Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7570
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dc.contributor.authorTse, Sui-Manen_US
dc.contributor.authorLiang, Yongen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLee, Kin-Hongen_US
dc.contributor.authorMok, Tony Shu-Kamen_US
dc.date.accessioned2023-03-24T02:22:41Z-
dc.date.available2023-03-24T02:22:41Z-
dc.date.issued2007-
dc.identifier.citationIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2007, vol. 37 (1) , pp. 84 - 91en_US
dc.identifier.issn10834419-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7570-
dc.description.abstractThis correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The objective is to minimize the tumor size under a set of constraints. We combine the adaptive elitist genetic algorithm with a local search algorithm called iterative dynamic programming (IDP) to form a new memetic algorithm (MA-IDP) for solving the problem. MA-IDP has been shown to be very efficient in solving the multidrug scheduling optimization problem. © 2007 IEEE.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cyberneticsen_US
dc.titleA memetic algorithm for multiple-drug cancer chemotherapy schedule optimizationen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1109/TSMCB.2006.883265-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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