黑箱随机优化问题的分布式演化策略
Distributed Evolution Strategies for Black-box Stochastic Optimization
摘要：
这项工作涉及分布式随机黑箱优化的进化方法，其中每个工人可以单独解决问题的近似与自然启发的算法。我们提出了一个分布式进化策略（DES）算法的基础上适当的修改演化策略，一个家庭的经典进化算法，以及与现有的分布式框架仔细结合。在光滑和非凸的景观，DES有一个收敛速度竞争现有的零阶方法，并可以利用稀疏性，如果适用的话，以匹配一阶方法的速度。DES方法使用高斯概率模型来指导搜索，避免了现有零阶方法中有限差分技术带来的数值问题。DES方法也完全适应问题的景观，因为它的收敛性是保证与任何参数设置。我们进一步提出了两种替代的采样方案，显着提高采样效率，同时导致类似的性能。对几个机器学习问题的仿真研究表明，所提出的方法在减少收敛时间和提高对参数设置的鲁棒性方面表现出很大的希望。
英文摘要：
This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms. We propose a distributed evolution strategy (DES) algorithm grounded on a proper modification to evolution strategies, a family of classic evolutionary algorithms, as well as a careful combination with existing distributed frameworks. On smooth and nonconvex landscapes, DES has a convergence rate competitive to existing zeroth-order methods, and can exploit the sparsity, if applicable, to match the rate of first-order methods. The DES method uses a Gaussian probability model to guide the search and avoids the numerical issue resulted from finite-difference techniques in existing zeroth-order methods. The DES method is also fully adaptive to the problem landscape, as its convergence is guaranteed with any parameter setting. We further propose two alternative sampling schemes which significantly improve the sampling efficiency while leading to similar performance. Simulation studies on several machine learning problems suggest that the proposed methods show much promise in reducing the convergence time and improving the robustness to parameter settings.
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