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020 _a9783540497745
_99783540497745
024 7 _a10.1007/9783540497745
_2doi
035 _avtls000349863
039 9 _a201509030454
_bVLOAD
_c201405050348
_dVLOAD
_y201402071213
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTA329-348
100 1 _aYang, Shengxiang.
_eeditor.
_9331163
245 1 0 _aEvolutionary Computation in Dynamic and Uncertain Environments /
_cedited by Shengxiang Yang, Yew-Soon Ong, Yaochu Jin.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2007.
300 _axxiii, 605 páginas 272 ilustraciones Also available online.
_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 ;
_v51
500 _aSpringer eBooks
505 0 _aOptimum Tracking in Dynamic Environments -- Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments -- Particle Swarm Optimization in Dynamic Environments -- Evolution Strategies in Dynamic Environments -- Orthogonal Dynamic Hill Climbing Algorithm: ODHC -- Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments -- Learning and Anticipation in Online Dynamic Optimization -- Evolutionary Online Data Mining: An Investigation in a Dynamic Environment -- Adaptive Business Intelligence: Three Case Studies -- Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks -- Approximation of Fitness Functions -- Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization -- Evolutionary Shape Optimization Using Gaussian Processes -- A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer -- An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks -- Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design -- Handling Noisy Fitness Functions -- Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation -- Evolving Multi Rover Systems in Dynamic and Noisy Environments -- A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions -- Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem -- Search for Robust Solutions -- Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty -- Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms -- Evolutionary Robust Design of Analog Filters Using Genetic Programming -- Robust Salting Route Optimization Using Evolutionary Algorithms -- An Evolutionary Approach For Robust Layout Synthesis of MEMS -- A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs -- An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models -- Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm.
520 _aThis book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aOng, Yew-Soon.
_eeditor.
_9331164
700 1 _aJin, Yaochu.
_eeditor.
_9329091
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783540497721
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-540-49774-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c296238
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