Differential Evolution (DE) has become one of the leading metaheuristics in the class of Evolutionary Algorithms, which consists of methods that operate off of survival-of-the-fittest principles. This general purpose optimization algorithm is viewed as an improvement over Genetic Algorithms, which are widely used to find solutions to chemometric problems. Using straightforward vector operations and random draws, DE can provide fast, efficient optimization of any real, vector-valued function. This article reviews the basic algorithm and a few of its modifications with various enhancements. We provide guidance for practitioners, discuss implementation issues and give illustrative applications of DE with the corresponding R codes to find different types of optimal designs for various statistical models in chemometrics that involve the Arrhenius equation, reaction rates, concentration measures and chemical mixtures.

中文翻译：

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使用差分进化设计最佳实验

差分进化 (DE) 已成为进化算法类中领先的元启发式算法之一，该类算法由基于适者生存原则运行的方法组成。这种通用优化算法被视为对遗传算法的改进，遗传算法被广泛用于寻找化学计量问题的解决方案。使用简单的向量运算和随机绘制，DE 可以对任何实向量值函数提供快速、有效的优化。本文回顾了基本算法及其一些具有各种增强功能的修改。我们为从业者提供指导，