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在 PBIL算法及自私基因算法的基础上 ,提出了一个适应性更广、搜索能力更强的优化搜索算法。该算法从各基因位的初始等位基因概率出发 ,通过一系列概率采样、选择与搜索、概率修正等操作 ,使搜索空间逐步收敛于最优点。该算法既吸取了遗传算法的群体搜索的特点 ,又吸收了局部搜索算法的局部搜索能力强的优点。最后介绍了该算法在图论中的几个应用实例。
Based on the PBIL algorithm and selfish gene algorithm, a more adaptive search algorithm is proposed. The algorithm starts from the initial allele probability of each locus and makes the search space gradually converge to the optimal point through a series of operations such as probabilistic sampling, selection and search, probability correction and so on. The algorithm not only draws the characteristics of the group search of genetic algorithm, but also absorbs the advantage of the local search ability of the local search algorithm. Finally, several application examples of the algorithm in graph theory are introduced.