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Computational Intelligence in Expensive Optimization Problems

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Feldname	Details
Vorliegende Sprache	eng
Hinweise auf parallele Ausgaben	322617952 Buchausg. u.d.T.: ‡Computational intelligence in expensive optimization problems
ISBN	978-3-642-10700-9
Name	Tenne, Yoel
Name	Goh, Chi-Keong
Name ANZEIGE DER KETTE	Goh, Chi-Keong
T I T E L	Computational Intelligence in Expensive Optimization Problems
Verlagsort	Berlin, Heidelberg
Verlag	Springer-Verlag Berlin Heidelberg
Erscheinungsjahr	2010
Erscheinungsjahr	2010
Umfang	Online-Ressource (800p. 270 illus, digital)
Reihe	Adaptation Learning and Optimization ; 2
Notiz / Fußnoten	Includes bibliographical references and index
Weiterer Inhalt	Title Page; Preface; Contents; Part I Techniques for Resource-Intensive Problems; A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms; Introduction; Fitness Approximation Methods; Instance-Based Learning Methods; Machine Learning Methods; Statistical Learning Methods; Existing Research in Multi-surrogate Assisted EAs; Comparative Studies for Different Approximate Models; The Working Styles of Fitness Approximation; Direct Fitness Replacement Methods; Indirect Fitness Approximation Methods; The Management of Fitness Approximation; Evolution Control. Offline Model TrainingOnline Model Updating; Hierarchical Approximate Models and Model Migration; Case Studies: Two Surrogate-Assisted EA Real-World Applications; The Welded Beam Design Domain; Supersonic Aircraft Design Domain; Final Remarks; References; References; References; A Review of Techniques for Handling ExpensiveFunctions in Evolutionary Multi-Objective Optimization; Introduction; Basic Concepts; Pareto Dominance; Pareto Optimality; Pareto Front; Knowledge Incorporation; Surrogates; Polynomials: Response Surface Methods (RSM); Gaussian Process or Kriging; Radial Basis Functions. Artificial Neural NetworksSupport Vector Machines; Clustering; Fitness Inheritance; Real-World Applications; Use of Problem Approximation; Use of RSM by Polynomial Approximation; Use of Artificial Neural Networks; Use of a Gaussian Process or Kriging; Use of Clustering; Use of Radial Basis Functions; Conclusions and Future Research Paths; References; Multilevel Optimization Algorithms Based on Metamodel- and Fitness Inheritance-Assisted Evolutionary Algorithms; Introduction; Metamodel-Assisted EAs and Distributed MAEAs; Surrogate Evaluation Models for MAEAs; Fitness Inheritance. Radial Basis Function (RBF) NetworksAssessment of MAEA and DMAEA; Multilevel Search Algorithms and the Underlying Hierarchy; The Three Multilevel Modes - Defining a HDMAEA; Distributed Hierarchical Search - DHMAEA vs. HDMAEA; Assessment of Multilevel-Hierarchical Optimization; Optimization of an Annular Cascade; Conclusions; References; Knowledge-Based Variable-Fidelity Optimization of Expensive Objective Functions through Space Mapping; Introduction; Space Mapping Optimization; Formulation of the Space Mapping Algorithm; Space Mapping Surrogate Models; Characterization of Space Mapping. Practical Issues and Open ProblemsSpace Mapping Illustration; Space Mapping Efficiency; Example 1: Microstrip Bandpass Filter; Example 2: Ring Antenna b32; Discussion; Exploiting Extra Knowledge: Tuning Space Mapping; Tuning Space Mapping Formulation; TSM Optimization of Chebyshev Bandpass Filter; Summary; Conclusions; References; Reducing Function Evaluations Using Adaptively Controlled Differential Evolution with Rough Approximation Model; Introduction; Optimization and Approximation Models; Optimization Problems; Evolutionary Algorithms Using Approximation Models. Estimated Comparison Method
Titelhinweis	Buchausg. u.d.T.: ‡Computational intelligence in expensive optimization problems
ISBN	ISBN 978-3-642-10701-6
Klassifikation	TBJ
	MAT003000
	*90-06
	90C60
	68Q25
	00B15
	006.31
	519
	519.6
	TA329-348
	TA640-643
	ST 301
Kurzbeschreibung	In modern science and engineering, laboratory experiments are replaced by high fidelity and computationally expensive simulations. Using such simulations reduces costs and shortens development times but introduces new challenges to design optimization process. Examples of such challenges include limited computational resource for simulation runs, complicated response surface of the simulation inputs-outputs, and etc. Under such difficulties, classical optimization and analysis methods may perform poorly. This motivates the application of computational intelligence methods such as evolutionary
1. Schlagwortkette	Optimierungsproblem
1. Schlagwortkette	Soft Computing
1. Schlagwortkette ANZEIGE DER KETTE	Optimierungsproblem -- Soft Computing
2. Schlagwortkette	Optimierungsproblem
2. Schlagwortkette	Soft Computing
ANZEIGE DER KETTE	Optimierungsproblem -- Soft Computing
SWB-Titel-Idn	322304482
Signatur	Springer E-Book
Bemerkungen	Elektronischer Volltext - Campuslizenz
Elektronische Adresse	$uhttp://dx.doi.org/10.1007/978-3-642-10701-6
Internetseite / Link	Volltext
Siehe auch	Volltext
Siehe auch	Cover
Siehe auch	Inhaltstext

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