1 School of Biomedical Engineering, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China.
2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
SLAS Technol. 2017 Jun;22(3):289-305. doi: 10.1177/2472630317690318. Epub 2017 Jan 31.
Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.
差分进化（DE）在过去十年中被广泛应用于药物组合优化研究。它允许以最小的实验工作量来确定所需的药物组合。本文提出了一种用于 DE 算法的自适应种群大小方法。与原始 DE 算法和基于常数逐步种群减少的 DE 算法相比，我们的新方法在效率和收敛性方面都有所改进，这将减少识别最佳药物组合所需的细胞和动物数量。该方法根据优化过程的阶段不断调整种群大小的减少。我们的自适应方案限制了种群减少仅在开发阶段发生。我们认为，在进化过程中不断调整更有效的种群大小是 DE 算法收敛速度显著提高的主要原因。该方法的性能通过一组单峰和多峰基准函数进行评估。与自适应变异和交叉常数方案相结合，这种自适应种群减少方法可以为完全无参数调整的自适应 DE 算法的未来方向提供一些启示。
