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04060nam a22005055i 4500 |
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|a 9783540794387
|9 978-3-540-79438-7
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|a 10.1007/978-3-540-79438-7
|2 doi
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|a MAT003000
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|a 519
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|a Adaptive and Multilevel Metaheuristics
|h [electronic resource] /
|c edited by Carlos Cotta, Marc Sevaux, Kenneth Sörensen.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2008.
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|a XV, 275 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 136
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|a 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.
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|a 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.
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|a Engineering.
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|a Artificial intelligence.
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|a Applied mathematics.
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|a Engineering mathematics.
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|a Engineering.
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|a Appl.Mathematics/Computational Methods of Engineering.
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|a Artificial Intelligence (incl. Robotics).
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|a Cotta, Carlos.
|e editor.
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|a Sevaux, Marc.
|e editor.
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|a Sörensen, Kenneth.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783540794370
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 136
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856 |
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|u http://dx.doi.org/10.1007/978-3-540-79438-7
|z Full Text via HEAL-Link
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|a ZDB-2-ENG
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950 |
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|a Engineering (Springer-11647)
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