Optimal control of a cancer chemotherapy problem with different toxic elimination processes
Refereed conference paper presented and published in conference proceedings


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AbstractIn this paper, we propose two new anticancer drug scheduling models with different toxicity clearances according to kinetics of enzyme-catalyzed chemical reactions. We also present a sophisticated automating drug scheduling approach based on evolutionary computation and computer modeling. To explore multiple efficient drug scheduling policies, we use a multimodal optimization algorithm - adaptive elitist-population based genetic algorithm (AEGA) to solve the models, and discuss the situation of multiple optimal solutions under different parameter settings. The simulation results obtained by the new models 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.
All Author(s) ListLiang Y, Leung KS, Mok TSK
Name of ConferenceIEEE Congress on Evolutionary Computation
Start Date of Conference16/07/2006
End Date of Conference21/07/2006
Place of ConferenceVancouver
Country/Region of ConferenceCanada
Detailed descriptionorganized by IEEE Congress,
Year2006
Month1
Day1
PublisherIEEE
Pages2460 - 2467
ISBN978-0-7803-9487-2
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

Last updated on 2020-24-09 at 01:55