Abstract

Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems in many areas of computer science and operations research, including propositional satisfiability, constraint satisfaction, routing, and scheduling. SLS algorithms have also become increasingly popular for solving challenging combinatorial problems in many application areas, such as e-commerce and bioinformatics.
Hoos and Stützle offer the first systematic and unified treatment of SLS algorithms. In this groundbreaking new book, 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 methods and explores their underlying principles, properties, and features. This book gives hands-on experience with some of the most widely used search techniques, and provides readers with the necessary understanding and skills to use this powerful tool.

• Provides the first unified view of the field
• Offers an extensive review of state-of-the-art stochastic local search algorithms and their applications
• Presents and applies an advanced empirical methodology for analyzing the behavior of SLS algorithms
• A companion website offers lecture slides as well as source code and Java applets for exploring and demonstrating SLS algorithms

“The book and companion web page are well written, easy to read and suited for students, scholars, researchers and practitioners alike.”
— Ruben Ruiz and Marco Pranzo, European Journal of Operational Research Article in Press
“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, Université Libre de Bruxelles
“Hoos and Stützle’s treatment of the topic is comprehensive and covers a variety of techniques, 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, Université de Montréal
“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.”
— Olivier Martin, Université 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. An excellent overview of the wide range of applications of stochastic local search methods is included.”
— Bart Selman, Cornell University
“... 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
“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. It will serve as an entry point for this field for quite some time.”
— Stefan Voss, University of Hamburg, Hamburg, Germany
“This book is the first in unifying the dispersed field of Stochastic Local Search (SLS) algorithms. It provides an excellent empirical scientific methodology geared towards the successful application of SLS algorithms in practice.”
— European Journal of Operational Research