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

Show full item record

SCOPUSTM   
Citations

1
checked on Nov 3, 2024

Page view(s)

40
Last Week
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.