Optimal control drug scheduling of cancer chemotherapy
Automatica (Journal of IFAC)
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Genetic subgradient method for solving location-allocation problems
Applied Soft Computing
Genetic algorithms in classifier fusion
Applied Soft Computing
Adaptive elitist-population based genetic algorithm for multimodal function optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Heuristic design of cancer chemotherapies
IEEE Transactions on Evolutionary Computation
A novel evolutionary drug scheduling model in cancer chemotherapy
IEEE Transactions on Information Technology in Biomedicine
A Memetic Algorithm for Multiple-Drug Cancer Chemotherapy Schedule Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automating the drug scheduling of cancer chemotherapy via evolutionary computation
Artificial Intelligence in Medicine
A mathematical model of cycle-specific chemotherapy
Mathematical and Computer Modelling: An International Journal
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Through incorporating into Martin's drug scheduling model a toxicity metabolism term, our modified model takes into account the body's ability of recovering from the effect of the drug and successively overcomes two unreasonable problems in Martin's model. Since different drugs have different toxicity metabolism processes, we propose two renewed drug scheduling models with different toxicity metabolism according to kinetics of enzyme-catalyzed chemical reactions. For exploring multiple efficient drug scheduling policies, we use our adaptive elitist-population based genetic algorithm (AEGA) to solve the renewed models, and discuss the situation of multiple optimal solutions under different parameter settings. The simulation results obtained by the renewed models match well with the clinical treatment experience, and can provide much more drug scheduling polices for the doctor to choose depending on the particular conditions of the patients.