Learning and Intelligent Optimization [[electronic resource] ] : 11th International Conference, LION 11, Nizhny Novgorod, Russia, June 19-21, 2017, Revised Selected Papers / / edited by Roberto Battiti, Dmitri E. Kvasov, Yaroslav D. Sergeyev

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
• Edizione: 1st ed. 2017.
• Descrizione fisica: 1 online resource (XIII, 390 p. 92 illus.)
• Disciplina: 006.31
• Soggetto topico: Algorithms; Computer science; Artificial intelligence; Numerical analysis; Computer simulation; Computer Science Logic and Foundations of Programming; Artificial Intelligence; Numerical Analysis; Theory of Computation; Computer Modelling
• Persona (resp. second.): BattitiRoberto; KvasovDmitri E; SergeyevYaroslav D
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Long Papers -- An Importance Sampling Approach to the Estimation of Algorithm Performance in Automated Algorithm Design -- 1 Introduction -- 2 The Algorithm Design Problem (ADP) -- 3 Performance Estimation in PbO -- 3.1 Prior Art -- 3.2 An Importance Sampling Approach -- 4 Envisioned Benefits -- 5 Theoretical Feasibility -- 6 The Proof of Concept -- 6.1 Practical Challenges -- 6.2 High-Level Search Strategy -- 7 Experiments -- 7.1 Experimental Setup -- 7.2 Results and Discussion -- 8 Conclusion -- References -- Test Problems for Parallel Algorithms of Constrained Global Optimization -- 1 Introduction -- 2 Problem Statement -- 3 Generating a Series of Problems -- 4 Parallel Global Optimization Index Algorithm -- 5 Results of Numerical Experiments -- 6 Conclusion -- References -- Automatic Configuration of Kernel-Based Clustering: An Optimization Approach -- Abstract -- 1 Introduction -- 2 Material and Methods -- 2.1 Notation -- 2.2 The Case Study and the Data Generation Process -- 2.3 Kernel K-means -- 3 Hyperparameter Optimization of the Unsupervised Learning Phase of the Machine Learning Pipeline -- 3.1 Hyperparameters in the Pipeline: The Design Variables -- 3.2 Clustering Performance: The Objective Function -- 3.3 Sequential Model Based Optimization -- 3.3.1 Building the Surrogate of the Objective Function: Gaussian Processes and Random Forest -- 3.3.2 Acquisition Function: Confidence Bound -- 3.3.3 Termination Criterion -- 3.3.4 Software Environment -- 4 Results and Discussion -- 5 Conclusions -- References -- Solution of the Convergecast Scheduling Problem on a Square Unit Grid When the Transmission Range is 2 -- 1 Introduction -- 2 General Problem Formulation -- 3 CSP in the Unit Square Grid When the Transmission Distance is 2 -- 3.1 The Exact Lower Bound for the Schedule Length. ; 3.2 Algorithm A -- 4 Conclusion -- References -- A GRASP for the Minimum Cost SAT Problem -- 1 Introduction -- 2 Mathematical Formulation of the Problem -- 3 A GRASP for MinCostSAT -- 4 Probabilistic Stopping Rule -- 4.1 Fitting Data Procedure -- 4.2 Improve Probability Procedure -- 5 Results -- 6 Conclusions -- References -- A New Local Search for the p-Center Problem Based on the Critical Vertex Concept -- 1 Introduction -- 2 GRASP Construction Phase -- 3 Plateau Surfer: A New Local Search Based on the Critical Vertex Concept -- 4 Experimental Results -- 5 Concluding Remarks -- References -- An Iterated Local Search Framework with Adaptive Operator Selection for Nurse Rostering -- 1 Introduction -- 2 The Nurse Rostering Problem -- 3 The Proposed Approach -- 3.1 Credit Assignment Module -- 3.2 Action Selection Methodology -- 4 Experimental Results -- 4.1 Experimental Setup -- 4.2 Experimental Results and Analysis -- 5 Conclusions -- References -- Learning a Reactive Restart Strategy to Improve Stochastic Search -- 1 Introduction -- 2 Restart Strategies -- 3 Learning Dynamic Parameter Updates -- 4 A Hyper-Parameterized Restart Strategy -- 4.1 Features -- 4.2 Turning Features into Scores -- 4.3 The Reactive Restart Framework -- 5 Experimental Analysis -- 5.1 Problems and Benchmarks -- 5.2 Data Collection -- 5.3 Training of Hyper -- 5.4 Results -- 6 Conclusion -- References -- Efficient Adaptive Implementation of the Serial Schedule Generation Scheme Using Preprocessing and Bloom Filters -- 1 Introduction -- 2 SSGS Implementation Details -- 2.1 Initialisation of A -- 2.2 Efficient Search of the Earliest Feasible Slot for a Job -- 2.3 Preprocessing and Automated Parameter Control -- 3 SSGS Implementation Using Bloom Filters -- 3.1 Optimisation of Bloom Filter Structure -- 3.2 Additional Speed-ups -- 4 Hybrid Control Mechanism -- 5 Empirical Evaluation. ; 6 Conclusions and Future Work -- References -- Interior Point and Newton Methods in Solving High Dimensional Flow Distribution Problems for Pipe Networks -- 1 Introduction -- 2 Problem Statement -- 3 Newton Method -- 4 Interior Point Method -- 5 Matrices Multiplication in Interior Point Method -- 6 Acceleration by Constant Multiplication -- 7 Combined Method for Constrained Problem -- 8 Numerical Results -- 9 Conclusion -- References -- Hierarchical Clustering and Multilevel Refinement for the Bike-Sharing Station Planning Problem -- 1 Introduction -- 2 Related Work -- 3 Problem Formalization -- 3.1 Solution Representation -- 3.2 Objective -- 3.3 Calculation of Fulfilled Customer Demand -- 3.4 Calculation of Rebalancing Costs -- 4 Multilevel Refinement Approach -- 4.1 Coarsening -- 4.2 Initialization -- 4.3 Extension -- 5 Computational Results -- 6 Conclusion and Future Work -- References -- Decomposition Descent Method for Limit Optimization Problems -- 1 Introduction -- 2 Auxiliary Problem Properties -- 3 Limit Decomposition Method and its Convergence -- 4 Modifications and Applications -- 5 Computational Experiments -- 6 Conclusions -- References -- RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization -- 1 Introduction -- 2 Model-Based Global Optimization -- 2.1 Parallel MBO -- 3 Resource-Aware Scheduling with Synchronous Model Update -- 3.1 Infill Criterion - Priority -- 3.2 Resource Estimation -- 3.3 Resource-Aware Knapsack Scheduling -- 3.4 Refinement of Job Priorities via Clustering -- 4 Numerical Experiments -- 4.1 Quality of Resource Estimation -- 4.2 High Runtime Estimation Quality: rosenbrock -- 4.3 Low Runtime Estimation Quality: rastrigin -- 5 Conclusion -- References -- A New Constructive Heuristic for the No-Wait Flowshop Scheduling Problem. ; 1 Introduction -- 2 The No-Wait Flowshop Scheduling Problem -- 2.1 Description of the Problem -- 2.2 State-of-the-Art -- 3 IBI: Iterated Best Insertion Heuristic -- 3.1 Analysis of Optimal Solutions Structure -- 3.2 Design of IBI -- 3.3 Experimental Analysis of Parameters -- 4 Experiments -- 4.1 Efficiency of IBI -- 4.2 IBI as Initialization of a Local Search -- 5 Conclusion and Perspectives -- References -- Sharp Penalty Mappings for Variational Inequality Problems -- 1 Introduction -- 2 Notations and Preliminaries -- 3 Sharp Penalty Mappings -- 4 Iteration Algorithm -- 5 Conclusions -- References -- A Nonconvex Optimization Approach to Quadratic Bilevel Problems -- 1 Introduction -- 2 Statement of the Problem and Its Reduction -- 3 The Local Search -- 4 Global Optimality Conditions and the Global Search Procedure -- 5 Computational Simulation -- 6 Conclusion -- References -- An Experimental Study of Adaptive Capping in irace -- 1 Introduction -- 2 Elitist Iterated Racing in irace -- 3 ParamILS and Adaptive Capping -- 4 Adaptive Capping in irace -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 5.3 Additional Analysis of iracecap -- 6 Comparison to Other Configurators -- 7 Conclusions -- References -- Duality Gap Analysis of Weak Relaxed Greedy Algorithms -- 1 Introduction -- 2 Weak Relaxed Greedy Algorithms -- 3 Dual Convergence Results -- 3.1 Duality Gap -- 3.2 Dual Convergence Result for WRGA(co) -- 3.3 Dual Convergence Result for WRGA() -- 4 Conclusion -- References -- Controlling Some Statistical Properties of Business Rules Programs -- 1 Introduction -- 1.1 Preliminaries -- 1.2 Related Works -- 2 Learning Goals with Histograms -- 2.1 A MIP for Learning Quantized Distributions -- 2.2 A MILP for the Max Percentage Problem -- 2.3 A MILP for the Almost Uniform Distribution Problem -- 3 Implementation and Experiments. ; 3.1 The Max Percentage Problem -- 3.2 The Almost Uniform Distribution Problem -- 4 Conclusion, Discussion and Future Work -- References -- GENOPT Paper -- Hybridization and Discretization Techniques to Speed Up Genetic Algorithm and Solve GENOPT Problems -- Abstract -- 1 Introduction -- 2 Preliminary Concepts -- 3 The GABRLS Algorithm -- 3.1 The Modified GA -- 3.2 Bounding Restart (BR) Technique -- 3.3 Hybridizing GABR with Local Searches -- 4 Tuning and Results of GABRLS on GENOPT Challenge -- 4.1 High Level Setting -- 4.2 Results and Prizes -- 5 Conclusion -- References -- Short Papers -- Identification of Discontinuous Thermal Conductivity Coefficient Using Fast Automatic Differentiation -- Abstract -- 1 Introduction -- 2 Formulation of the Problem -- 3 Numerical Solution of the Problem -- Acknowledgments -- References -- Comparing Two Approaches for Solving Constrained Global Optimization Problems -- 1 Introduction -- 2 Index Method -- 3 Results of Experiments -- 4 Conclusion -- References -- Towards a Universal Modeller of Chaotic Systems -- 1 Introduction -- 2 Previous Work -- 3 Learning Algorithm -- 3.1 Idle Mode -- 4 Experimental Setup -- 4.1 Repetition -- 4.2 Fractal Dimension -- 4.3 Lyapunov Exponent -- 5 Results -- 6 Conclusion -- References -- An Approach for Generating Test Problems of Constrained Global Optimization -- 1 Introduction -- 2 Test Problem Classes -- 3 Some Numerical Results -- 4 Conclusion -- References -- Global Optimization Using Numerical Approximations of Derivatives -- 1 Introduction -- 2 One-Dimensional Global Optimization Algorithm Using Numerical Estimations of Derivatives -- 2.1 Core One-Dimensional Global Search Algorithm Using Derivatives -- 2.2 One-Dimensional Global Search Algorithm Using Numerical Derivatives -- 3 Results of Computational Experiments -- 4 Conclusion -- References. ; Global Optimization Challenges in Structured Low Rank Approximation.
• Sommario/riassunto: This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Conference on Learning and Intelligent Optimization, LION 11, held in Nizhny,Novgorod, Russia, in June 2017. The 20 full papers (among these one GENOPT paper) and 15 short papers presented have been carefully reviewed and selected from 73 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.
• Titolo autorizzato: Learning and Intelligent Optimization
• ISBN: 3-319-69404-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996465339903316
• Lo trovi qui: Univ. di Salerno
• Serie: Theoretical Computer Science and General Issues, . 2512-2029 ; ; 10556