Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7571
Title: | Fast drug scheduling optimization approach for cancer chemotherapy |
Authors: | Liang, Yong Prof. LEUNG Kwong Sak Mok, Tony Shu Kam |
Issue Date: | 2007 |
Publisher: | Springer Verlag |
Source: | Lecture 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 - 1107 |
Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Abstract: | In 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. |
Type: | Conference Paper |
URI: | http://hdl.handle.net/20.500.11861/7571 |
ISBN: | 978-354072589-3 |
ISSN: | 03029743 |
DOI: | 10.1007/978-3-540-72590-9_165 |
Appears in Collections: | Applied Data Science - Publication |
Find@HKSYU Show full item record
SCOPUSTM
Citations
1
checked on Nov 3, 2024
Page view(s)
40
Last Week
0
0
Last month
checked on Nov 13, 2024
Google ScholarTM
Impact Indices
Altmetric
PlumX
Metrics
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.