Multilevel cooperative coevolution for large scale optimization

In this paper, we propose a multilevel cooperative coevolution (MLCC) framework for large scale optimization problems. The motivation is to improve our previous work on grouping based cooperative coevolution (EACC-G) [1], which has a hard-to-determine parameter, group size, in tackling problem decomposition. The problem decomposer takes group size as parameter to divide the objective vector into low dimensional subcomponents with a random grouping strategy. In the MLCC, a set of problem decomposers is constructed based on the random grouping strategy with different group sizes. The evolution process is divided into a number of cycles, and at the start of each cycle MLCC uses a self-adapted mechanism to select a decomposer according to its historical performance. Since different group sizes capture different interaction levels between the original objective variables, MLCC is able to selfadapt among different levels. The efficacy of the proposed MLCC is evaluated on the set of benchmark...

• Type: Conference
• Year: 2008
• Where: CEC
• Authors: Zhenyu Yang, Ke Tang, Xin Yao
• Keywords: Artificial Intelligence, CEC 2008, Cooperative Coevolution, Group Sizes, Random Grouping Strategy