Stochastic Local Search

Author: Holger H. Hoos
Publisher: Morgan Kaufmann
ISBN: 1558608729
Format: PDF
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Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems. Offering a systematic treatment of SLS algorithms, this book examines the general concepts and specific instances of SLS algorithms and considers their development, analysis and application.

Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimization

Author: Luis F. Paquete
Publisher: IOS Press
ISBN: 9781586035969
Format: PDF, ePub
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Stochastic Local Search algorithms were shown to give state-of-the-art results for many other problems, but little is known on how to design and analyse them for Multiobjective Combinatorial Optimization Problems. This book aims to fill this gap. It defines two search models that correspond to two distinct ways of tackling MCOPs by SLS algorithms."

Engineering Stochastic Local Search Algorithms Designing Implementing and Analyzing Effective Heuristics

Author: Thomas Stützle
Publisher: Springer
ISBN: 3642037518
Format: PDF
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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.

AI 2005 Advances in Artificial Intelligence

Author: Shichao Zhang
Publisher: Springer
ISBN:
Format: PDF
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This book constitutes the refereed proceedings of the 18th Australian Joint Conference on Artificial Intelligence, AI 2005, held in Sydney, Australia in December 2005. The 77 revised full papers and 119 revised short papers presented together with the abstracts of 3 keynote speeches were carefully reviewed and selected from 535 submissions. The papers are catgorized in three broad sections, namely: AI foundations and technologies, computational intelligence, and AI in specialized domains. Particular topics addressed by the papers are logic and reasoning, machine learning, game theory, robotic technology, data mining, neural networks, fuzzy theory and algorithms, evolutionary computing, Web intelligence, decision making, pattern recognition, agent technology, and AI applications.

Parallel Problem Solving from Nature PPSN VIII

Author: Xin Yao
Publisher: Springer
ISBN:
Format: PDF, ePub
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This book constitutes the refereed proceedings of the 8th International Conference on Parallel Problem Solving from Nature, PPSN 2004, held in Birmingham, UK, in September 2004. The 119 revised full papers presented were carefully reviewed and selected from 358 submissions. The papers address all current issues in biologically inspired computing; they are organized in topical sections on theoretical and foundational issues, new algorithms, applications, multi-objective optimization, co-evolution, robotics and multi-agent systems, and learning classifier systems and data mining.