LEADER 02924oam 2200457 450 001 9910299491003321 005 20190911112726.0 010 $a3-319-03422-7 024 7 $a10.1007/978-3-319-03422-5 035 $a(OCoLC)877106574 035 $a(MiFhGG)GVRL6XBD 035 $a(EXLCZ)993710000000075061 100 $a20131105d2014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 12$aA brief introduction to continuous evolutionary optimization /$fOliver Kramer 205 $a1st ed. 2014. 210 1$aCham, Switzerland :$cSpringer,$d2014. 215 $a1 online resource (xi, 94 pages) $cillustrations (some color) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3704 300 $a"ISSN: 2191-530X." 311 $a3-319-03421-9 320 $aIncludes bibliographical references and index. 327 $aPart I Foundations -- Part II Advanced Optimization -- Part III Learning -- Part IV Appendix. 330 $aPractical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator, and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods. 410 0$aSpringerBriefs in applied sciences and technology.$pComputational intelligence. 606 $aEvolutionary computation 606 $aComputational intelligence 615 0$aEvolutionary computation. 615 0$aComputational intelligence. 676 $a006.3 700 $aKramer$b Oliver$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761919 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910299491003321 996 $aA Brief Introduction to Continuous Evolutionary Optimization$91951234 997 $aUNINA LEADER 03125nam 22005655 450 001 9910254170503321 005 20230717191137.0 010 $a3-319-52156-X 024 7 $a10.1007/978-3-319-52156-5 035 $a(CKB)3710000001041167 035 $a(DE-He213)978-3-319-52156-5 035 $a(MiAaPQ)EBC6306502 035 $a(MiAaPQ)EBC5590682 035 $a(Au-PeEL)EBL5590682 035 $a(OCoLC)969344131 035 $a(PPN)198340540 035 $a(EXLCZ)993710000001041167 100 $a20170107d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGenetic Algorithm Essentials /$fby Oliver Kramer 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (IX, 92 p. 38 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v679 311 $a3-319-52155-1 320 $aIncludes bibliographical references and index. 327 $aPart I: Foundations -- Introduction -- Genetic Algorithms -- Parameters -- Part II: Solution Spaces -- Multimodality -- Constraints -- Multiple Objectives -- Part III: Advanced Concepts -- Theory -- Machine Learning -- Applications -- Part IV: Ending -- Summary and Outlook -- Index -- References. 330 $aThis book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v679 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a519.7 700 $aKramer$b Oliver$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761919 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254170503321 996 $aGenetic Algorithm Essentials$92041865 997 $aUNINA LEADER 05484nam 2200733Ia 450 001 9911020069903321 005 20200520144314.0 010 $a9786610272198 010 $a9781280272196 010 $a1280272198 010 $a9780470299777 010 $a0470299770 010 $a9780470868249 010 $a0470868244 010 $a9780470868256 010 $a0470868252 035 $a(CKB)111087027098546 035 $a(EBL)163125 035 $a(OCoLC)52849426 035 $a(SSID)ssj0000182814 035 $a(PQKBManifestationID)11198742 035 $a(PQKBTitleCode)TC0000182814 035 $a(PQKBWorkID)10171951 035 $a(PQKB)11109392 035 $a(MiAaPQ)EBC163125 035 $a(Perlego)2750755 035 $a(EXLCZ)99111087027098546 100 $a20030623d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 13$aAn introduction to parametric digital filters and oscillators /$fMikhail Cherniakov 210 $aChichester $cWiley$dc2003 215 $a1 online resource (263 p.) 300 $aDescription based upon print version of record. 311 08$a9780470851043 311 08$a047085104X 320 $aIncludes bibliographical references and index. 327 $aAn Introduction to Parametric Digital Filters and Oscillators; Contents; Preface; 1 Introduction: Basis of Discrete Signals and Digital Filters; 1.1 Discrete Signals and Systems; 1.2 Discrete Signals; 1.2.1 Time-Domain Representation for Discrete Signals; 1.2.2 Presentation of Discrete Signals by Fourier Transform; 1.2.3 Discrete Fourier Transform; 1.2.4 Laplace and z-Transforms; 1.3 Time-Invariant Discrete Linear Systems; 1.3.1 Difference Equation and Impulse Response; 1.3.2 DLS Representation via Transfer Function; 1.4 Stability and Causality of Discrete Systems 327 $a1.5 Frequency Response of a Discrete Linear System1.5.1 Properties of the Frequency Response of a Discrete Linear System; 1.5.2 Transfer Function versus Frequency Response; 1.6 Case Study: Low-Order Filters; 1.6.1 Purely Recursive Filters; 1.6.2 Effects of Word Length Limitation; 1.6.3 Transversal and Combined Filters; 1.7 Summary; 1.8 Abbreviations; 1.9 Variables; 1.10 References; Part One Linear Discrete Time-Variant Systems; 2 Main Characteristics of Time-Variant Systems; 2.1 Description of a Linear Time-Variant Discrete System Through Difference Equations; 2.2 Impulse Response 327 $a2.3 Generalized Transfer Function2.4 Signals Analysis in Frequency Domain; 2.5 Sampling Frequency Choice for Linear Time-Variant Discrete Systems; 2.6 Random Signals Processing in Linear Time-Variant Discrete Systems; 2.7 Combinations of Time-Variant Systems; 2.7.1 Parallel Connections; 2.7.2 Cascade Connections; 2.7.3 Systems with Feedback; 2.7.4 Continuous and Discrete LTV Systems; 2.8 Time-Varying Sampling; 2.8.1 Systems with Non-Uniform Sampling; 2.8.2 Systems with Stochastic Sampling Interval; 2.9 Summary; 2.10 Abbreviations; 2.11 Variables; 2.12 References 327 $a3 Periodically Time-Variant Discrete Systems3.1 Difference Equation; 3.2 Impulse Response; 3.3 Generalized Transfer Function and Frequency Response; 3.4 Signals in Periodically Linear Time-Variant Systems; 3.4.1 Bifrequency Function; 3.4.2 Deterministic Signal Processing; 3.4.3 Random Signals Processing; 3.5 Generalization of the Sampling Theorem; 3.6 System Stability; 3.6.1 General Stability Problem; 3.6.2 Selection of Stability Criteria; 3.6.3 Stability Evaluation; 3.6.4 Stability of Parametric Recursive Systems; 3.7 Stability of Second-Order Systems; 3.8 Stability of Stochastic Systems 327 $a3.9 Summary3.10 Abbreviations; 3.11 Variables; 3.12 References; Part Two Parametric Systems; 4 Parametric Filters Analysis; 4.1 Non-Recursive Parametric Filters; 4.2 The First-Order Recursive Parametric Filter; 4.2.1 Impulse Response; 4.2.2 Generalized Transfer Function; 4.3 A Recursive Parametric Filter of the Second Order; 4.3.1 Impulse Response; 4.3.2 Generalized Transfer Function; 4.4 Parametric Filters of an Arbitrary Order; 4.4.1 Direct Equation Solution; 4.4.2 Equation Solution in a State Space; 4.5 Approximate Method for Analysis of Periodical Linear Time-Variant Discrete Systems 327 $a4.6 Summary 330 $aSince the 1960s Digital Signal Processing (DSP) has been one of the most intensive fields of study in electronics. However, little has been produced specifically on linear non-adaptive time-variant digital filters.* The first book to be dedicated to Time-Variant Filtering* Provides a complete introduction to the theory and practice of one of the subclasses of time-varying digital systems, parametric digital filters and oscillators* Presents many examples demonstrating the application of the techniquesAn indispensable resource for professional engineers, researchers and PhD 606 $aElectric filters, Digital 606 $aOscillators, Electric 606 $aParametric devices 615 0$aElectric filters, Digital. 615 0$aOscillators, Electric. 615 0$aParametric devices. 676 $a621.3815 676 $a621.3815/324 676 $a621.3815324 700 $aCherniakov$b Mikhail$01841646 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020069903321 996 $aAn introduction to parametric digital filters and oscillators$94421799 997 $aUNINA