基于聚类锦标赛与父代匹配的遗传规划算法

Genetic programming algorithm based on cluster tournament and parent matching

在遗传规划算法中,种群多样性在避免早熟收敛方面有重要作用,通过控制种群多样性改进算法是遗传规划算法的研究热点。从多样性角度改进算法的选择机制,提出一种基于聚类锦标赛与父代匹配的遗传规划算法。通过聚类将种群划分为多个子种群,从而调整算法的选择压力以维持种群多样性,提高算法的搜索能力。此外,提取个体的二进制特征,利用局部匹配对父代进行针对性交叉操作,从父代成对多样性的角度实现算法在探索和开发之间的较好平衡。对不同基准问题进行了多个对比实验,实验结果表明所提算法在种群多样性上有较大改善,在寻优能力和收敛速度上均取得了较好的提升。

In the genetic programming algorithm,population diversity plays an important role in avoiding premature convergence.Improving algorithms by controlling population diversity is a hot spot in genetic programming.Therefore,this paper improves the selection mechanism of the algorithm from the perspective of diversity,and proposes a genetic programming algorithm based on clustering tournament mechanism and parent generation matching.The algorithm divides the population into multiple sub populations through clustering so as to adjust the selection pressure of the algorithm to maintain population diversity and to improve the search ability of the algorithm.In addition,the algorithm extracts individual binary features and uses local matching to cross operate the parent generation.From the perspective of parent pair diversity,the algorithm achieves a better balance between the exploration and the exploitation.Multiple comparative experiments on different benchmark problems verify that the population diversity of the proposed algorithm is greatly improved and the optimization ability and convergence speed are better improved.