Working towards the development of an evolvable cancer treatment simulator, the investigation of Differential Evolution was considered, motivated by the high efficiency of variations of this technique in real-valued problems. A basic DE algorithm, namely "DE/rand/1" was used to optimize the simulated design of a targeted drug delivery system for tumor treatment on PhysiCell simulator. The suggested approach proved to be more efficient than a standard genetic algorithm, which was not able to escape local minima after a predefined number of generations. The key attribute of DE that enables it to outperform standard EAs, is the fact that it keeps the diversity of the population high, throughout all the generations. This work will be incorporated with ongoing research in a more wide applicability platform that will design, develop and evaluate targeted drug delivery systems aiming cancer tumours.

中文翻译：

---

利用差异进化优化靶向癌症治疗

为了开发可进化的癌症治疗模拟器，考虑了差异进化的研究，其动机是该技术在实值问题中的高效变化。基本的DE算法，即“DE/rand/1”用于优化PhysiCell模拟器上用于肿瘤治疗的靶向给药系统的模拟设计。事实证明，所建议的方法比标准遗传算法更有效，标准遗传算法在预定义的代数后无法摆脱局部最小值。DE 使其能够超越标准 EA 的关键属性是它在所有世代中保持人口的高度多样性。这项工作将与正在进行的研究结合在一个更广泛的适用性平台中，该平台将设计，