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dc.contributor.authorHung, Ping-Chuen_US
dc.contributor.authorChen, Ying-Pingen_US
dc.date.accessioned2019-04-02T06:04:44Z-
dc.date.available2019-04-02T06:04:44Z-
dc.date.issued2006-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/151035-
dc.description.abstractExtended compact genetic algorithm (ECCA) is an algorithm that can solve hard problems in the binary domain. ECCA is reliable and accurate because of the capability of detecting building blocks, but certain difficulties are encountered when we directly apply ECGA to problems in the integer domain. In this paper, we propose a new algorithm that extends ECGA, called integer extended compact genetic algorithm (iECCA). iECGA uses a modified probability model and inherits the capability of detecting building blocks from ECGA. iECGA is specifically designed for problems in the integer domain and can avoid the difficulties that ECGA encounters. With the experimental results, we show the performance comparisons between ECCA, iECGA, and a simple GA. The results indicate that iECGA has good performance on problems in the integer domain.en_US
dc.language.isoen_USen_US
dc.subjectextended compact genetic algorithmsen_US
dc.subjectinteger representationsen_US
dc.subjectgenetic linkageen_US
dc.subjectbuilding blocksen_US
dc.titleiECGA: Integer extended compact genetic algorithmen_US
dc.typeProceedings Paperen_US
dc.identifier.journalGECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2en_US
dc.citation.spage1415en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000249917300201en_US
dc.citation.woscount1en_US
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