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dc.contributor.authorChi, Tzong-Heng
dc.contributor.authorLin, Cheng-Li
dc.date.accessioned2009-06-02T07:06:41Z
dc.date.accessioned2020-05-25T06:47:30Z-
dc.date.available2009-06-02T07:06:41Z
dc.date.available2020-05-25T06:47:30Z-
dc.date.issued2009-02-12T06:46:59Z
dc.date.submitted2009-02-12
dc.identifier.urihttp://dspace.lib.fcu.edu.tw/handle/2377/11228-
dc.description.abstractLogistics network related problems are usually associated with geographical locations, but most of evolutionary computing heuristics such as genetic algorithms (GA) in solving them have not given appropriate labeling for locations. By our experiments, it can introduce fatal failure sometimes. However, once we took the linkage information into consideration, we found the linkage learning genetic algorithm (LLGA) is a more stable solution method possessing the independence of coding schemes for facility location problems than the simple genetic algorithm. Except for what mentioned above and many parameter-setting experiments, we also address the way to improve the performance of the LLGA with more but limited interpretation points. All of these can be good guidelines for users interested in applying the evolutionary computing to solve logistics network related problems.
dc.description.sponsorship淡江大學,台北縣
dc.format.extent6p.
dc.relation.ispartofseries2008 ICS會議
dc.subjectLinkage Learning
dc.subjectGenetic Algorithm
dc.subjectLogistics Networks
dc.subject.otherArtificial Intelligence
dc.titleThe Characteristic of Logistics Network Specific Coding in Genetic Algorithms
分類:2008年 ICS 國際計算機會議

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