Optimal Control of a Cancer Chemotherapy Problem with Different Toxic Elimination Processes

In 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.