  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.

Neural and Evolutionary Computing;Machine Learning