轮盘赌
聚类分析
遗传算法
基于群体的增量学习
计算机科学
适应度比例选择
人口
选择(遗传算法)
进化算法
渡线
文化算法
算法
数学优化
人工智能
机器学习
适应度函数
数学
几何学
人口学
社会学
作者
Weibo Xia,Lianshuan Shi,Rui Zhang,Jiale Zhang,Jiaxing Zhao
标识
DOI:10.1109/icivc58118.2023.10270272
摘要
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.
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