Deep learning for algorithm portfolios

Abstract

Automated feature construction for algorithm portfolios in machine learning is provided. A gray scale image is generated from a text representing a problem instance. The gray scale image is rescaled or reshaped to a predefined size that is smaller than an initial size of the gray scale image. The rescaled gray scale image represents features of the problem instance. The rescaled gray scale image is input as features to a machine learning-based convolutional neural network. Based on the rescaled gray scale image, the machine learning-based convolutional neural network is automatically trained to learn to automatically determine one or more problem solvers from a portfolio of problem solvers suited for solving the problem instance.