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Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences

Author: David Greiner
Publisher: Cham : Springer, [2014] ©2015
Series: Computational methods in applied sciences, volume 36.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
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  Read more...
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Genre/Form: Electronic books
Conference papers and proceedings
Congresses
Additional Physical Format: Print version:
Greiner, David.
Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences.
Cham : Springer International Publishing, ©2014
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: David Greiner
ISBN: 9783319115412 3319115413 3319115405 9783319115405
OCLC Number: 895661108
Notes: Includes author index.
Description: 1 online resource (xi, 522 pages) : illustrations.
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.
Series Title: Computational methods in applied sciences, volume 36.
Responsibility: David Greiner [and 5 more], editors.

Abstract:

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.

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