A Co-Evolutionary Genetic Algorithm Based on Improved K-Means Clustering

In this paper, we propose a co-evolutionary genetic algorithm based on k-means clustering improvement. In this algorithm, the initial populations are subjected to different operations and the two evolve collaboratively; firstly, the generated population individuals are k-means clustered separately for genetic operations, while the population individuals are combined with roulette selection to make the population undergo adaptive genetic operations, and secondly, after performing the operations separately, they are integrated in a certain proportion, and the two evolve collaboratively so that the overall performance of the genetic algorithm is improved. Finally, in this paper, a typical test function is used to test and analyze the algorithm, and the performance of the algorithm is significantly improved.