Time complexity analysis of an evolutionary algorithm for finding nearly maximum cardinality matching

Most of works on the time complexity analysis of evolutionary algorithms have always focused on some artificial binary problems The time complexity of the algorithms for combinatorial optimisation has not been well understood. This paper considers the time complexity of an evolutionary algorithm for a classical combinatorial optimisation problem, to find the maximum cardinality matching in a graph. It is shown that the evolutionary algorithm can produce, a matching with nearly maximum cardinality in average polynomial time. The work is partially supported by Engineering and Physical Sciences Research Council (GR/R52541/01) and State Key Lab of Software Engineering at Wuhan University. The work was reported at UK 2002 Workshop on Computational Intelligence. Jun He received his M.Sc. degree in mathematics and Ph.D. degree in computer science from Wuhan University, China, in 1992 and 1995 respectively. He is currently a research fellow at University of Birmingham, England. His research interests include evolutionary computation, network security and parallel algorithms. Xin Yao received the Ph.D. degree in computer science from the Univ. Sci. Tech. China in 1990. He is currently a professor of computer science at the University of Birmingham, England. He is a fellow of IEEE. His major research interests include evolutionary computation, neural network ensembles, co-evolution, evolvable hardware, time complexity of evolutionary algorithms, and data mining. He published extensively in these areas.