Adaptive and Multilevel Metaheuristics / Studies in Computational Intelligence Bd.136 (PDF)
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...
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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.
- 2008, 2008, 275 Seiten, Englisch
- Herausgegeben: Carlos Cotta, Marc Sevaux, Kenneth Sörensen
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3540794387
- ISBN-13: 9783540794387
- Erscheinungsdatum: 17.06.2008
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- Grösse: 8.03 MB
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