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Improved WTA problem solving method using a parallel genetic algorithm which applied the RMI initialization method

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dc.contributor.authorHong, S.-S.-
dc.contributor.authorYun, J.-
dc.contributor.authorChoi, B.-
dc.contributor.authorKong, J.-
dc.contributor.authorHan, M.-M.-
dc.date.available2020-02-29T09:45:46Z-
dc.date.created2020-02-11-
dc.date.issued2012-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/17514-
dc.description.abstractThe problem of Weapon Target Allocation (WTA) is to find an optimum solution, the type of vector that our weapons assign to targets, to minimize the damage of our assets from the target of an enemy offending us. we proposed the novel parallel genetic algorithm for solved to the WTA problem. The proposed. As the first step, our proposed algorithm is to expand the problem search space through the Random Mutation Inherit (RMI) population initialization method thereby improving convergence performance. We proposed an algorithm which obtains the WTA solution quickly and solves the WTA problem efficiently. © 2012 IEEE.-
dc.language영어-
dc.language.isoen-
dc.relation.isPartOf6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012-
dc.subjectConvergence performance-
dc.subjectIntialization-
dc.subjectParallel genetic algorithms-
dc.subjectParallel process-
dc.subjectPopulation initializations-
dc.subjectProblem Solving methods-
dc.subjectWeapon assignment-
dc.subjectWeapon-target allocation-
dc.subjectGenetic algorithms-
dc.subjectOptimization-
dc.subjectSoft computing-
dc.subjectIntelligent systems-
dc.titleImproved WTA problem solving method using a parallel genetic algorithm which applied the RMI initialization method-
dc.typeArticle-
dc.type.rimsART-
dc.description.journalClass1-
dc.identifier.doi10.1109/SCIS-ISIS.2012.6505315-
dc.identifier.bibliographicCitation6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012, pp.2189 - 2193-
dc.identifier.scopusid2-s2.0-84877796701-
dc.citation.endPage2193-
dc.citation.startPage2189-
dc.citation.title6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012-
dc.contributor.affiliatedAuthorHong, S.-S.-
dc.contributor.affiliatedAuthorYun, J.-
dc.contributor.affiliatedAuthorChoi, B.-
dc.contributor.affiliatedAuthorKong, J.-
dc.contributor.affiliatedAuthorHan, M.-M.-
dc.type.docTypeConference Paper-
dc.subject.keywordAuthorGenetic Algorithm-
dc.subject.keywordAuthorOptimization-
dc.subject.keywordAuthorParallel Process-
dc.subject.keywordAuthorPopulation Intialization-
dc.subject.keywordAuthorWeapon Assignment-
dc.subject.keywordPlusConvergence performance-
dc.subject.keywordPlusIntialization-
dc.subject.keywordPlusParallel genetic algorithms-
dc.subject.keywordPlusParallel process-
dc.subject.keywordPlusPopulation initializations-
dc.subject.keywordPlusProblem Solving methods-
dc.subject.keywordPlusWeapon assignment-
dc.subject.keywordPlusWeapon-target allocation-
dc.subject.keywordPlusGenetic algorithms-
dc.subject.keywordPlusOptimization-
dc.subject.keywordPlusSoft computing-
dc.subject.keywordPlusIntelligent systems-
dc.description.journalRegisteredClassscopus-
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