Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412)

Authors Holger H. Hoos, Frank Neumann, Heike Trautmann and all authors of the abstracts in this report



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Holger H. Hoos
Frank Neumann
Heike Trautmann
and all authors of the abstracts in this report

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Holger H. Hoos, Frank Neumann, and Heike Trautmann. Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412). In Dagstuhl Reports, Volume 6, Issue 10, pp. 33-74, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/DagRep.6.10.33

Abstract

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 cnp-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.
Keywords
  • algorithm configuration
  • algorithm selection
  • features
  • machine learning
  • optimisation
  • performance prediction

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