中文摘要：

针对合作协同进化算法（CCEA）动态适值空间的特点,研究信息补偿方法以消除由问题分解所导致的病态现象,并提出基于动态多种群进化策略的抗病态CCEA.每个协进化种群可动态分离出多个变化的子种群,利用它们同时获取多个全局或局部最优解作为交互信息,以实现信息补偿.针对引发病态行为的标准测试函数,与3种典型CCEA进行比较分析,实验结果表明所提出算法能有效克服病态现象,具有良好的全局优化能力.

英文摘要：

In order to counteract the pathologies of cooperative coevolutionary algorithms（CCEAs） caused by information loss when dealing with problem decomposition, the information compensation strategy is investigated with respect to the dynamic nature of the landscapes of the CCEAs. A dynamic multi-population evaluation based anti-pathology CCEA is proposed. In the algorithm, several dynamic child populations can be split off from a coevolutionary population and search global or local optimum which are used as the interacting information to compensate information. Two pathology-causing benchmark problems are used to test and compare the proposed algorithm to three classical CCEAs. Experimental results show that the proposed algorithm effectively counteract the relative overgeneralization pathology and significantly improve the rate of global-optimization convergence.