1. A computer-implemented method of automated feature construction for algorithm portfolios in machine learning, comprising: receiving, by one or more processors, a problem instance represented as text describing a problem to be solved by computer-implemented problem solver; generating, by one or more of the processors, a gray scale image from the text by converting the text into the gray scale image that corresponds to the text; rescaling, by one or more of the processors, the gray scale image to a predefined size that is smaller than an initial size of the gray scale image, into a rescaled gray scale image, the rescaled gray scale image representing features of the problem instance; inputting, by one or more of the processors, the rescaled gray scale image as features to a machine learning-based convolutional neural network; and training based on the rescaled gray scale image, by one or more of the processors, the machine learning-based convolutional neural network to learn to automatically determine one or more problem solvers from a portfolio of problem solvers suited for solving the problem instance, wherein the receiving, generating, rescaling and inputting are performed for a plurality of problem instances for the training.
1. A computer-implemented method of automated feature construction for algorithm portfolios in machine learning, comprising: receiving, by one or more processors, a problem instance represented as text describing a problem to be solved by computer-implemented problem solver; generating, by one or more of the processors, a gray scale image from the text by converting the text into the gray scale image that corresponds to the text; rescaling, by one or more of the processors, the gray scale image to a predefined size that is smaller than an initial size of the gray scale image, into a rescaled gray scale image, the rescaled gray scale image representing features of the problem instance; inputting, by one or more of the processors, the rescaled gray scale image as features to a machine learning-based convolutional neural network; and training based on the rescaled gray scale image, by one or more of the processors, the machine learning-based convolutional neural network to learn to automatically determine one or more problem solvers from a portfolio of problem solvers suited for solving the problem instance, wherein the receiving, generating, rescaling and inputting are performed for a plurality of problem instances for the training. 2. The method of claim 1 , further comprising: executing, by one or more of the processors, the machine learning-based convolutional neural network that is trained, to select the problem solver suited to solving a new problem instance.