Adaptive and Multilevel Metaheuristics

Herausgegeben:Cotta, Carlos; Sevaux, Marc; Sörensen, Kenneth

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Studies in Computational Intelligence 136
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-642-09833-8
• Softcover reprint of hardcover 1st ed. 2008
• Seitenzahl: 292
• Erscheinungstermin: 28. Oktober 2010
• Englisch
• Abmessung: 235mm x 155mm x 16mm
• Gewicht: 447g
• ISBN-13: 9783642098338
• ISBN-10: 3642098339
• Artikelnr.: 32058807

Inhaltsangabe

Reviews of the Field.- Hyperheuristics: Recent Developments.- Self-Adaptation in Evolutionary Algorithms for Combinatorial Optimisation.- New Techniques and Applications.- An Efficient Hyperheuristic for Strip-Packing Problems.- Probability-Driven Simulated Annealing for Optimizing Digital FIR Filters.- RASH: A Self-adaptive Random Search Method.- Market Based Allocation of Transportation Orders to Vehicles in Adaptive Multi-objective Vehicle Routing.- A Simple Evolutionary Algorithm with Self-adaptation for Multi-objective Nurse Scheduling.- Individual Evolution as an Adaptive Strategy for Photogrammetric Network Design.- Adaptive Estimation of Distribution Algorithms.- Initialization and Displacement of the Particles in TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm.- Evolution of Descent Directions.- "Multiple Neighbourhood" Search in Commercial VRP Packages: Evolving Towards Self-Adaptive Methods.- Automated Parameterisation of a Metaheuristic for the Orienteering Problem.