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Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex.   In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters.   With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.

Mathematical optimization. --- Heuristic programming. --- Problem solving --- Computer algorithms. --- Data processing. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Computer science. --- Operations research. --- Decision making. --- Computers. --- Artificial intelligence. --- Computational intelligence. --- Computer Science. --- Theory of Computation. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Optimization. --- Operation Research/Decision Theory. --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Intelligence, Computational --- 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 --- Fifth generation computers --- Neural computers --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Informatics --- Science --- Decision making --- Soft computing --- Programming (Mathematics) --- Algorithms --- Information theory. --- Engineering. --- Artificial Intelligence. --- Operations Research/Decision Theory. --- Communication theory --- Communication --- Construction --- Industrial arts --- Technology --- Mathematical optimization --- Heuristic programming --- Computer algorithms --- Data processing --- Operations Research and Decision Theory.