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dc.contributor.authorKuo, Ten_US
dc.contributor.authorHwang, SYen_US
dc.date.accessioned2014-12-08T15:02:57Z-
dc.date.available2014-12-08T15:02:57Z-
dc.date.issued1996en_US
dc.identifier.issn0288-3635en_US
dc.identifier.urihttp://hdl.handle.net/11536/1555-
dc.description.abstractWhat makes a problem easy or hard for a genetic algorithm (GA)? Much previous work has studied this question by applying Walsh analysis.(4)) In this paper, we demonstrate a function that is GA-hard by analyzing the Walsh coefficients of this function's Walsh decomposition. Then, we construct five functions with differing degrees of difficulty for genetic algorithms. Some are GA-easy and some are GA-hard. In a previous paper,(29)) wh have proposed a novel selection method, disruptive selection. This method devotes more trials to both better solutions and worse solutions than it does to moderate solutions, whereas the conventional method allocates its attention according to the performance of each solution. Experimental results show that DGAs (GAs using disruptive selection) perform very well on both GA-easy and GA-hard functions. Finally, we discuss why DGAs outperform conventional GAs.en_US
dc.language.isoen_USen_US
dc.subjectgenetic algorithmen_US
dc.subjectdisruptive selectionen_US
dc.subjectWalsh analysisen_US
dc.subjectdeceptive functionen_US
dc.titleWhy DGAs work well on GA-hard functions?en_US
dc.typeArticleen_US
dc.identifier.journalNEW GENERATION COMPUTINGen_US
dc.citation.volume14en_US
dc.citation.issue4en_US
dc.citation.spage459en_US
dc.citation.epage479en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:A1996VL52700003-
dc.citation.woscount2-
Appears in Collections:Articles