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Advances in evolutionary an...

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

Greiner, David,

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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Congress proceedings (reports) Conference papers and proceedings. Conference papers and proceedings. Actes de congrès.

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monografia Rebiun34396961 https://catalogo.rebiun.org/rebiun/record/Rebiun34396961 m o d cr cnu|||unuuu 141117t20142015sz a o 101 0 eng d GBB8L7045 bnb 900312101 908088834 961520899 1005809830 1012050563 9783319115412 electronic bk.) 3319115413 electronic bk.) 3319115405 9783319115405 9783319115405 10.1007/978-3-319-11541-2 doi AU@ 000056034611 DEBBG BV042990911 DEBBG BV043618749 NLGGC 383918383 NZ1 15841499 UKMGB 019128114 CBUC 991018640549706706 com.springer.onix.9783319115412 Springer Nature N$T eng rda pn N$T YDXCP GW5XE OCLCO OCLCF N$T IDEBK EBLCP OCLCO OCLCQ OCLCO OCL OCLCO DEBBG UAB Z5A ESU OCLCQ COO TXI AZK VT2 IOG NJR U3W OCLCQ CEF OCLCQ AU@ UKMGB OCLCQ WURST AJS OCLCO COM OCLCO OCLCQ OCLCO MAT 003000 bisacsh MAT 029000 bisacsh TBD bicssc 519.6 23 Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences David Greiner [and 5 more], editors Cham Springer [2014] Cham Cham Springer 2015 1 online resource (xi, 522 pages) illustrations 1 online resource (xi, 522 pages) Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Computational Methods in Applied Sciences 1871-3033 volume 36 Includes author index 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] 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 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 Mathematical optimization- Congresses Engineering design- Mathematical models- Congresses Optimisation mathématique- Congrès Conception technique- Modèles mathématiques- Congrès Mechanics of solids. Maths for scientists. Optimization. Technical design. MATHEMATICS- Applied. MATHEMATICS- Probability & Statistics- General. Engineering design- Mathematical models. Mathematical optimization. mechanica mechanics engineering computational science optimalisatie optimization ontwerp design Engineering (General) Techniek (algemeen) Congress proceedings (reports) Conference papers and proceedings. Conference papers and proceedings. Actes de congrès. Greiner, David editor EUROGEN (Conference) 10th :. 2013 :. Las Palmas, Canary Islands) Print version Greiner, David. Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Cham : Springer International Publishing, 2014 9783319115405 Computational methods in applied sciences volume 36

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monografia Rebiun34396961 https://catalogo.rebiun.org/rebiun/record/Rebiun34396961 m o d cr cnu|||unuuu 141117t20142015sz a o 101 0 eng d GBB8L7045 bnb 900312101 908088834 961520899 1005809830 1012050563 9783319115412 electronic bk.) 3319115413 electronic bk.) 3319115405 9783319115405 9783319115405 10.1007/978-3-319-11541-2 doi AU@ 000056034611 DEBBG BV042990911 DEBBG BV043618749 NLGGC 383918383 NZ1 15841499 UKMGB 019128114 CBUC 991018640549706706 com.springer.onix.9783319115412 Springer Nature N$T eng rda pn N$T YDXCP GW5XE OCLCO OCLCF N$T IDEBK EBLCP OCLCO OCLCQ OCLCO OCL OCLCO DEBBG UAB Z5A ESU OCLCQ COO TXI AZK VT2 IOG NJR U3W OCLCQ CEF OCLCQ AU@ UKMGB OCLCQ WURST AJS OCLCO COM OCLCO OCLCQ OCLCO MAT 003000 bisacsh MAT 029000 bisacsh TBD bicssc 519.6 23 Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences David Greiner [and 5 more], editors Cham Springer [2014] Cham Cham Springer 2015 1 online resource (xi, 522 pages) illustrations 1 online resource (xi, 522 pages) Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Computational Methods in Applied Sciences 1871-3033 volume 36 Includes author index 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] 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 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 Mathematical optimization- Congresses Engineering design- Mathematical models- Congresses Optimisation mathématique- Congrès Conception technique- Modèles mathématiques- Congrès Mechanics of solids. Maths for scientists. Optimization. Technical design. MATHEMATICS- Applied. MATHEMATICS- Probability & Statistics- General. Engineering design- Mathematical models. Mathematical optimization. mechanica mechanics engineering computational science optimalisatie optimization ontwerp design Engineering (General) Techniek (algemeen) Congress proceedings (reports) Conference papers and proceedings. Conference papers and proceedings. Actes de congrès. Greiner, David editor EUROGEN (Conference) 10th :. 2013 :. Las Palmas, Canary Islands) Print version Greiner, David. Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Cham : Springer International Publishing, 2014 9783319115405 Computational methods in applied sciences volume 36