Improving Genetic Algorithms with Solution Space Partitioning and Evolution Refinements
學年 96
學期 1
發表日期 2007-08-24
作品名稱 Improving Genetic Algorithms with Solution Space Partitioning and Evolution Refinements
作品名稱（其他語言）
著者 Liou, Ay-hwa Andy; Chi, Tzong-heng; Yu, I-jun
作品所屬單位 淡江大學資訊管理學系
出版者
會議名稱 Natural Computation, 2007. ICNC 2007. Third International Conference on
會議地點 Haikou, China
摘要 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.
關鍵字 Evolution Refinement;Genetic Algorithms;Graph Theory;Irregularity Sum;Problem Decomposition
語言 en
收錄於
會議性質 國際
校內研討會地點
研討會時間 20070824~20070827
通訊作者
國別 CHN
公開徵稿 Y
出版型式
出處 Natural Computation, 2007. ICNC 2007. Third International Conference on, v.4, pp.238-242
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/68439 )

機構典藏連結