| Description |
1 online resource (xv, 273 pages) : illustrations |
| Series |
Studies in computational intelligence, 1860-949X ; v. 136 |
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Studies in computational intelligence ; v. 136. 1860-949X
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| 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.
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Combinatorial optimization.
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Heuristic programming.
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Combinatorial optimization.
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Ingénierie.
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Combinatorial optimization
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Heuristic programming
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| Form |
Electronic book
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| Author |
Cotta, Carlos.
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Sevaux, Marc.
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Sörensen, Kenneth.
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| ISBN |
9783540794387 |
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3540794387 |
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9783540794370 |
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3540794379 |
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