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The Resource Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences, David Greiner [and 5 more], editors, (electronic book)

Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences, David Greiner [and 5 more], editors, (electronic book)

Label
Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences
Title
Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences
Statement of responsibility
David Greiner [and 5 more], editors
Contributor
Editor
Subject
Genre
Language
eng
Summary
This book contains state-of-the-art contributions in the field of evolutionary and deterministic methods for design, optimization and control in engineering and sciences. Specialists have written each of the 34 chapters as extended versions of selected papers presented at the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2013). The conference was one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS). Topics treate
Member of
Cataloging source
N$T
Dewey number
519.6
Illustrations
illustrations
Index
index present
LC call number
QA402.5
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
2013
http://library.link/vocab/relatedWorkOrContributorName
  • Greiner, David
  • EUROGEN (Conference)
Series statement
Computational Methods in Applied Sciences,
Series volume
volume 36
http://library.link/vocab/subjectName
  • Mathematical optimization
  • Engineering design
Label
Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences, David Greiner [and 5 more], editors, (electronic book)
Instantiates
Publication
Copyright
Note
Includes author index
Antecedent source
unknown
Bibliography note
References2 Hybrid Optimization Algorithms and Hybrid Response Surfaces; 2.1 Introduction; 2.2 Hybrid Optimization Algorithm Concepts; 2.3 Hybrid Response Surface Generation Concepts; 2.3.1 Polynomial Regression; 2.3.2 Self Organizing Algorithms [19, 20]; 2.3.3 Kriging; 2.3.4 Radial Basis Functions; 2.3.5 Wavelet Based Neural Networks [31, 32]; 2.4 Hybrid Methods for Response Surfaces; 2.4.1 Fittest Polynomial Radial Basis Function (FP-RBF) [28]; 2.4.2 Kriging Approximation with Fittest Polynomial Radial Basis Function (KRG-FP-RBF) ; 2.4.3 Hybrid Self Organizing Model With RBF [20]
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface; Contents; Part ITheoretical and Numerical Methodsand Tools for Optimization:Theoretical Methods and Tools; 1 Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges; 1.1 Introduction; 1.2 Basic Concepts; 1.3 Dealing with Expensive Problems; 1.3.1 Use of Problem Approximation; 1.3.2 Use of Functional Approximation; 1.3.3 Use of Evolutionary Approximation; 1.4 Other Approaches; 1.4.1 Cultural Algorithms; 1.4.2 Use of Very Small Population Sizes; 1.4.3 Use of Efficient Search Techniques; 1.5 Future Research Paths; 1.6 Conclusions
  • 2.4.4 Genetic Algorithm Based Wavelet Neural Network (HYBWNN) [31, 32]2.5 Comparison Among Different Response Surface Algorithms; 2.5.1 Fittest Polynomial RBF Versus Hybrid Wavelet Neural Network [42]; 2.5.2 Fittest Polynomial RBF Versus Kriging; 2.5.3 Fittest Polynomial RBF Versus Hybrid Self Organizing Response Surface Method -- HYBSORSM ; 2.5.4 Fittest Polynomial RBF Versus Kriging Approximation with Fittest Polynomial Radial Basis Function -- KRG-FP-RBF; 2.6 Conclusions; References; 3 A Genetic Algorithm for a Sensor Device Location Problem; 3.1 Introductionaut]Daniele, Elia
  • 3.2 Constrained Location Problem3.2.1 Preliminaries; 3.2.2 The Facility Location Game; 3.2.3 Location of Sensor Devices on a Grid; 3.3 Nash Genetic Algorithm for the Location Problem; 3.3.1 Genetic Algorithm; 3.3.2 Nash Equilibrium Game; 3.3.3 Test Cases; 3.4 Conclusions; References; 4 The Role of Artificial Neural Networks in Evolutionary Optimisation: A Review; 4.1 Introduction; 4.1.1 Evolutionary Algorithms; 4.1.2 Artificial Neural Networks ANN; 4.2 Different Use of ANNEO and EOANN; 4.2.1 The Use of EOs in ANNs: EOANN; 4.2.2 The Use of ANNs in EO: ANNEO
  • 4.3 Some Applications Using ANNEO and EOANN4.4 Conclusions; References; 5 Reliability-Based Design Optimization with the Generalized Inverse Distribution Function; 5.1 Introduction; 5.2 Robust Optimization; 5.3 The Generalized Inverse Distribution Function Method; 5.4 A Robust Optimization Test Case; 5.5 Evaluating and Improving the Quantile Estimation; 5.6 Single and Multi-objective Reliability Optimization Tests; 5.7 Conclusions; References; Part IITheoretical and Numerical Methodsand Tools for Optimization:Numerical Methods and Tools
Control code
SPR895661108
Dimensions
unknown
Extent
1 online resource (xi, 522 pages)
File format
unknown
Form of item
online
Isbn
9783319115412
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote
Label
Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences, David Greiner [and 5 more], editors, (electronic book)
Publication
Copyright
Note
Includes author index
Antecedent source
unknown
Bibliography note
References2 Hybrid Optimization Algorithms and Hybrid Response Surfaces; 2.1 Introduction; 2.2 Hybrid Optimization Algorithm Concepts; 2.3 Hybrid Response Surface Generation Concepts; 2.3.1 Polynomial Regression; 2.3.2 Self Organizing Algorithms [19, 20]; 2.3.3 Kriging; 2.3.4 Radial Basis Functions; 2.3.5 Wavelet Based Neural Networks [31, 32]; 2.4 Hybrid Methods for Response Surfaces; 2.4.1 Fittest Polynomial Radial Basis Function (FP-RBF) [28]; 2.4.2 Kriging Approximation with Fittest Polynomial Radial Basis Function (KRG-FP-RBF) ; 2.4.3 Hybrid Self Organizing Model With RBF [20]
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface; Contents; Part ITheoretical and Numerical Methodsand Tools for Optimization:Theoretical Methods and Tools; 1 Multi-objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges; 1.1 Introduction; 1.2 Basic Concepts; 1.3 Dealing with Expensive Problems; 1.3.1 Use of Problem Approximation; 1.3.2 Use of Functional Approximation; 1.3.3 Use of Evolutionary Approximation; 1.4 Other Approaches; 1.4.1 Cultural Algorithms; 1.4.2 Use of Very Small Population Sizes; 1.4.3 Use of Efficient Search Techniques; 1.5 Future Research Paths; 1.6 Conclusions
  • 2.4.4 Genetic Algorithm Based Wavelet Neural Network (HYBWNN) [31, 32]2.5 Comparison Among Different Response Surface Algorithms; 2.5.1 Fittest Polynomial RBF Versus Hybrid Wavelet Neural Network [42]; 2.5.2 Fittest Polynomial RBF Versus Kriging; 2.5.3 Fittest Polynomial RBF Versus Hybrid Self Organizing Response Surface Method -- HYBSORSM ; 2.5.4 Fittest Polynomial RBF Versus Kriging Approximation with Fittest Polynomial Radial Basis Function -- KRG-FP-RBF; 2.6 Conclusions; References; 3 A Genetic Algorithm for a Sensor Device Location Problem; 3.1 Introductionaut]Daniele, Elia
  • 3.2 Constrained Location Problem3.2.1 Preliminaries; 3.2.2 The Facility Location Game; 3.2.3 Location of Sensor Devices on a Grid; 3.3 Nash Genetic Algorithm for the Location Problem; 3.3.1 Genetic Algorithm; 3.3.2 Nash Equilibrium Game; 3.3.3 Test Cases; 3.4 Conclusions; References; 4 The Role of Artificial Neural Networks in Evolutionary Optimisation: A Review; 4.1 Introduction; 4.1.1 Evolutionary Algorithms; 4.1.2 Artificial Neural Networks ANN; 4.2 Different Use of ANNEO and EOANN; 4.2.1 The Use of EOs in ANNs: EOANN; 4.2.2 The Use of ANNs in EO: ANNEO
  • 4.3 Some Applications Using ANNEO and EOANN4.4 Conclusions; References; 5 Reliability-Based Design Optimization with the Generalized Inverse Distribution Function; 5.1 Introduction; 5.2 Robust Optimization; 5.3 The Generalized Inverse Distribution Function Method; 5.4 A Robust Optimization Test Case; 5.5 Evaluating and Improving the Quantile Estimation; 5.6 Single and Multi-objective Reliability Optimization Tests; 5.7 Conclusions; References; Part IITheoretical and Numerical Methodsand Tools for Optimization:Numerical Methods and Tools
Control code
SPR895661108
Dimensions
unknown
Extent
1 online resource (xi, 522 pages)
File format
unknown
Form of item
online
Isbn
9783319115412
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote

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