Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7625
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dc.contributor.authorXu, Zong-Benen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLiang, Yongen_US
dc.contributor.authorLeung, Yeeen_US
dc.date.accessioned2023-03-28T03:54:33Z-
dc.date.available2023-03-28T03:54:33Z-
dc.date.issued2003-
dc.identifier.citationApplied Mathematics and Computation, 2003, vol. 142 ( 2-3), pp. 341 - 388en_US
dc.identifier.issn00963003-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7625-
dc.description.abstractThe efficiency speed-up strategies for evolutionary computation were discussed. Incorporation of the strategies with any known evolutionary algorithm leads to an accelerated version of the algorithm. An arbitrarily high-precision (resolution) solution of a high-dimensional problem was obtained by means of a successive low-resolution search in low-dimensional search spaces. The fast-genetic algorithms were experimentally tested with a test suit containing 10 complex multimodal function optimization problems.en_US
dc.language.isoenen_US
dc.relation.ispartofApplied Mathematics and Computationen_US
dc.titleEfficiency speed-up strategies for evolutionary computation: Fundamentals and fast-GAsen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1016/S0096-3003(02)00309-0-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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