| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 94 === This study presents a new algorithm to solve combination problems. The main purpose of this research is to find a set of pareto solutions with both natures of convergence and diversity. The heuristic proposed in this research uses Mining Gene Structures with Inheritance Sub-Population Genetic Algorithm (MGISPGA) to solve multi-objective flowshop scheduling problems, multi-objective parallel machine scheduling problems and multiple knapsack problems. The mining gene structure used in MGISPGA can be divided into three categories: the simple mining gene structure (SMGS), weighted mining gene structure (WMGS ) ,and the threshold mining gene structure（TWMGS）. The experimental results of MGISPGA used in this research will be compared with three evolving algorithms, SPGA, NSGA-II and SPEA2, and three kinds of performance metrics: , R metric ,and C metric are utilized as the measurement tools. The finding shows that overall speaking, MGISPGA has better solution in convergence and diversity. Besides, among these three kinds of gene structure methods, TWMGS has the best performance. Through the experiments, MGISPGA coucld be an effective approach for solving combination problems.
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