As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization problems. While the advantages of DE are well-recognized, achieving peak performance heavily depends on its hyperparameters such as the mutation factor, crossover probability, and the selection of specific DE strategies. Traditional approaches to this hyperparameter dilemma have leaned towards parameter tuning or adaptive mechanisms. However, identifying the optimal settings tailored for specific problems remains a persistent challenge. In response, we introduce MetaDE, an approach that evolves DE's intrinsic hyperparameters and strategies using DE itself at a meta-level. A pivotal aspect of MetaDE is a specialized parameterization technique, which endows it with the capability to dynamically modify DE's parameters and strategies throughout the evolutionary process. To augment computational efficiency, MetaDE incorporates a design that leverages parallel processing through a GPU-accelerated computing framework. Within such a framework, DE is not just a solver but also an optimizer for its own configurations, thus streamlining the process of hyperparameter optimization and problem-solving into a cohesive and automated workflow. Extensive evaluations on the CEC2022 benchmark suite demonstrate MetaDE's promising performance. Moreover, when applied to robot control via evolutionary reinforcement learning, MetaDE also demonstrates promising performance. The source code of MetaDE is publicly accessible at: https://github.com/EMI-Group/metade.

翻译：作为进化计算领域的基石，差分进化算法以其简洁性和处理复杂黑盒优化问题的有效性而闻名。尽管差分进化的优势已得到广泛认可，但其峰值性能的实现高度依赖于超参数设置，如变异因子、交叉概率以及特定差分进化策略的选择。针对这一超参数困境的传统方法主要侧重于参数调优或自适应机制。然而，针对特定问题确定最优参数配置仍是持续存在的挑战。为此，我们提出MetaDE方法，通过在元层级利用差分进化自身来演化其内在超参数与策略。MetaDE的核心在于一种专用参数化技术，使其能够在进化过程中动态调整差分进化的参数与策略。为提升计算效率，MetaDE采用基于GPU加速计算框架的并行处理设计。在此框架下，差分进化不仅作为求解器，更成为其自身配置的优化器，从而将超参数优化与问题求解过程整合为统一且自动化的流程。基于CEC2022基准测试集的广泛评估表明MetaDE具有优异的性能表现。此外，在通过进化强化学习实现机器人控制的应用中，MetaDE同样展现出显著潜力。MetaDE的源代码已公开于：https://github.com/EMI-Group/metade。