期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2013
卷号：2
期号：12
页码：7785
出版社：S&S Publications
摘要：In this paper, new genetics and Ant colony optimization algorithm for solving the problem of graphcorrespondence is presented. When using the genetics technique for the problem of graph correspondence, it is not easyto define the crossover operator. our attempt will be to present a definition holding the integration of the populationgraph in a one-to-one correspondence. we present new and suitable definitions for the target function and a functiongiving score to a solution at the end of any cycle. We compare both algorithms and try to find their advantages andtheir shortcomings.
