Liang Yong, Leung Kwong-Sak, Mok Tony Shu Kam
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR.
IEEE Trans Inf Technol Biomed. 2006 Apr;10(2):237-45. doi: 10.1109/titb.2005.859888.
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.
在本文中，我们介绍了一种用于癌症化疗中药物调度的改进型最优控制模型以及一种新的基于自适应精英种群的遗传算法（AEGA）来求解该模型。我们与一位肿瘤学家密切合作，首先对现有模型进行了修改，因为其累积药物毒性方程与医学知识和临床经验不一致。为了探索多种有效的药物调度策略，我们提出了一种新颖的变量表示——逐周期表示，并对AEGA中的精英遗传搜索算子进行了修改。改进模型得到的模拟结果与临床治疗经验吻合良好，可为肿瘤学家提供多种有效的解决方案供其参考。此外，研究表明，进化药物调度方法简单，并且能够通过采用进化算法的多模态版本来解决复杂的癌症化疗问题。
