论文标题
MME：基于混合模型的大规模优化的演化策略
MMES: Mixture Model based Evolution Strategy for Large-Scale Optimization
论文作者
论文摘要
这项工作为大规模设置中的协方差矩阵适应演化策略（CMA-ES）提供了有效的采样方法。在与CMA-ES中高斯采样的合同中，提出的方法从混合模型中产生突变向量，这有助于利用有限时间预算内的问题景观的丰富可变相关性。我们分析了该混合模型的概率分布，并表明它具有可控精度近似CMA-E的高斯分布。我们使用这种采样方法，再加上一种新的突变强度适应方法来制定基于混合模型的进化策略（MMES） - 一种用于大规模优化的CMA-ES变体。数值模拟表明，尽管MME可显着降低CMA-ES的时间复杂性，但MMES保留了旋转不变性，可扩展到高维问题，并且在执行全局优化方面具有与最先进的竞争力。
This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to the Gaussian sampling in CMA-ES, the proposed method generates mutation vectors from a mixture model, which facilitates exploiting the rich variable correlations of the problem landscape within a limited time budget. We analyze the probability distribution of this mixture model and show that it approximates the Gaussian distribution of CMA-ES with a controllable accuracy. We use this sampling method, coupled with a novel method for mutation strength adaptation, to formulate the mixture model based evolution strategy (MMES) -- a CMA-ES variant for large-scale optimization. The numerical simulations show that, while significantly reducing the time complexity of CMA-ES, MMES preserves the rotational invariance, is scalable to high dimensional problems, and is competitive against the state-of-the-arts in performing global optimization.
