Tse, Sui-ManSui-ManTseLiang, YongYongLiangProf. LEUNG Kwong SakLee, Kin-HongKin-HongLeeMok, Tony Shu-KamTony Shu-KamMok2023-03-242023-03-242007IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2007, vol. 37 (1) , pp. 84 - 9110834419http://hdl.handle.net/20.500.11861/7570This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The objective is to minimize the tumor size under a set of constraints. We combine the adaptive elitist genetic algorithm with a local search algorithm called iterative dynamic programming (IDP) to form a new memetic algorithm (MA-IDP) for solving the problem. MA-IDP has been shown to be very efficient in solving the multidrug scheduling optimization problem. © 2007 IEEE.enOptimization AlgorithmOptimal ScheduleHybrid Genetic AlgorithmChemotherapy SchedulesDrug AdministrationOptimal ControlSearch AlgorithmOptimal Control ProblemDrug ResistanceDays of TreatmentDrug ConcentrationVector ControlCourse of TreatmentDrug DoseSet of EquationsChemotherapeutic DrugsGrid PointsGlobal OptimizationDrug ToxicityTreatment CyclesCumulative ToxicityDrug ScheduleMultiple SolutionsGenetic OperatorsTreatment PatternsOptimal Control PolicyCrossover OperatorCycle LengthPrevious IterationTreatment SchedulePeer Reviewed Journal Article10.1109/TSMCB.2006.883265