Automated Algorithm Selection and Configuration

This report documents the programme and the outcomes of Dagstuhl Seminar 16412 “Automated Algorithm Selection and Configuration”, which was held October 9–14, 2016 and attended by 34 experts from 10 countries. Research on automated algorithm selection and configuration has lead to some of the most impressive successes within the broader area of empirical algorithmics, and has proven to be highly relevant to industrial applications. Specifically, high-performance algorithms for NP-hard problems, such as propositional satisfiability (SAT) and mixed integer programming (MIP), are known to have a huge impact on sectors such as manufacturing, logistics, healthcare, finance, agriculture and energy systems, and algorithm selection and configuration techniques have been demonstrated to achieve substantial improvements in the performance of solvers for these problems. Apart from creating synergy through close interaction between the world’s leading groups in the area, the seminar pursued two major goals: to promote and develop deeper understanding of the behaviour of algorithm selection and configuration techniques and to lay the groundwork for further improving their efficacy. Towards these ends, the organisation team brought together a group of carefully chosen researchers with strong expertise in computer science, statistics, mathematics, economics and engineering; a particular emphasis was placed on bringing together theorists, empiricists and experts from various application areas, with the goal of closing the gap between theory and practice. Seminar October 9–14, 2016 – http://www.dagstuhl.de/16412 1998 ACM Subject Classification I.2 Artificial Intelligence, I.2.2 Automatic Programming, I.2.6 Learning, I.2.8 Problem Solving, Control Methods, and Search, G.1.6 Optimization