Adaptive and Multilevel Metaheuristics Hardback
Edited by Carlos Cotta, Marc Sevaux, Kenneth Sorensen
Part of the Studies in Computational Intelligence series
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.
- Format: Hardback
- Pages: 275 pages, 41 Tables, black and white; XV, 275 p.
- Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
- Publication Date: 29/05/2008
- Category: Mathematics
- ISBN: 9783540794370