为了有效地处理动态优化问题，提出一种短时记忆与长时记忆相结合的混合记忆策略. 被记忆的信息由最优个体与种群概率向量组成. 短时记忆作用于进化过程中的每一代，对记忆中的信息进行提取并构建下一代的种群；长时记忆仅在环境发生变化时产生作用，取新环境中最优的一组信息对短时记忆进行赋值. 该文首先构建了动态环境下基于混合记忆策略的遗传算法，然后在非周期、周期和带噪声周期动态环境下进行算法的性能验证. 实验结果表明，新算法处理动态优化问题的能力优于同类算法.

In order to effectively solve dynamic optimization problems, a new hybrid memory scheme that consists of short-term memory and long-term memory is proposed. Information to be memorized includes the best individual and the probability vector of current population. Information of short-term memory is extracted to build the next population in each generation. Long-term memory is assigned for the short-term memory when a environmental change is detected. A new genetic algorithm is thus constructed based on the hybrid memory. Performance of the algorithm is verified in different environments including non-cyclic, cyclic, and cyclic with noise. Computation results indicate that this algorithm is superior to similar algorithms in dealing with dynamic optimization problems.