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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/68439

    Title: Improving Genetic Algorithms with Solution Space Partitioning and Evolution Refinements
    Authors: Liou, Ay-hwa Andy;Chi, Tzong-heng;Yu, I-jun
    Contributors: 淡江大學資訊管理學系
    Keywords: Evolution Refinement;Genetic Algorithms;Graph Theory;Irregularity Sum;Problem Decomposition
    Date: 2007-08
    Issue Date: 2011-10-23 12:56:23 (UTC+8)
    Abstract: Irregular sum problem (ISP) is an issue resulted from mathematical problems and graph theories. It has the characteristic that when the problem size is getting bigger, the space of the solution is also become larger. Therefore, while searching for the feasible solution, the larger the question the harder the attempt to come up with an efficient search. We propose a new genetic algorithm, called the Incremental Improving Genetic Algorithm (IIGA), which is considered efficient and has the capability to incrementally improve itself from partial solutions. The initial solutions can be constructed by satisfying the constraints in stepwise fashion. The effectiveness of IIGA also comes from the allowing of suitable percentage of illegal solutions during the evolution for increasing the effectiveness of searching. The cut-point of the genetic coding for generating the descendants has carefully planned so that the algorithm can focus on the key factors for the contradiction and has the chances to fix it. After comparing the results of IIGA and usual genetic algorithm among different graphs, we found and shown that the performance of IIGA is truly better.
    Relation: Natural Computation, 2007. ICNC 2007. Third International Conference on, v.4, pp.238-242
    DOI: 10.1109/ICNC.2007.439
    Appears in Collections:[Graduate Institute & Department of Information Management] Proceeding

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