Instance-Specific Algorithm Configuration

Yuri Malitsky

ISBN 10: 3319381237 / ISBN 13: 9783319381237

Published by Springer, Birkhäuser Aug 2016, 2016

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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 beensuccessfullyapplied to a number of solvers competing in the SATand 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 144 pp. Englisch.

Seller Inventory # 9783319381237

Bibliographic Details

• Title: Instance-Specific Algorithm Configuration
• Publisher: Springer, Birkhäuser Aug 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3319381237
• ISBN 13: 9783319381237
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 9D millimeters 235 millimeters width by 155 millimeters height by 9 millimeters depth
• Weight: 230 grams

About this title

Synopsis

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.

About the Author

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.