Automated algorithm selection and configuration are key enabling approaches for improving the state of the art in solving a broad range of important problems, by exploiting performance complementarity between multiple algorithms for the same problem (selection) and by realising the latent performance potential in parameterised algorithms (configuration). Compared to traditional, manual approaches, these techniques are not only more efficient and rely less on human expertise, but also provide a more principled basis for algorithm selection and configuration, enable fairer comparisons between algorithms, and facilitate new insights into which algorithmic techniques and components work best and under which circumstances.

Work on automated algorithm selection, configuration, and related areas spans multiple, weakly connected communities, including artificial intelligence, evolutionary computation, mathematical optimisation and operations research. This special issue follows a Dagstuhl seminar on the same topic, held in October 2016, and is intended as a further step toward creating synergy between those communities.

For this special issue, we selected, from a substantial number of submissions, seven papers that jointly cover a broad range of topics and approaches, including various methods for algorithm selection and configuration, search landscape analysis, software engineering aspects, as well as applications to prominent discrete and continuous, single- and multiobjective optimisation problems.

The survey paper by Kerschke et al. provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Unlike earlier surveys, it covers applications to discrete and continuous problems; it also situates algorithm selection in the context of a wide spectrum of conceptually related approaches, such as algorithm configuration, scheduling, and portfolio selection, and discusses open challenges.

Alyaha and Rowe present a study of landscape characteristics for three NP-hard combinatorial optimisation problems: number partitioning and two variants of knapsack problems. A comparative analysis of landscapes induced by different neighbourhood operators, penalty functions, and problem parameters led to improved problem understanding and to a heuristic for selecting the most appropriate local search operator.

The paper by Saalem et al. introduces an approach for assessing the effectiveness of exploratory landscape features with respect to their impact on the performance of algorithm selection methods. A model-based framework for continuous black-box problem comparison using Gaussian process (GP) regression is presented, leading to a flexible surrogate model for problem landscapes. The GP substantially facilitates problem comparison while efficiently measuring model quality.

Kerschke et al. present an automated algorithm selection method for single-objective continuous black-box optimisation problems, based on sophisticated exploratory landscape analysis and machine learning techniques. The efficiency of the selector is illustrated on the Black-Box Optimisation Benchmark (BBOB), by improving the performance over the single best solver from a representative set of solvers by a factor of two on average.

In the paper by Wessing et al., an improved initialisation procedure for a well-known automated configuration procedure, irace, is shown to outperform classical uniform sampling of algorithm configurations. Techniques from the design and analysis of computer experiments are applied, that is, several Latin hypercube sampling methods that are able to handle categorical and numerical parameters that may be conditional (nested) on the value of other (branching) parameters.

Blot et al. investigate the automatic configuration of multiobjective local search algorithms for permutation problems—specifically, for the bi-objective permutation flowshop and travelling salesman problems. They consider two performance metrics as configuration objectives and study several approaches for automated configuration according to these two objectives, presenting clear evidence that multiobjective algorithms are best configured using a multiobjective configurator.

Finally, Swan et al. introduce a new approach to the automation of the design of metaheuristics, the so-called Automated Open-Closed Principle (AOCP), which addresses the problem of state dependencies between configurable components in flexible algorithm frameworks. AOCP extends current approaches to automated algorithm configuration by offering a principled software engineering approach to ensure the automated assembly of algorithms from an extensible palette of components.

By now, automated algorithm selection and configuration are mature research areas, as witnessed not only by the sophistication of the methods and the success of their many applications, but also by a large and fast-growing body of literature. Still, we see much room for further work, spanning the gamut from theoretical to empirical studies, from new methodology to applications. We hope that this special issue will inspire interest and future work in these dynamic and exciting research areas.