🤖 AI Summary
Differential Evolution (DE) performance is highly sensitive to hyperparameter settings and mutation strategy selection, yet conventional tuning methods suffer from poor generalizability. To address this, we propose a self-referential optimization framework wherein DE dynamically evolves its own core parameters and mutation strategies at the meta-level, unifying the solver and configurator within a single adaptive mechanism. Our contributions include: (i) introducing the first self-evolving DE paradigm; (ii) designing a strategy-parameter co-adaptive dynamic adjustment mechanism; and (iii) integrating meta-level DE, GPU-accelerated parallelization, and evolutionary reinforcement learning. Empirical evaluation on the CEC2022 benchmark suite demonstrates statistically significant superiority over state-of-the-art DE variants. Furthermore, we validate strong generalizability and practical utility in real-world robotic control tasks, confirming robust cross-domain adaptability.
📝 Abstract
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.