Adaptive and Multilevel Metaheuristics

Carlos Cotta

ISBN 10: 3642098339 / ISBN 13: 9783642098338

Verlag: Springer Berlin Heidelberg Okt 2010, 2010

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 292 pp. Englisch.

Bestandsnummer des Verkäufers 9783642098338

Bibliografische Details

• Titel: Adaptive and Multilevel Metaheuristics
• Verlag: Springer Berlin Heidelberg Okt 2010
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 3642098339
• ISBN-13: 9783642098338
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 155 H x 16 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 16 mm
• Gewicht: 446 Gramm

Über diesen Titel

Inhaltsangabe

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.