MetaDE: Evolving Differential Evolution by Differential Evolution

TL;DR

MetaDE is a novel method that uses differential evolution itself to automatically optimize its hyperparameters and strategies, enhancing performance and efficiency in black-box optimization and robot control tasks.

Contribution

MetaDE introduces a meta-level evolution approach that dynamically adjusts DE hyperparameters and strategies using DE, combined with GPU acceleration for efficiency.

Findings

MetaDE outperforms traditional DE on CEC2022 benchmarks.

MetaDE shows promising results in robot control via evolutionary reinforcement learning.

GPU-accelerated implementation improves computational efficiency.

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.