A Multi-objective optimal evolutionary algorithm based on tree-ranking

Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcomings, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time. Foundation item: Supported by the National Natural Science Foundation of China(60073043, 70071042, 60133010) Biography: Shi Chuan( 1978-), male, Master candidate, research direction; intellective computation, evolutionary computation.