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000724332 24500 $$aAdvances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences$$h[electronic resource] /$$cDavid Greiner [and 5 more], editors.
000724332 264_1 $$aCham :$$bSpringer,$$c[2014]
000724332 264_4 $$c©2015
000724332 300__ $$a1 online resource (xi, 522 pages) :$$billustrations.
000724332 336__ $$atext$$btxt$$2rdacontent
000724332 337__ $$acomputer$$bc$$2rdamedia
000724332 338__ $$aonline resource$$bcr$$2rdacarrier
000724332 4901_ $$aComputational Methods in Applied Sciences,$$x1871-3033 ;$$vvolume 36
000724332 500__ $$aIncludes author index.
000724332 504__ $$aReferences2 Hybrid Optimization Algorithms and Hybrid Response Surfaces; 2.1 Introduction; 2.2 Hybrid Optimization Algorithm Concepts; 2.3 Hybrid Response Surface Generation Concepts; 2.3.1 Polynomial Regression; 2.3.2 Self Organizing Algorithms [19, 20]; 2.3.3 Kriging; 2.3.4 Radial Basis Functions; 2.3.5 Wavelet Based Neural Networks [31, 32]; 2.4 Hybrid Methods for Response Surfaces; 2.4.1 Fittest Polynomial Radial Basis Function (FP-RBF) [28]; 2.4.2 Kriging Approximation with Fittest Polynomial Radial Basis Function (KRG-FP-RBF); 2.4.3 Hybrid Self Organizing Model With RBF [20]
000724332 5050_ $$aPreface; 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
000724332 5058_ $$a2.4.4 Genetic Algorithm Based Wavelet Neural Network (HYBWNN) [31, 32]2.5 Comparison Among Different Response Surface Algorithms; 2.5.1 Fittest Polynomial RBF Versus Hybrid Wavelet Neural Network [42]; 2.5.2 Fittest Polynomial RBF Versus Kriging; 2.5.3 Fittest Polynomial RBF Versus Hybrid Self Organizing Response Surface Method -- HYBSORSM ; 2.5.4 Fittest Polynomial RBF Versus Kriging Approximation with Fittest Polynomial Radial Basis Function -- KRG-FP-RBF; 2.6 Conclusions; References; 3 A Genetic Algorithm for a Sensor Device Location Problem; 3.1 Introductionaut]Daniele, Elia
000724332 5058_ $$a3.2 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
000724332 5058_ $$a4.3 Some Applications Using ANNEO and EOANN4.4 Conclusions; References; 5 Reliability-Based Design Optimization with the Generalized Inverse Distribution Function; 5.1 Introduction; 5.2 Robust Optimization; 5.3 The Generalized Inverse Distribution Function Method; 5.4 A Robust Optimization Test Case; 5.5 Evaluating and Improving the Quantile Estimation; 5.6 Single and Multi-objective Reliability Optimization Tests; 5.7 Conclusions; References; Part IITheoretical and Numerical Methodsand Tools for Optimization:Numerical Methods and Tools
000724332 506__ $$aAccess limited to authorized users.
000724332 520__ $$aThis book contains state-of-the-art contributions in the field of evolutionary and deterministic methods for design, optimization and control in engineering and sciences. Specialists have written each of the 34 chapters as extended versions of selected papers presented at the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2013). The conference was one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS). Topics treate.
000724332 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 8, 2015).
000724332 650_0 $$aMathematical optimization$$vCongresses.
000724332 650_0 $$aEngineering design$$xMathematical models$$vCongresses.
000724332 7001_ $$aGreiner, David,$$eeditor.
000724332 7112_ $$aEUROGEN (Conference)$$n(10th :$$d2013 :$$cLas Palmas, Canary Islands)
000724332 77608 $$iPrint version:$$aGreiner, David$$tAdvances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences$$dCham : Springer International Publishing,c2014$$z9783319115405
000724332 830_0 $$aComputational methods in applied sciences ;$$vvolume 36.
000724332 852__ $$bebk
000724332 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-11541-2$$zOnline Access$$91397441.1
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000724332 980__ $$aEBOOK
000724332 980__ $$aBIB
000724332 982__ $$aEbook
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