Liang, YongYongLiangProf. LEUNG Kwong SakMok, Tony Shu KamTony Shu KamMok2023-03-242023-03-2420062006 IEEE Congress on Evolutionary Computation, CEC 2006, 2006, pp. 2475 - 2482, Article number 16886160780394879978-078039487-2http://hdl.handle.net/20.500.11861/7575In 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. © 2006 IEEE.enConference Proceedings