为了重用进化过程中沉淀在优秀个体集中的信息，设计了最大频繁序列模式挖掘算法，并在其基础上提出了重用最大频繁模式的可持续进化算法( MFPEA) ．该算法设置了多个不同层次的种群为不同适应度水平的个体提供生存空间，采用最大频繁序列模式挖掘算法挖掘种群中的优良基因，并将具有优良基因模块的新个体注入到不同适应度水平的种群中．文中还设计了针对不同问题动态调整进化种群规模的函数，通过一组统计数据研究了平衡计算时间与进化质量的相关参数． 实验结果表明，MFPEA 在维持遗传信息稳定性、避免早熟收敛方面表现良好，且获得了xit1083 问题的新最优解记录( 3611. 496) ．

In order to make good reuse of the information precipitated in excellent individuals during the evolutionary process,a maximal frequent sequential pattern mining algorithm ( MFSPMA) is proposed,based on which a sustainable evolutionary algorithm for reusing the maximum frequent patterns is put forward and is abbreviated to MFPEA. In MFPEA,several subpopulations are adopted to provide survival space for the individuals with different
fitness levels,MFSPMA is used to extract excellent genes from the population,and new individuals with excellent gene schema are poured into the subpopulations to stabilize the inheritance of genetic information. Furthermore,a self-adaptive function is designed to adjust the population size for different problems,and a series of statistical data is used to investigate the parameters balancing the computation time and the evolutionary quality. Experimental results show that MFPEA performs good functions in maintaining information stability and avoiding premature convergence,and that it sets a new tour record,namely 3611. 496,for the xit1083 instance.