Proceedings of the 8th 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
Proceedings of the 2009 International Conference on Hybrid Information Technology
Modeling and optimization of combined cytostatic and cytotoxic cancer chemotherapy
Artificial Intelligence in Medicine
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Bézier control parameterization for evolutionary optimization in disease models
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In 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