Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7632
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dc.contributor.authorCheang, Sin Manen_US
dc.contributor.authorLee, Kin Hongen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.date.accessioned2023-03-28T04:39:00Z-
dc.date.available2023-03-28T04:39:00Z-
dc.date.issued2003-
dc.identifier.citationCheang, Sin Man, Lee, Kin Hong & Leung, Kwong Sak (2003). Improving evolvability of genetic parallel programming using dynamic sample weighting. In Cantú-Paz, Erick, Foster, James A., Deb, Kalyanmoy, Davis, Lawrence David, Roy, Rajkumar, O'Reilly, Una-May, Beyer, Hans Georg, Standish, Russell, Kendall, Graham, Wilson, Stewart, Harman, Mark, Wegener, Joachim, Dasgupta, Dipankar, Potter, Mitch A., Schultz, Alan C., Dowsland, Kathryn A.. Jonoska, Natasha & Miller, Julian (Eds.). Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003, Chicago, USA (1802-1803). Springer Berlin, Heidelberg.en_US
dc.identifier.isbn9783540451105-
dc.identifier.isbn9783540406037-
dc.identifier.issn03029743-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7632-
dc.description.abstractThis paper investigates the sample weighting effect on Genetic Parallel Programming (GPP) that evolves parallel programs to solve the training samples captured directly from a real-world system. The distribution of these samples can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation and cause premature convergence. This paper compares the performance of four sample weighting (SW) methods, namely, Equal SW (ESW), Class-equal SW (CSW), Static SW (SSW) and Dynamic SW (DSW) on five training sets. Experimental results show that DSW is superior in performance on tested problems. © Springer-Verlag Berlin Heidelberg 2003.en_US
dc.language.isoenen_US
dc.publisherSpringer Berlin, Heidelbergen_US
dc.titleImproving evolvability of genetic parallel programming using dynamic sample weightingen_US
dc.typeConference Paperen_US
dc.relation.conferenceGenetic and Evolutionary Computation — GECCO 2003en_US
dc.identifier.doi10.1007/3-540-45110-2_72-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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