论文标题
模块化CMA-ES变体的景观感知固定预算性能回归和算法选择
Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
论文作者
论文摘要
自动化算法选择有望支持用户为给定问题选择最合适的算法的决定性任务。这些机器训练技术的一个共同组成部分是回归模型，这些模型可以预测给定算法在以前看不见的问题实例上的性能。在数值黑框优化的背景下，这种回归模型通常建立在探索性景观分析（ELA）的基础上，该模型量化了问题的几个特征。这些措施可用于训练监督性能回归模型。 在固定目标设置的背景下，已经迈出了基于ELA的性能回归的第一步。但是，在许多应用程序中，用户需要选择一种在给定的功能评估预算内执行最佳的算法。通过这种固定的预算设置，我们证明，通过合适的两个不同训练有素的回归模型，可以通过现成的监督学习方法来实现高质量的绩效预测。我们在一个非常具有挑战性的问题上测试了这种方法：对非常相似算法的投资组合中的算法选择，我们从模块化CMA-ES算法的家族中进行选择。
Automated algorithm selection promises to support the user in the decisive task of selecting a most suitable algorithm for a given problem. A common component of these machine-trained techniques are regression models which predict the performance of a given algorithm on a previously unseen problem instance. In the context of numerical black-box optimization, such regression models typically build on exploratory landscape analysis (ELA), which quantifies several characteristics of the problem. These measures can be used to train a supervised performance regression model. First steps towards ELA-based performance regression have been made in the context of a fixed-target setting. In many applications, however, the user needs to select an algorithm that performs best within a given budget of function evaluations. Adopting this fixed-budget setting, we demonstrate that it is possible to achieve high-quality performance predictions with off-the-shelf supervised learning approaches, by suitably combining two differently trained regression models. We test this approach on a very challenging problem: algorithm selection on a portfolio of very similar algorithms, which we choose from the family of modular CMA-ES algorithms.
