Instance-Specific Algorithm Configuration

2014
ISBN: 978-3-319-11229-9
Verlag: Springer International Publishing

Buch, Englisch, 134 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 3435 g

This book presents a modular and expandable technique in the rapidly emerging research area of automatic configuration and selection of the best algorithm for the instance at hand. The author presents the basic model behind ISAC and then details a number of modifications and practical applications. In particular, he addresses automated feature generation, offline algorithm configuration for portfolio generation, algorithm selection, adaptive solvers, online tuning, and parallelization. 

The author's related thesis was honorably mentioned (runner-up) for the ACP Dissertation Award in 2014, and this book includes some expanded sections and notes on recent developments. Additionally, the techniques described in this book have been successfully applied to a number of solvers competing in the SAT and MaxSAT International Competitions, winning a total of 18 gold medals between 2011 and 2014. 

The book will be of interest to researchers and practitioners in artificial intelligence, in particular in the area of machine learning and constraint programming.

Zielgruppe

Research

Autoren/Hrsg.

Malitsky, Yuri

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Introduction.- Survey of Related Work.- Architecture of Instance-Specific Algorithm Configuration Approach.- Applying ISAC to Portfolio Selection.- Generating a Portfolio of Diverse Solvers.- Handling Features.- Developing Adaptive Solvers.- Making Decisions Online.- Conclusions.

Über Autor(innen)

Dr. Yuri Malitsky received his PhD from Brown University in 2012 for his work on the Instance-Specific Algorithm Configuration (ISAC) approach. He was a postdoc in the Cork Constraint Computation Centre from 2012 to 2014. He is now a postdoc at the IBM Thomas J. Watson Research Center, working on problems in machine learning, combinatorial optimization, data mining, and data analytics. 

Dr. Malitsky's research focuses on applying machine learning techniques to improve the performance of combinatorial optimization and constraint satisfaction solvers. In particular, his work centers around automated algorithm configuration, algorithm portfolios, algorithm scheduling, and adaptive search strategies, aiming to develop the mechanisms to determine the structures of problems and their association with the behaviors of different solvers, and to develop methodologies that automatically adapt existing tools to the instances they will be evaluated on.