LEADER 11203nam 2200601 450 001 996466410203316 005 20231110222754.0 010 $a3-030-66515-1 035 $a(CKB)4100000011946215 035 $a(MiAaPQ)EBC6632764 035 $a(Au-PeEL)EBL6632764 035 $a(OCoLC)1253354229 035 $a(PPN)255882238 035 $a(EXLCZ)994100000011946215 100 $a20220126d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aBlack box optimization, machine learning, and no-free lunch theorems /$fPanos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dŠ2021 215 $a1 online resource (393 pages) 225 1 $aSpringer Optimization and Its Applications ;$vv.170 311 $a3-030-66514-3 327 $aIntro -- Preface -- Contents -- Learning Enabled Constrained Black-Box Optimization -- 1 Introduction -- 2 Constrained Black-Box Optimization -- 3 The Basic Probabilistic Framework -- 3.1 Gaussian Processes -- 3.2 GP-Based Optimization -- 4 Constrained Bayesian Optimization -- 5 Constrained Bayesian Optimization for Partially Defined Objective Functions -- 6 Software for the Generation of Constrained Test Problems -- 6.1 Emmental-Type GKLS Generator -- 7 Conclusions -- References -- Black-Box Optimization: Methods and Applications -- 1 Introduction -- 2 Overview of BBO Methods -- 2.1 Direct Search Methods -- 2.1.1 Simplex Search -- 2.1.2 Coordinate Search -- 2.1.3 Generalized Pattern Search -- 2.1.4 Mesh Adaptive Direct Search -- 2.2 Model-Based Methods -- 2.2.1 Model-Based Trust Region -- 2.2.2 Projection-Based Methods -- 2.3 Heuristic Methods -- 2.3.1 DIRECT -- 2.3.2 Multilevel Coordinate Search -- 2.3.3 Hit-and-Run algorithms -- 2.3.4 Simulated Annealing -- 2.3.5 Genetic Algorithm -- 2.3.6 Particle Swarm Optimization -- 2.3.7 Surrogate Management Framework -- 2.3.8 Branch and Fit -- 2.4 Hybrid Methods -- 2.5 Extension to Constrained Problems -- 2.5.1 Penalty Method -- 2.5.2 Augmented Lagrangian -- 2.5.3 Filter Method -- 2.5.4 Surrogate Modeling -- 3 BBO Solvers -- 4 Recent Applications -- 4.1 Automatic Machine Learning -- 4.2 Optimization Solvers -- 4.3 Fluid Mechanics -- 4.4 Oilfield Development and Operations -- 4.5 Chemical and Biochemical Engineering -- 5 Open Problems and Future Research Directions -- References -- Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives -- 1 Introduction -- 2 Tuning: Strategies -- 2.1 Key Topics -- 2.2 Stochastic Optimization Algorithms -- 2.3 Algorithm Tuning -- 2.4 Example: Grefenstette's Study of Control Parameters for Genetic Algorithms. 327 $a2.5 No Free Lunch Theorems -- 2.6 Tuning for Deterministic Algorithms -- 3 Test Sets -- 3.1 Test Functions -- 3.2 Application Domains -- 3.2.1 Tuning in Industry -- 3.2.2 Energy -- 3.2.3 Water Industry -- 3.2.4 Steel Industry -- 3.2.5 Automotive -- 3.2.6 Information Technology -- 4 Statistical Considerations -- 4.1 Experimental Setup -- 4.2 Design of Experiments -- 4.3 Measuring Performance -- 4.4 Reporting Results -- 5 Parallelization -- 5.1 Overview -- 5.2 Simplistic Approaches -- 5.3 Parallelization in Surrogate Model-Based Optimization -- 5.3.1 Uncertainty-Based Methods -- 5.3.2 Surrogate-Assisted Algorithms -- 6 Tuning Approaches -- 6.1 Overview -- 6.2 Manual Tuning -- 6.3 Automatic Tuning -- 6.4 Interactive Tuning -- 6.5 Internal Tuning -- 7 Tuning Software -- 7.1 Overview -- 7.2 IRACE -- 7.3 SPOT -- 7.4 SMAC -- 7.5 ParamILS -- 7.6 GGA -- 7.7 Usability and Availability of Tuning Software -- 7.8 Example: SPOT -- 8 Research Directions and Open Problems -- 9 Summary and Outlook -- References -- Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization -- 1 Introduction -- 2 Problem Formulation -- 2.1 Collections of Solutions -- 2.2 How Do We Measure the Performance of a QD Algorithm? -- 3 Optimizing a Collection of Solutions -- 3.1 MAP-Elites -- 3.2 A Unified Framework -- 3.2.1 Containers -- 3.2.2 Selection Operators -- 3.2.3 Population Scores -- 3.3 Considerations of Quality-Diversity Optimization -- 4 Origins and Related Work -- 4.1 Searching for Diverse Behaviors -- 4.2 Connections to Multimodal Optimization -- 4.3 Connections to Multitask Optimization -- 5 Current Topics -- 5.1 Expensive Objective Functions -- 5.2 High-Dimensional Feature Space -- 5.3 Learning the Behavior Descriptor -- 5.4 Improving Variation Operators -- 5.5 Noisy Functions -- 6 Conclusion -- References. 327 $aMulti-Objective Evolutionary Algorithms: Past, Present, and Future -- 1 Introduction -- 2 Basic Concepts -- 3 The Past -- 3.1 Non-Elitist Non-Pareto Approaches -- 3.1.1 Linear Aggregating Functions -- 3.1.2 Vector Evaluated Genetic Algorithm (VEGA) -- 3.1.3 Lexicographic Ordering -- 3.1.4 Target-Vector Approaches -- 3.2 Non-Elitist Pareto-Based Approaches -- 3.2.1 Multi-Objective Genetic Algorithm (MOGA) -- 3.2.2 Nondominated Sorting Genetic Algorithm (NSGA) -- 3.2.3 Niched-Pareto Genetic Algorithm (NPGA) -- 3.3 Elitist Pareto-Based Approaches -- 3.3.1 The Strength Pareto Evolutionary Algorithm (SPEA) -- 3.3.2 The Pareto Archived Evolution Strategy (PAES) -- 3.3.3 The Nondominated Sorting Genetic Algorithm-II (NSGA-II) -- 4 The Present -- 4.1 Some Applications -- 5 The Future -- 6 Conclusions -- References -- Black-Box and Data-Driven Computation -- 1 Introduction -- 2 Black Box and Oracle -- 3 Reduction -- 4 Data-Driven Computation -- References -- Mathematically Rigorous Global Optimization and FuzzyOptimization -- 1 Introduction -- 2 Interval Analysis: Fundamentals and Philosophy -- 2.1 Overview -- 2.2 Interval Logic -- 2.3 Extensions -- 2.4 History and References -- 3 Fuzzy Sets: Fundamentals and Philosophy -- 3.1 Fuzzy Logic -- 3.2 A Brief History -- 4 The Branch and Bound Framework: Some Definitions and Details -- 5 Interval Technology: Some Details -- 5.1 Interval Newton Methods -- 5.2 Constraint Propagation -- 5.3 Relaxations -- 5.4 Interval Arithmetic Software -- 6 Fuzzy Technology: A Few Details -- 7 Conclusions -- References -- Optimization Under Uncertainty Explains Empirical Success of Deep Learning Heuristics -- 1 Formulation of the Problem -- 2 Why Rectified Linear Neurons Are Efficient: A Theoretical Explanation -- 3 Why Sigmoid Activation Functions -- 4 Selection of Poolings -- 5 Why Softmax -- 6 Which Averaging Should We Choose. 327 $a7 Proofs -- References -- Variable Neighborhood Programming as a Tool of Machine Learning -- 1 Introduction -- 2 Variable Neighborhood Search -- 3 Variable Neighborhood Programming -- 3.1 Solution Presentation -- 3.2 Neighborhood Structures -- 3.3 Elementary Tree Transformation in Automatic Programming -- 3.3.1 ETT in the Tree of an Undirected Graph -- 3.3.2 ETT in AP Tree -- 3.3.3 Bound on Cardinality of AP-ETT(T) -- 4 VNP for Symbolic Regression -- 4.1 Test Instances and Parameter Values -- 4.2 Comparison of BVNP with Other Methods -- 5 Life Expectancy Estimation as a Symbolic Regression Problem Solved by VNP: Case Study on Russian Districts -- 5.1 Life Expectancy Estimation as a Machine Learning Problem -- 5.2 VNP for Estimating Life Expectancy Problem -- 5.3 Case Study at Russian Districts -- 5.3.1 One-Attribute Analysis -- 5.3.2 Results and Discussion on 3-Attribute Data -- 5.4 Conclusions -- 6 Preventive Maintenance in Railway Planning as a Machine Learning Problem -- 6.1 Literature Review and Motivation -- 6.2 Reduced VNP for Solving the Preventive Maintenance Planning of Railway Infrastructure -- 6.2.1 Learning for Stage 1: Prediction -- 6.2.2 Learning for Stage 2: Classification -- 6.3 Computation Results -- 6.3.1 Prediction -- 6.3.2 Classification -- 6.4 Conclusions and Future Work -- 7 Conclusions -- References -- Non-lattice Covering and Quantization of High Dimensional Sets -- 1 Introduction -- 2 Weak Covering -- 2.1 Comparison of Designs from the View Point of Weak Covering -- 2.2 Reduction to the Probability of Covering a Point by One Ball -- 2.3 Designs of Theoretical Interest -- 3 Approximation of Cd(Zn,r) for Design 1 -- 3.1 Normal Approximation for PU,?,?,r -- 3.2 Refined Approximation for PU,?,?,r -- 3.3 Approximation for Cd(Zn,r) for Design 1 -- 4 Approximating Cd(Zn,r) for Design 2a -- 4.1 Normal Approximation for PU,?,0,r. 327 $a4.2 Refined Approximation for PU,?,0,r -- 4.3 Approximation for Cd(Zn,r) -- 5 Approximating Cd(Zn,r) for Design 2b -- 5.1 Establishing a Connection Between Sampling with and Without Replacement: General Case -- 5.2 Approximation of Cd(Zn,r) for Design 2b. -- 6 Numerical Study -- 6.1 Assessing Accuracy of Approximations of Cd(Zn,r) and Studying Their Dependence on ? -- 6.2 Comparison Across ? -- 7 Quantization in a Cube -- 7.1 Quantization Error and Its Relation to Weak Covering -- 7.2 Quantization Error for Design 1 -- 7.3 Quantization Error for Design 2a -- 7.4 Quantization Error for Design 2b -- 7.5 Accuracy of Approximations for Quantization Error and the ?-Effect -- 8 Comparative Numerical Studies of Covering Properties for Several Designs -- 8.1 Covering Comparisons -- 8.2 Quantization Comparisons -- 9 Covering and Quantization in the d-Simplex -- 9.1 Characteristics of Interest -- 9.2 Numerical Investigation of the ?-Effect for d-Simplex -- 10 Appendix: An Auxiliary Lemma -- References -- Finding Effective SAT Partitionings Via Black-Box Optimization -- 1 Introduction -- 2 Preliminaries -- 2.1 Boolean Satisfiability Problem (SAT) -- 2.2 SAT-Based Cryptanalysis -- 3 Decomposition Sets and Backdoors in SAT with Application to Inversion of Discrete Functions -- 3.1 On Interconnection Between Plain Partitionings and Cryptographic Attacks -- 3.2 Using Monte Carlo Method to Estimate Runtime of SAT-Based Guess-and-Determine Attacks -- 4 Practical Aspects of Evaluating Effectiveness of SAT Partitionings -- 4.1 Narrowing Search Space to SUPBS -- 4.2 Applications of Incremental SAT Solving -- 4.3 Finding Partitionings via Incremental SAT -- 5 Employed Optimization Algorithms -- 6 Experimental Results -- 6.1 Considered Problems -- 6.2 Implementations of Objective Functions -- 6.3 Finding Effective SAT Partitionings. 327 $a6.4 Solving Hard SAT Instances via Found Partitionings. 410 0$aSpringer Optimization and Its Applications 606 $aMachine learning$xMathematics 606 $aAprenentatge automātic$2thub 606 $aOptimitzaciķ matemātica$2thub 606 $aAlgorismes computacionals$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning$xMathematics. 615 7$aAprenentatge automātic 615 7$aOptimitzaciķ matemātica 615 7$aAlgorismes computacionals 676 $a006.31 702 $aPardalos$b P. M$g(Panos M.),$f1954- 702 $aRasskazova$b Varvara 702 $aVrahatis$b Michael N.$f1955- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466410203316 996 $aBlack box optimization, machine learning, and no-free lunch theorems$92586334 997 $aUNISA LEADER 03256nam 2200661 a 450 001 9910220102503321 005 20200520144314.0 010 $a1-282-45105-7 010 $a9786612451058 010 $a0-8330-4862-7 035 $a(CKB)2550000000005576 035 $a(EBL)475080 035 $a(OCoLC)276956206 035 $a(SSID)ssj0000343686 035 $a(PQKBManifestationID)12135967 035 $a(PQKBTitleCode)TC0000343686 035 $a(PQKBWorkID)10291716 035 $a(PQKB)11348322 035 $a(MiAaPQ)EBC475080 035 $a(oapen)doab114837 035 $a(EXLCZ)992550000000005576 100 $a20080611d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aThinking about America's defense $ean analytical memoir /$fGlenn A. Kent ; with David Ochmanek, Michael Spirtas, Bruce R. Pirnie 210 $aSanta Monica, CA $cRand Corp.$d2008 215 $a1 online resource (283 p.) 300 $aDescription based upon print version of record. 311 08$a0-8330-4452-4 320 $aIncludes bibliographical references. 327 $aCover; Preface; Contents; Figures; Tables; Boxes; Acknowledgments; Abbreviations; Foreword - Creating Strategic Analysis: Thomas C. Schelling; Foreword - Putting Analysis to Work: Harold Brown; Introduction: David Ochmanek, Bruce Pirnie, and Michael Spirtas; Chapter One - The Single Integrated Operational Plan; Chapter Two - Nuclear Weapons: Strategy and Arms Control; Chapter Three - Analysis, Force Planning, and the Paradigm forModernizing; Chapter Four - Modernizing Nuclear Forces; Chapter Five - Modernizing Conventional Forces; Chapter Six - Analytical Tools 327 $aChapter Seven - Summing Up: Kent's MaximsChronology; Awards; Bibliography 330 $aLieutenant General Glenn A. Kent was a uniquely acute analyst and developer of American defense policy in the second half of the twentieth century. His 33-year career in the Air Force was followed by more than 20 years as one of the leading analysts at RAND. This volume is not a memoir in the normal sense but rather a summary of the dozens of national security issues in which Glenn was personally engaged over the course of his career. These issues included creating the single integrated operational plan (SIOP),leading DoD's official assessment of strategic defenses in the 1960s,developing and analyz 606 $aMilitary planning$zUnited States 606 $aGenerals$zUnited States$vBiography 606 $aStrategic forces$zUnited States 606 $aNational security$zUnited States 607 $aUnited States$xDefenses$xDecision making 607 $aUnited States$xMilitary policy 615 0$aMilitary planning 615 0$aGenerals 615 0$aStrategic forces 615 0$aNational security 676 $a355/.033073 700 $aKent$b Glenn A.$f1915-$01236034 701 $aOchmanek$b David A$0869935 701 $aSpirtas$b Michael$01236035 701 $aPirnie$b Bruce$f1940-$01047835 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910220102503321 996 $aThinking about America's defense$92870402 997 $aUNINA