The algorithm selection problem is to select the best algorithm for solving a given problem. It is relevant where algorithm portfolios are employed and instead of tackling a set of problem instances with just a single algorithm, a set of them is used with a subset, which may be of size 1, being selected for each instance.A common approach to algorithm selection in practice is to characterise the problem instance to be solved through sets of features that can be extracted in a computationally efficient manner. These features, along with ground truth data of algorithm performance on some problem instances, are then used to induce performance models of the portfolio and its constituent algorithms. Usually, machine learning is used to induce such models. To solve a given new problem instance, the learned model makes a prediction as to the most suitable algorithm(s).