Stochastic local search (SLS) algorithms are established tools for the solution of computationally hard problems arising in computer science, business adm- istration, engineering, biology, and various other disciplines. To a large extent, their success is due to their conceptual simplicity, broad applicability and high performance for many important problems studied in academia and enco- tered in real-world applications. SLS methods include a wide spectrum of te- niques, ranging from constructive search procedures and iterative improvement algorithms to more complex SLS methods, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search, and variable neighborhood search. Historically, the development of e?ective SLS algorithms has been guided to a large extent by experience and intuition. In recent years, it has become - creasingly evident that success with SLS algorithms depends not merely on the adoption and e?cient implementation of the most appropriate SLS technique for a given problem, but also on the mastery of a more complex algorithm - gineering process. Challenges in SLS algorithm development arise partly from the complexity of the problems being tackled and in part from the many - grees of freedom researchers and practitioners encounter when developing SLS algorithms. Crucial aspects in the SLS algorithm development comprise al- rithm design, empirical analysis techniques, problem-speci?c background, and background knowledge in several key disciplines and areas, including computer science, operations research, arti?cial intelligence, and statistics.

This book constitutes the refereed proceedings of the International Workshop on Engineering Stochastic Local Search Algorithms 2007, held in Brussels, Belgium, September 6-8, 2007.

The 12 revised full papers presented together with 9 short papers were carefully reviewed and selected from more than 50 submissions. The topics include Methodological developments, behavior of SLS algorithms, search space analysis, algorithm performance, tuning procedures, AI/OR techniques and dynamic behaviour.

The 7th International Conference on Theory and Applications of Satis?ab- ity Testing (SAT 2004) was held 10-13 May 2004 in Vancouver, BC, Canada. The conference featured 9 technical paper sessions, 2 poster sessions, as well as the 2004 SAT Solver Competition and the 2004 QBF Solver Evaluation. It also included invited talks by Stephen A. Cook (University of Toronto) and Kenneth McMillan (Cadence Berkeley Labs). The 89 participants represented no less than 17 countries and four continents. SAT 2004 continued the series of meetings which started with the Workshops on Satis?ability held in Siena, Italy (1996), Paderborn, Germany (1998) and Renesse, The Netherlands (2000); the Workshop on Theory and Applications of Satis?ability Testing held in Boston, USA(2001);theSymposiumonTheoryandApplicationsofSatis?abilityTesting held in Cincinnati, USA (2002); and the 6th International Conference on Theory and Applications of Satis?ability Testing held in Santa Margherita Ligure, Italy (2003). The International Conference on Theory and Applications of Satis?ability Testing is the primary annual meeting for researchers studying the propo- tional satis?ability problem (SAT), a prominent problem in both theoretical and applied computer science. SAT lies at the heart of the most important open problem in complexity theory (P vsNP) and underlies many applications in, among other examples, arti?cial intelligence, operations research and electronic design engineering. The primary objective of the conferences is to bring together researchersfromvariousareasandcommunities, includingtheoreticalandexp- imental computer science as well as many relevant application areas, to promote collaboration and the communication of new theoretical and practical results in SAT-related research and its industrial applications

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 Stutzle 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.