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About Adaptive and Multilevel Metaheuristics
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.
Detailed Information
| Author: | Konstantin Chakhlevitch, Peter Cowling (auth.), Carlos Cotta, Marc Sevaux, Kenneth Sörensen (eds.) |
|---|---|
| Publication Year: | 2008 |
| ISBN: | 9783540794370 |
| Pages: | 259 |
| Language: | English |
| File Size: | 7.339 |
| Format: | |
| Price: | FREE |
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