Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors

• Record Nr.: UNINA9910483695503321
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-66515-1
• Descrizione fisica: 1 online resource (393 pages)
• Collana: Springer Optimization and Its Applications ; ; v.170
• Disciplina: 006.31
• Soggetti: Machine learning - Mathematics; Aprenentatge automàtic; Optimització matemàtica; Algorismes computacionals; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- 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.; 2.5 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.; Multi-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.; 7 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,δ,α,r -- 3.2 Refined Approximation for PU,δ,α,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,δ,0,r.; 4.2 Refined Approximation for PU,δ,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 δ -- 6.2 Comparison Across α -- 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 δ-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 δ-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.; 6.4 Solving Hard SAT Instances via Found Partitionings.

The devil behind the mirror : globalization and politics in the Dominican Republic / / Steven Gregory ; with a new preface

• Record Nr.: UNINA9910787897403321
• Autore: Gregory Steven <1954->
• Pubbl/distr/stampa: Berkeley : , : University of California Press, , [2014]; ©2014
• ISBN: 0-520-95789-X
• Descrizione fisica: 1 online resource (311 p.)
• Disciplina: 330.97293
• Soggetti: Globalization - Dominican Republic; Globalization - Political aspects - Dominican Republic; Tourism - Dominican Republic; Dominican Republic Foreign economic relations; Dominican Republic Economic conditions 1961-; Dominican Republic Social conditions 1961-
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: The politics of livelihood -- The spatial economy of difference -- Structures of the imagination -- Sex, tourism and the political economy of masculinity -- Race, identity, and the body politic -- The politics of transnational capital.
• Sommario/riassunto: In The Devil behind the Mirror, Steven Gregory provides a compelling and intimate account of the impact that transnational processes associated with globalization are having on the lives and livelihoods of people in the Dominican Republic. Grounded in ethnographic fieldwork ; conducted in the adjacent towns of Boca Chica and Andrés, Gregory's study deftly demonstrates how transnational flows of capital, culture, and people are mediated by contextually specific power relations, politics, and history. He explores such topics as the informal economy, the making of a telenova, sex tourism, and racism and discrimination against Haitians, who occupy the lowest rung on the Dominican economic ladder. Innovative, beautifully written, and now updated with a new preface, The Devil behind the Mirror masterfully situates the analysis of global economic change in everyday lives.