained by the genetic algorithm are clustered through k-means. Then, according to the clustering results, new individuals are generated in the subspace containing a small number of individuals and in the subspace to which the current optimal solution belongs, thus improving the performance of the genetic algorithm.