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Title Adaptive and multilevel metaheuristics / Carlos Cotta, Marc Sevaux, Kenneth Sörensen (eds.)
Published Berlin : Springer, [2008]
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Description 1 online resource (xv, 273 pages) : illustrations
Series Studies in computational intelligence, 1860-949X ; v. 136
Studies in computational intelligence ; v. 136. 1860-949X
Contents Part I Reviews of the Field -- Hyperheuristics: Recent Developments -- Self-Adaptation in Evolutionary Algorithms for Combinatorial Optimisation -- Part II 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
Summary 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
Bibliography Includes bibliographical references and indexes
Notes Print version record
Subject Heuristic programming.
Combinatorial optimization.
Form Electronic book
Author Cotta, Carlos.
Sevaux, Marc.
Sörensen, Kenneth.
LC no. 2008925773
ISBN 3540794379 (hbk.)
9783540794370 (hbk.)