Stochastic Local Search: Foundations and Applications (Morgan Kaufmann Series in Artificial Intelligence) [Gebundene Ausgabe]

"The book and companion web page are well written, easy to read and suited for students, scholars, researchers and practitioners alike. We see SLS:FA as an important step towards an improved understanding of SLS methods and their behavior, and we expect that this book will inspire future researchers and spark new lines of work in this area." - Ruben Ruiz and Marco Pranzo, European Journal of Operational Research Article in Press "Hoos and Stutzle, two major players in the field, provide us with an excellent overview of stochastic local search. If you are looking for a book that covers all the major metaheuristics, gives you insight into their working, and guides you in their application to a wide set of combinatorial optimization problems, this is the book. " - Marco Dorigo, Universite Libre de Bruxelles "Stochastic Local Search: Foundations and Applications provides an original and synthetic presentation of a large class of algorithms more commonly known as metaheuristics. Over the last 20 years, these methods have become extremely popular, often representing the only practical approach for tackling so many of the hard combinatorial problems that are encountered in real-life applications. Hoos and Stutzle's treatment of the topic is comprehensive and covers a variety of techniques, including simulated annealing, tabu search, genetic algorithms and ant colony optimization, but a main feature of the book is its proposal of a most welcome unifying framework for describing and analyzing the various methods." - Michel Gendreau, Universite de Montreal "Local search algorithms are often the most practical approach to solving constraint satisfaction and optimization problems that admit no fast deterministic solution. This book is full of information and insights that would be invaluable for both researchers and practitioners." - Henry Kautz, University of Washington "This extensive book provides an authoritative and detailed exposition for novices and experts alike who need to tackle difficult decision or combinatorial optimization problems. The chapters span fundamental theoretical questions such as, "When and why do heuristics work well?" but also more applied aspects involving, for instance, the comparison of very different algorithms. The authors are university faculty members and leading players in their research fields; our communities will enjoy in particular their books' valuable teaching material and a "complete" bibliography of the state of the art for the field." - Olivier Martin, Universite Paris-Sud, Orsay "The authors provide a lucid and comprehensive introduction to the large body of work on stochastic local search methods for solving combinatorial problems. The text also covers a series of carefully executed empirical studies that provide significant further insights into the performance of such methods and show the value of an empirical scientific methodology in the study of algorithms. An excellent overview of the wide range of applications of stochastic local search methods is included." - Bart Selman, Cornell University "Stochastic local search is a powerful search technique for solving a wide range of combinatorial problems. If you only want to read one book on this important topic, you should read Hoos and Stutzle's. It is a comprehensive and informative survey of the field that will equip you with the tools and understanding to use stochastic local search to solve the problems you come across." - Toby Walsh, Cork Constraint Computation Centre, University College Cork "This book provides remarkable coverage and synthesis of the recent explosion of work on randomized local search algorithms. It will serve as a good textbook for classes on heuristic search and metaheuristics as well as a central reference for researchers. The book provides a unification of a broad spectrum of methods that enables concise, highly readable descriptions of theoretical and experimental results." - David L. Woodruff, University of California, Davis "One of the nice features of this book is that it puts some considered metaheuristics into broader perspective. The book provides a comprehensive treatment of stochastic local search. It will serve as an entry point for this field for quite some time." - Stefan Voss, University of Hamburg, Hamburg, Germany "The book establishes a landmark in the broad field of this type of algorithms that are also known as metaheuristics. This book is the first in unifying the dispersed field of Stochastic Local Search (SLS) algorithms. Written in a clear and easy-to-read style, the book tries to cover all possible audiences, from graduate students or doctoral students to practitioners and researchers. The book more than accomplishes its main goal of covering both the foundations and the many applications of SLS algorithms. It provides an excellent empirical scientific methodology geared towards the successful application of SLS algorithms in practice. Furthermore, it presents advances in the explanation of complex SLS behavior, something which is much needed and refreshingly new in the field. The book and companion web page are well written, easy to read and suited for students, scholars, researchers and practitioners alike." - European Journal of Operational Research

Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally hard problems in many fields of Computer Science and Operations Research. They are becoming increasingly popular for solving problems in domains including propositional satisfiability, constraint satisfaction, planning, scheduling, bioinformatics, and combinatorial auctions.

Hoos and Stützle offer the first systematic and unified treatment of SLS algorithms. They examine the general concepts and specific instances of SLS algorithms and carefully consider their development, analysis and application. The discussion focuses on the most successful SLS techniques in order to facilitate an understanding of the relationships between existing techniques, to examine the empirical evaluation of SLS algorithms, and to give hands-on experience with some of the most widely used search techniques.