Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7598
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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, Shu-Kam Tonyen_US
dc.date.accessioned2023-03-27T03:21:15Z-
dc.date.available2023-03-27T03:21:15Z-
dc.date.issued2005-
dc.identifier.citation2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings, vol. 1, pp. 699 - 706en_US
dc.identifier.isbn0780393635-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7598-
dc.description.abstractThis paper proposes a new memetic algorithm (MA) to solve the Multi-drug chemotherapy optimization problem. The new MA combines GA with a local search algorithm called Iterative Dynamic Programming (IDP). A multi-drug chemotherapy model is introduced to simulate the possible response of the tumor cells under drugs administration. Optimization of the multiple chemotherapeutic agents' administration schedules is based on this tumor model. We formulate the optimization problem as an optimal control problem (OCP) with a set of dynamic equations. The objective is to design efficient schedules which minimize the tumor size under a set of constraints. Our new MA has been shown to be very efficient on solving our Multi-drug model. © 2005 IEEE.en_US
dc.language.isoenen_US
dc.relation.ispartof2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedingsen_US
dc.titleMulti-drug cancer chemotherapy scheduling by a new memetic optimization algorithmen_US
dc.typeConference Paperen_US
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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