engineering stochastic local search (13 Ergebnisse)

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Taschenbuch. Zustand: Neu. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics | International Workshop, SLS 2009, Brussels, Belgium, September 3-5, 2009, Proceedings | Thomas Stützle (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2009 | Springer | EAN 9783642037504 | Verantwortliche Person für die EU: Lauinger, Sonia, Sonia Lauinger, Lauinger Verlag, Heinrich-Köhler-Platz 8, 76187 Karlsruhe, mail[at]lauinger-verlag[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics | International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007, Proceedings | Thomas Stützle (u. a.) | Taschenbuch | x | Englisch | 2007 | Springer | EAN 9783540744450 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computer science, operations research, and engineering. To a large degree, this popularity is based on the conceptual simplicity of many SLS methods and on their excellent performance on a wide gamut of problems, ranging from rather abstract problems of high academic interest to the very s- ci c problems encountered in many real-world applications. SLS methods range from quite simple construction procedures and iterative improvement algorithms to more complex general-purpose schemes, also widely known as metaheuristics, 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, and overall resembled more an art than a science. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-speci c background, and which relies heavily on knowledge from a number of disciplines and areas, including computer science, operations research, arti cial intelligence, and statistics. This development process needs to be - sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion.