Instance-Specific Algorithm Configuration

Nonfiction, Science & Nature, Mathematics, Applied, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Instance-Specific Algorithm Configuration by Yuri Malitsky, Springer International Publishing
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Author: Yuri Malitsky ISBN: 9783319112305
Publisher: Springer International Publishing Publication: November 20, 2014
Imprint: Springer Language: English
Author: Yuri Malitsky
ISBN: 9783319112305
Publisher: Springer International Publishing
Publication: November 20, 2014
Imprint: Springer
Language: English

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.

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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.

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