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This book presents a modular and expandable technique in the rapidly emerging research area of automatically configuring or selecting 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 book will be of interest to researchers and practitioners in artificial intelligence, in particular in the area of machine learning and constraint programming.§