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.
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