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The importance of tuning metaheuristics is widely acknowledged in scientific literature. However, there is very little dedicated research on the subject. Typically, scientists and practitioners tune metaheuristics by hand, guided only by their experience and by some rules of thumb. Tuning metaheuristics is often considered to be more of an art than a science. This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning. By adopting a machine learning perspective, the author gives a formal definition of the tuning problem, develops a generic algorithm for tuning metaheuristics, and defines an appropriate experimental methodology for assessing the performance of metaheuristics.

Machine learning --- Heuristic algorithms --- Mathematical optimization --- Computer Science --- Civil Engineering --- Applied Mathematics --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Machine learning. --- Combinatorial optimization. --- Heuristic programming. --- Computational intelligence. --- Intelligence, Computational --- Optimization, Combinatorial --- Learning, Machine --- Mathematics. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Applications of Mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Engineering --- Engineering analysis --- Mathematical analysis --- 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 --- Math --- Science --- Mathematics --- Artificial intelligence --- Soft computing --- Programming (Mathematics) --- Combinatorial analysis --- Mathematical and Computational Engineering. --- Artificial Intelligence.

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Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- machine learning --- ingenieurswetenschappen --- robots

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Complex analysis --- Operational research. Game theory --- Computer science --- Information systems --- Computer. Automation --- gegevensopslag --- IR (information retrieval) --- stochastische analyse --- complexe analyse (wiskunde) --- database management --- informatietechnologie --- programmatielogica

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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

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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.

Complex analysis --- Operational research. Game theory --- Computer science --- Information systems --- Computer. Automation --- gegevensopslag --- IR (information retrieval) --- stochastische analyse --- complexe analyse (wiskunde) --- database management --- informatietechnologie --- programmatielogica