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dc.contributor.authorKuo, Ten_US
dc.contributor.authorHwang, SYen_US
dc.date.accessioned2014-12-08T15:02:44Z-
dc.date.available2014-12-08T15:02:44Z-
dc.date.issued1996-04-01en_US
dc.identifier.issn1083-4419en_US
dc.identifier.urihttp://dx.doi.org/10.1109/3477.485880en_US
dc.identifier.urihttp://hdl.handle.net/11536/1377-
dc.description.abstractGenetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. Applying the ''survival-of-the-fittest'' principle, traditional genetic algorithms allocate more trials to above-average schemata, However, increasing the sampling rate of schemata that are above average does not guarantee convergence to a global optimum; the global optimum could be a relatively isolated peak or located in schemata that have large variance in performance, In this paper we propose a novel selection method, disruptive selection. This method adopts a nonmonotonic fitness function that is quite different from traditional monotonic fitness functions, Unlike traditional genetic algorithms, this method favors both superior and inferior individuals. Experimental results show that GA's using the proposed method easily find the optimal solution of a function that is hard for traditional GA's to optimize, We also present convergence analysis to estimate the occurrence ratio of the optima of a deceptive function after a certain number of generations of a genetic algorithm, Experimental results show that GA's using disruptive selection in some occasions find the optima more quickly and reliably than GA's using directional selection, These results suggest that disruptive selection can be useful in solving problems that have large variance within schemata and problems that are GA-deceptive.en_US
dc.language.isoen_USen_US
dc.titleA genetic algorithm with disruptive selectionen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/3477.485880en_US
dc.identifier.journalIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICSen_US
dc.citation.volume26en_US
dc.citation.issue2en_US
dc.citation.spage299en_US
dc.citation.epage307en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department資訊科學與工程研究所zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentInstitute of Computer Science and Engineeringen_US
dc.identifier.wosnumberWOS:A1996UD02400008-
dc.citation.woscount33-
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