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The ACM journal of experimental algorithmics
| The ACM journal of experimental algorithmics |
| Pubbl/distr/stampa | New York, : ACM |
| Disciplina | 004 |
| Soggetto topico |
Computer algorithms
Data structures (Computer science) Algorithms Algorismes Algorismes computacionals Estructures de dades (Informàtica) |
| Soggetto genere / forma |
Periodicals.
Revistes electròniques. |
| Formato | Materiale a stampa  |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Journal of experimental algorithmics
JEA ACM JEA Association for Computing Machinery journal of experimental algorithmics |
| Record Nr. | UNINA-9910376057303321 |
| New York, : ACM | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithmic Aspects of Cloud Computing : 7th International Symposium, ALGOCLOUD 2022, Potsdam, Germany, September 6, 2022, Revised Selected Papers / / edited by Luca Foschini, Spyros Kontogiannis
| Algorithmic Aspects of Cloud Computing : 7th International Symposium, ALGOCLOUD 2022, Potsdam, Germany, September 6, 2022, Revised Selected Papers / / edited by Luca Foschini, Spyros Kontogiannis |
| Autore | Foschini Luca |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (111 pages) |
| Disciplina | 004.0151 |
| Altri autori (Persone) | KontogiannisSpyros |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer science
Computer networks Computers, Special purpose Data structures (Computer science) Information theory Application software Computer systems Theory of Computation Computer Communication Networks Special Purpose and Application-Based Systems Data Structures and Information Theory Computer and Information Systems Applications Computer System Implementation Computació en núvol Algorismes computacionals Matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031334375
303133437X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cloud-Based Urban Mobility Services -- SQL Query Optimization in Distributed NoSQL Databases for Cloud-based Applications -- MAGMA: Proposing a Massive Historical Graph Management System -- New Results in Priority-Based Bin Packing -- More Sparking Soundex-based Privacy-Preserving Record Linkage -- Privacy Preserving Queries of Shortest Path Distances. |
| Record Nr. | UNINA-9910728390403321 |
Foschini Luca
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithmic foundations of robotics XIV : proceedings of the Fourteenth Workshop on the Algorithmic Foundations of Robotics / / editors, Steven M. LaValle [et al.]
| Algorithmic foundations of robotics XIV : proceedings of the Fourteenth Workshop on the Algorithmic Foundations of Robotics / / editors, Steven M. LaValle [et al.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (581 pages) |
| Disciplina | 629.892 |
| Collana | Springer Proceedings in Advanced Robotics |
| Soggetto topico |
Robotics
Robotics - Mathematics Machine learning Algorithms Robòtica Algorismes computacionals Aprenentatge automàtic |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-66723-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483482303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithms with JULIA : optimization, machine learning, and differential Equations using the JULIA language / / Clemens Heitzinger
| Algorithms with JULIA : optimization, machine learning, and differential Equations using the JULIA language / / Clemens Heitzinger |
| Autore | Heitzinger Clemens |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2023] |
| Descrizione fisica | 1 online resource (447 pages) |
| Disciplina | 005.1 |
| Soggetto topico |
Computer algorithms
Julia (Computer program language) Algorismes computacionals |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031165603
9783031165597 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910634042203321 |
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Heitzinger Clemens
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| Cham, Switzerland : , : Springer International Publishing, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithms with JULIA : optimization, machine learning, and differential Equations using the JULIA language / / Clemens Heitzinger
| Algorithms with JULIA : optimization, machine learning, and differential Equations using the JULIA language / / Clemens Heitzinger |
| Autore | Heitzinger Clemens |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2023] |
| Descrizione fisica | 1 online resource (447 pages) |
| Disciplina | 005.1 |
| Soggetto topico |
Computer algorithms
Julia (Computer program language) Algorismes computacionals |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031165603
9783031165597 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996503551003316 |
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Heitzinger Clemens
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| Cham, Switzerland : , : Springer International Publishing, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Ant colony optimization and applications / / Stefka Fidanova
| Ant colony optimization and applications / / Stefka Fidanova |
| Autore | Fidanova Stefka <1964-> |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (135 pages) : illustrations |
| Disciplina | 519.3 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Algorismes computacionals Investigació operativa Intel·ligència computacional |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-67380-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484772503321 |
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Fidanova Stefka <1964->
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applications of advanced computing in systems : proceedings of International Conference on Advances in Systems, Control and Computing / / Rajesh Kumar, [and three others], editors
| Applications of advanced computing in systems : proceedings of International Conference on Advances in Systems, Control and Computing / / Rajesh Kumar, [and three others], editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (334 pages) |
| Disciplina | 006.3 |
| Collana | Algorithms for intelligent systems |
| Soggetto topico |
Computer algorithms
Machine learning Artificial intelligence Algorismes computacionals Aprenentatge automàtic Intel·ligència artificial |
| Soggetto genere / forma |
Llibres electrònics
Congressos |
| ISBN | 981-334-862-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | AISCC 2020 |
| Record Nr. | UNINA-9910736018503321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applications of flower pollination algorithm and its variants / / editor, Nilanjan Dey
| Applications of flower pollination algorithm and its variants / / editor, Nilanjan Dey |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (247 pages) : illustrations |
| Disciplina | 518.1 |
| Collana | Springer Tracts in Nature-Inspired Computing |
| Soggetto topico |
Algorithms
Swarm intelligence Computational intelligence Algorismes computacionals Intel·ligència computacional |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-336-104-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editor and Contributors -- 1 Flower Pollination Algorithm: Basic Concepts, Variants, and Applications -- 1 Introduction -- 2 Biological Inspirations: Pollination of Flowering Plants -- 3 Flower Pollination Optimization Algorithm (FPA) -- 3.1 Global Search in FPA: Biotic Pollination Process -- 3.2 Local Search in FPA: Abiotic Pollination Process -- 3.3 Switch Probability in FPA -- 3.4 Parametric Study for FPA -- 3.5 Implementation of FPA -- 3.6 Advantages of FPA -- 4 Variants of FPA -- 4.1 Multi-objective Flower Pollination Algorithm (MOFPA) -- 4.2 Modified Flower Pollination Algorithms (M-FPA) -- 4.3 Hybridized Variants of FPA -- 5 Applications of FPA and Its Variants -- 6 Comparative Analytical Studies of FPA and its Variants -- 7 Limitations of FPA -- 8 Challenging Problems in FPA -- 9 Conclusions -- References -- 2 Optimization of Non-rigid Demons Registration Using Flower Pollination Algorithm -- 1 Introduction -- 2 Methodology -- 2.1 Demons Registration -- 2.2 Flower Pollination Algorithm -- 3 Proposed Method -- 4 Results and Discussion -- 5 Conclusion -- References -- 3 Adaptive Neighbor Heuristics Flower Pollination Algorithm Strategy for Sequence Test Generation -- 1 Introduction -- 2 T-way Tests Generation Problem -- 2.1 T-way Tests Generation -- 2.2 Sequence t-way Tests Generation -- 3 Related Works -- 4 Adaptive Neighbor Heuristics Flower Pollination Algorithm Strategy -- 5 Experimental Results -- 5.1 Benchmarking with Existing Strategies -- 5.2 Convergence Rate Analysis -- 6 Summary -- References -- 4 Implementation of Flower Pollination Algorithm to the Design Optimization of Planar Antennas -- 1 Introduction -- 2 Flower Pollination Algorithm -- 2.1 Pollination Phenomenon -- 2.2 Modeling of Flower Pollination Algorithm -- 3 The Cooperating Platform for Simulation and Optimization of the Antenna Designs.
3.1 The Cooperating Platform -- 3.2 S-parameters -- 3.3 Cooperation of FPA and the Simulator -- 4 The Optimized Designs of Planar Antennas -- 4.1 UWB Antenna Design -- 4.2 Dual BN Characteristic Optimization of the UWB Antenna -- 4.3 Single UWB Antenna Element for a Quad-Element MIMO Antenna -- 4.4 Quad-Element MIMO Antenna -- 5 Conclusions -- References -- 5 Flower Pollination Algorithm for Slope Stability Analysis -- 1 Introduction -- 2 Problem Statement -- 2.1 Generation of Trial Slip Surface -- 2.2 Calculation of Factor of Safety -- 2.3 Application of Optimization Method -- 3 Flower Pollination Algorithm -- 4 Numerical Analysis -- 4.1 Sensitivity Analysis -- 4.2 Case-1 -- 4.3 Case-2 -- 4.4 Case-3 -- 5 Discussion and Conclusions -- References -- 6 Optimum Sizing of Truss Structures Using a Hybrid Flower Pollinations -- 1 Introduction -- 2 Sizing Optimization Problem -- 3 Optimization Algorithms -- 3.1 Flower Pollination Algorithm -- 3.2 Differential Evolution -- 3.3 Hybrid Flower Pollination-Differential Evolution -- 4 Numerical Experiments and Results -- 5 10-Bar Planar Truss -- 5.1 17-Bar Planar Truss -- 5.2 45-Bar Planar Truss -- 6 Conclusion -- References -- 7 Optimizing Reinforced Cantilever Retaining Walls Under Dynamic Loading Using Improved Flower Pollination Algorithm -- 1 Introduction -- 2 Design Steps of Reinforced Retaining Walls -- 2.1 Geometrical Design Variables -- 2.2 Geotechnical Stability of Reinforced Cantilever Retaining Walls -- 2.3 Structural Constraints for Reinforced Cantilever Retaining Walls -- 3 Optimum Design of Reinforced Concrete Cantilever Retaining Walls -- 3.1 Objective Function -- 4 Optimization Algorithms -- 4.1 Flower Pollination Algorithm -- 4.2 Improved Flower Pollination Algorithm -- 5 Numerical Experiments -- 5.1 Example 1 -- 5.2 Example 2 -- 6 Conclusions -- References. 8 Multi-objective Flower Pollination Algorithm and Its Variants to Find Optimal Golomb Rulers for WDM Systems -- 1 Introduction -- 2 Optimal Golomb Rulers (OGRs) -- 3 Multi-objective Flower Pollination Algorithm and Its Variants -- 3.1 Multi-objective Flower Pollination Algorithm -- 3.2 Variants of Multi-objective Flower Pollination Algorithm -- 4 Problem Formulation -- 5 Results and Discussion -- 5.1 Comparative Study of Flower Pollination-Inspired MOAs in Terms of the Ruler Length and Total Occupied Unequally Spaced Optical Channel Bandwidth -- 5.2 Comparative Study of Flower Pollination-Inspired MOAs in Terms of BEF -- 5.3 Comparative Study of Flower Pollination-Inspired MOAs in Terms of Computational CPU Time -- 5.4 Maximum Computation Complexity of Flower Pollination-Inspired MOAs in Terms of Big O Notation -- 5.5 Wilcoxon Rank-Sum Test of Flower Pollination-Inspired MOAs -- 6 Conclusions -- References -- 9 Applications of Flower Pollination Algorithm in Wireless Sensor Networking and Image Processing: A Detailed Study -- 1 Introduction -- 2 Swarm Intelligence Algorithm -- 2.1 Bat Algorithm -- 2.2 Firefly Algorithm -- 2.3 Particle Swarm Optimization -- 2.4 Artificial Bee Colony Algorithm -- 2.5 Cuckoo Search Algorithm -- 3 Flower Pollination Algorithm -- 3.1 Flower Pollination -- 3.2 The Flower Pollination Algorithm (FPA) -- 3.3 Variants of Flower Pollination Algorithm (FPA) -- 4 Wireless Sensor Networks -- 4.1 Impact of Flower Pollination Algorithm in Wireless Sensor Networking -- 5 Image Processing -- 5.1 Impact of Flower Pollination Algorithm in Image Processing -- 6 Discussion -- 7 Conclusion -- References -- 10 Flower Pollination Algorithm Tuned PID Controller for Multi-source Interconnected Multi-area Power System -- 1 Introduction -- 2 Power System Investigation -- 2.1 Proportional Integral Derivative (PID) Controller. 3 Flower Pollination Algorithm Tuned PID Controller -- 4 Result Analysis and Discussions -- 5 Conclusions -- References. |
| Record Nr. | UNINA-9910767581903321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors
| Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (393 pages) |
| Disciplina | 006.31 |
| Collana | Springer Optimization and Its Applications |
| Soggetto topico |
Machine learning - Mathematics
Aprenentatge automàtic Optimització matemàtica Algorismes computacionals |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-66515-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNISA-996466410203316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors
| Black box optimization, machine learning, and no-free lunch theorems / / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (393 pages) |
| Disciplina | 006.31 |
| Collana | Springer Optimization and Its Applications |
| Soggetto topico |
Machine learning - Mathematics
Aprenentatge automàtic Optimització matemàtica Algorismes computacionals |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-66515-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910483695503321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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