Abstract: The hill-climbing method is a local search algorithm，which has a good local search performance mainly because its search process is guided by information between individuals.In contrast，Traditional Genetic Algorithm（TGA） is a global search algorithm，which does not consider information between individuals in the search process.The convergence of TGA is limited because it only uses individuals’ fitness to guide the search.This paper proposes a new algorithm in which oriented hill-climbing mechanism is added to genetic algorithm.The new algorithm is named Oriented Hill-Climbing based Genetic Algorithm（OHCGA） which combines merits of hill-climbing method and TGA.Through the comparison of individuals，the algorithm uses the oriented hill-climbing operator to guide search to promising areas.Numerical experiments show that OHCGA improves the convergence speed and the ability of search optimal solutions compared with TGA.

摘要： 爬山法是一种局部搜索能力相当好的算法，主要是因为它是通过个体的优劣信息来引导搜索的。而传统的遗传算法作为一种全局搜索算法，在搜索过程中却没有考虑个体间的信息，而仅依靠个体适应度来引导搜索，使得算法的收敛性受到限制。将定向爬山机制应用于遗传算法，提出了一种基于定向爬山的遗传算法（OHCGA）。该算法结合了爬山法与遗传算法的优点，通过比较个体的优劣，使用定向爬山操作引导算法向更优秀的解区域进行搜索。实验结果表明，与传统遗传算法（TGA）相比，OHCGA较大地提高了算法的收敛速度和搜索最优解的能力。