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ISBN: 9783642080654

This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncer… Altro …

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Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence, 51) - edizione con copertina flessibile

2010

ISBN: 9783642080654

Editor: Yang, Shengxiang, Editor: Ong, Yew-Soon, Editor: Jin, Yaochu, Springer, Paperback, Auflage: Softcover reprint of hardcover 1st ed. 2007, 628 Seiten, Publiziert: 2010-11-25T00:00:0… Altro …

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Evolutionary Computation in Dynamic and Uncertain Environments - edizione con copertina flessibile

2010, ISBN: 3642080650

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Evolutionary Computation in Dynamic and Uncertain Environments Shengxiang Yang Editor
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9783642080654


This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes 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.

Informazioni dettagliate del libro - Evolutionary Computation in Dynamic and Uncertain Environments Shengxiang Yang Editor


EAN (ISBN-13): 9783642080654
ISBN (ISBN-10): 3642080650
Copertina rigida
Copertina flessibile
Anno di pubblicazione: 2010
Editore: Springer Berlin Heidelberg Core >1 >T
632 Pagine
Peso: 0,941 kg
Lingua: eng/Englisch

Libro nella banca dati dal 2011-05-04T02:43:23+02:00 (Rome)
Pagina di dettaglio ultima modifica in 2023-11-21T23:19:45+01:00 (Rome)
ISBN/EAN: 9783642080654

ISBN - Stili di scrittura alternativi:
3-642-08065-0, 978-3-642-08065-4
Stili di scrittura alternativi e concetti di ricerca simili:
Autore del libro : yang, jin, ong, soon, yao
Titolo del libro: evolutionary environments

Dati dell'editore

Autore: Shengxiang Yang; Yew-Soon Ong; Yaochu Jin
Titolo: Studies in Computational Intelligence; Evolutionary Computation in Dynamic and Uncertain Environments
Editore: Springer; Springer Berlin
605 Pagine
Anno di pubblicazione: 2010-11-30
Berlin; Heidelberg; DE
Stampato / Fatto in
Lingua: Inglese
213,99 € (DE)
219,99 € (AT)
236,00 CHF (CH)
POD
XXIII, 605 p.

BC; Hardcover, Softcover / Technik/Allgemeines, Lexika; Mathematik für Ingenieure; Verstehen; algorithm; algorithms; artificial intelligence; data mining; evolution; evolutionary algorithm; evolutionary computation; evolutionary strategies; genetic algorithms; genetic programming; intelligence; modeling; neural networks; optimization; uncertainty; Mathematical and Computational Engineering Applications; Computational Intelligence; Artificial Intelligence; Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Künstliche Intelligenz; Wahrscheinlichkeitsrechnung und Statistik; BB

Optimum 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 andRobustness 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.
State of the art of evolutionary algorithms in dynamic and uncertain environments Includes supplementary material: sn.pub/extras
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