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Books > Computing & IT > General theory of computing > Data structures
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Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics - International Workshop, SLS 2009, Brussels, Belgium, September 3-5, 2009, Proceedings (Paperback, 2009 ed.)
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Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics - International Workshop, SLS 2009, Brussels, Belgium, September 3-5, 2009, Proceedings (Paperback, 2009 ed.)
Series: Lecture Notes in Computer Science, 5752
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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.
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