Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7571
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dc.contributor.authorLiang, Yongen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorMok, Tony Shu Kamen_US
dc.date.accessioned2023-03-24T02:50:39Z-
dc.date.available2023-03-24T02:50:39Z-
dc.date.issued2007-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, vol. 4490 LNCS, Issue PART 4, Pages 1099 - 1107en_US
dc.identifier.isbn978-354072589-3-
dc.identifier.issn03029743-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7571-
dc.description.abstractIn this paper, we propose a novel fast evolutionary algorithm - cycle-wise genetic algorithm (CWGA) based on the theoretical analyses of a drug scheduling mathematical model for cancer chemotherapy. CWGA is more efficient than other existing algorithms to solve the drug scheduling optimization problem. Moreover, its simulation results match well with the clinical treatment experience, and can provide much more drug scheduling policies for a doctor to choose depending on the particular conditions of the patients. CWGA also can be widely used to solve other kinds of the real dynamic systems. © Springer-Verlag Berlin Heidelberg 2007.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.titleFast drug scheduling optimization approach for cancer chemotherapyen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1007/978-3-540-72590-9_165-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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