论文标题
Hessian估计进化策略
The Hessian Estimation Evolution Strategy
论文作者
论文摘要
我们提出了一种新型的黑匣子优化算法，称为Hessian估计进化策略。该算法通过直接估计目标函数的曲率来更新其采样分布的协方差矩阵。该算法设计的目标是两次连续可区分的问题。为此，我们将CMA-ES的累积步进适应算法扩展到镜像采样。我们证明，通过对BBOB/可可测试台进行评估，我们的协方差矩阵适应方法是有效的。我们还表明，当算法违反了两次可差异的目标函数时，该算法的核心假设令人惊讶。该方法具有具有竞争性能的新演化策略，同时它还为通常的协方差矩阵更新机制提供了有趣的替代方法。
We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy. The algorithm updates the covariance matrix of its sampling distribution by directly estimating the curvature of the objective function. This algorithm design is targeted at twice continuously differentiable problems. For this, we extend the cumulative step-size adaptation algorithm of the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance matrix adaptation is efficient by evaluation it on the BBOB/COCO testbed. We also show that the algorithm is surprisingly robust when its core assumption of a twice continuously differentiable objective function is violated. The approach yields a new evolution strategy with competitive performance, and at the same time it also offers an interesting alternative to the usual covariance matrix update mechanism.
