Evolutionary drug scheduling model for cancer chemotherapy

This paper presents 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 firstly modify the existing model, because the existing equation of the cumulative drug toxicity is not consistent with the clinical experience and the medicine knowledge. For exploring multiple efficient drug scheduling policies, we propose the novel variable representation the cycle-wise representation; and adjust the elitist genetic search operators in the AEGA. The results obtained by the new model match well with the clinical treatment experience, and can provide much more realistic solutions than that by the previous model. Moreover, it has been shown that the evolutionary drug scheduling approach is simple and capable of solving complex cancer chemotherapy problems by adapting the suitable coding and the multimodal versions of EAs.