Liang, YongYongLiangProf. LEUNG Kwong SakMok, Tony Shu KamTony Shu KamMok2023-03-242023-03-242006IEEE Transactions on Information Technology in Biomedicine, 2006, vol. 10 ( 2), pp. 237 - 24510897771http://hdl.handle.net/20.500.11861/7582In this paper, we introduce a modified optimal control model of drug scheduling in cancer chemotherapy and a new adaptive elitist-population-based genetic algorithm (AEGA) to solve it. Working closely with an oncologist, we first modify the existing model, because its equation for the cumulative drug toxicity is inconsistent with medical knowledge and clinical experience. To explore multiple efficient drug scheduling policies, we propose a novel variable representation - a cycle-wise representation, and modify the elitist genetic search operators in the AEGA. The simulation results obtained by the modified model match well with the clinical treatment experiences, and can provide multiple efficient solutions for oncologists to consider. Moreover, it has been shown that the evolutionary drug scheduling approach is simple, and capable of solving complex cancer chemotherapy problems by adapting multimodal versions of evolutionary algorithms. © 2006 IEEE.enDrug ScheduleSimulation ResultsClinical TreatmentClinical ExperienceOptimal ControlEvolutionary AlgorithmsDrug ToxicityMultiple DrugsMedical KnowledgeMultiple SolutionsRepresentative VariablesEfficient PoliciesMultiple PolicyGenetic OperatorsEfficient SchedulingCumulative ToxicityEffects of DrugsOptimization ProblemTumor SizePerformance IndicatorsNumber of Tumor CellsCrossover OperatorDrug DoseOptimal ScheduleFront PartMultimodal ProblemsDrug ConcentrationPerformance Index ValuesStochastic AlgorithmAdaptive SearchPeer Reviewed Journal Article10.1109/TITB.2005.859888