TY - BOOK ID - 5453879 TI - Adaptive and multilevel metaheuristics AU - Cotta, Carlos AU - Sevaux, Marc AU - Sòˆrensen, Kenneth PY - 2008 SN - 9783540794370 3540794379 3540794387 PB - Berlin ; Heidelberg : Springer, DB - UniCat KW - Heuristic programming. KW - Combinatorial optimization. KW - Programmation heuristique KW - Optimisation combinatoire KW - Heuristic programming KW - Combinatorial optimization KW - Civil Engineering KW - Operations Research KW - Applied Mathematics KW - Civil & Environmental Engineering KW - Engineering & Applied Sciences KW - Optimization, Combinatorial KW - Engineering. KW - Artificial intelligence. KW - Applied mathematics. KW - Engineering mathematics. KW - Appl.Mathematics/Computational Methods of Engineering. KW - Artificial Intelligence (incl. Robotics). KW - Mathematical and Computational Engineering. KW - Artificial Intelligence. KW - Engineering KW - Engineering analysis KW - Mathematical analysis KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Mathematics KW - Artificial intelligence KW - Programming (Mathematics) KW - Combinatorial analysis KW - Mathematical optimization UR - https://www.unicat.be/uniCat?func=search&query=sysid:5453879 AB - One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself -endowing it with smart mechanisms to self-adapt to the problem- has been a long pursued goal in the field of metaheuristics. These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc. Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization. ER -