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Local search has been applied successfully to a diverse collection of optimization problems. It's appreciated for its basic conceptual foundation, its general applicability, and its power to serve as a source for new search paradigms. The typical characteristics of combinatorial optimization problems to which local search can be applied, its relation to complexity theory, and the combination with randomized search features have led to a wealth of interesting theoretical results. However, these results are scattered throughout the literature. This is the first book that presents a large collection of theoretical results in a consistent manner, thus providing the reader with a coherent overview of the achievements obtained so far, but also serving as a source of inspiration for the development of novel results in the challenging field of local search.

Combinatorial optimization. --- Heuristic programming. --- Artificial intelligence --- Programming (Mathematics) --- Optimization, Combinatorial --- Combinatorial analysis --- Mathematical optimization --- Mathematical optimization. --- Computer science. --- Computer software. --- Information theory. --- Artificial intelligence. --- Optimization. --- Discrete Mathematics. --- Mathematics of Computing. --- Algorithm Analysis and Problem Complexity. --- Theory of Computation. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Communication theory --- Communication --- Cybernetics --- Software, Computer --- Computer systems --- Informatics --- Science --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- System analysis --- Discrete mathematics. --- Computer science—Mathematics. --- Algorithms. --- Computers. --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Calculators --- Cyberspace --- Algorism --- Algebra --- Arithmetic --- Discrete mathematical structures --- Mathematical structures, Discrete --- Structures, Discrete mathematical --- Numerical analysis --- Foundations

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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.

Computer algorithms --- Electronic information resource searching --- Search theory --- Heuristic programming --- Stochastic programming --- Algorithmes --- Recherche de l'information électronique --- Programmation heuristique --- Congresses. --- Congrès --- Computer Science --- Engineering & Applied Sciences --- Computer searching --- Electronic searching --- Online searching --- Searching electronic information resources --- Computer science. --- Data structures (Computer science). --- Algorithms. --- Mathematical statistics. --- Data mining. --- Information storage and retrieval. --- Computer Science. --- Data Structures. --- Data Storage Representation. --- Algorithm Analysis and Problem Complexity. --- Probability and Statistics in Computer Science. --- Data Mining and Knowledge Discovery. --- Information Storage and Retrieval. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Algorism --- Algebra --- Arithmetic --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Informatics --- Science --- Statistical methods --- Foundations --- Linear programming --- Operations research --- Artificial intelligence --- Programming (Mathematics) --- Information retrieval --- Data structures (Computer scienc. --- Computer software. --- Information storage and retrieva. --- Software, Computer --- Computer systems --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Artificial intelligence—Data processing. --- Information retrieval. --- Computer architecture. --- Computer science—Mathematics. --- Data Science. --- Data centers --- Architecture, Computer --- Data retrieval --- Data storage --- Discovery, Information --- Information discovery --- Information storage and retrieval --- Retrieval of information --- Documentation --- Information science --- Information storage and retrieval systems