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
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
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
An application of a multivariate estimation of distribution algorithm to cancer chemotherapy
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Fast Drug Scheduling Optimization Approach for Cancer Chemotherapy
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
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The toxicity of an anticancer drug is cleared from the body by different processes, including saturable metabolic and nonsaturable renal-excretion pathways. According to the principles of toxicokinetics, we propose a new anticancer drug scheduling model with different toxic elimination processes in this paper. 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 new model, and discuss the situation of multiple optimal solutions under different parameter settings. The simulation results obtained by the new model 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.