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020 _a9783540794387
_99783540794387
024 7 _a10.1007/9783540794387
_2doi
035 _avtls000351868
039 9 _a201509030450
_bVLOAD
_c201405060257
_dVLOAD
_y201402171147
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTA329-348
100 1 _aCotta, Carlos.
_eeditor.
_9330522
245 1 0 _aAdaptive and Multilevel Metaheuristics /
_cedited by Carlos Cotta, Marc Sevaux, Kenneth Sörensen.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2008.
300 _brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v136
500 _aSpringer eBooks
505 0 _aReviews 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.
520 _aOne 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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aSevaux, Marc.
_eeditor.
_9335355
700 1 _aSörensen, Kenneth.
_eeditor.
_9335356
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783540794370
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-540-79438-7
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c298788
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