论文标题
卷积神经网络选择黑匣子算法
Black Box Algorithm Selection by Convolutional Neural Network
论文作者
论文摘要
尽管已经提出了针对黑匣子优化问题的大量优化算法，但无免费的午餐定理告诉我们，没有算法可以在所有类型的问题上击败其他算法。不同类型的优化问题需要不同的优化算法。为了解决这个问题，研究人员提出算法选择，建议从设定的给定未知优化问题的算法中提出最佳优化算法。通常，算法选择被视为分类或回归任务。深度学习已被证明在各种分类和回归任务上都表现良好，它应用于本文的算法选择问题。我们的深度学习体系结构基于卷积神经网络，并遵循视觉几何组的主要体系结构。该体系结构已应用于许多不同类型的二维数据。此外，我们还提出了一种新颖的方法，以从优化问题中提取景观信息并将信息保存为2-D图像。在实验部分中，我们进行了三个实验，以研究我们方法对BBOB函数的分类和优化能力。结果表明，我们的新方法可以有效解决算法选择问题。
Although a large number of optimization algorithms have been proposed for black box optimization problems, the no free lunch theorems inform us that no algorithm can beat others on all types of problems. Different types of optimization problems need different optimization algorithms. To deal with this issue, researchers propose algorithm selection to suggest the best optimization algorithm from the algorithm set for a given unknown optimization problem. Usually, algorithm selection is treated as a classification or regression task. Deep learning, which has been shown to perform well on various classification and regression tasks, is applied to the algorithm selection problem in this paper. Our deep learning architecture is based on convolutional neural network and follows the main architecture of visual geometry group. This architecture has been applied to many different types of 2-D data. Moreover, we also propose a novel method to extract landscape information from the optimization problems and save the information as 2-D images. In the experimental section, we conduct three experiments to investigate the classification and optimization capability of our approach on the BBOB functions. The results indicate that our new approach can effectively solve the algorithm selection problem.
