Learning and Intelligent Optimization [[electronic resource] ] : 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011, Selected Papers / / edited by Carlos A. Coello-Coello

• Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011
• Edizione: 1st ed. 2011.
• Descrizione fisica: 1 online resource (XVII, 636 p.)
• Disciplina: 006.33
• Soggetto topico: Algorithms; Computer science; Artificial intelligence; Computer networks; Pattern recognition systems; Computer science—Mathematics; Discrete mathematics; Theory of Computation; Artificial Intelligence; Computer Communication Networks; Automated Pattern Recognition; Discrete Mathematics in Computer Science
• Persona (resp. second.): Coello-CoelloCarlos A
• Note generali: Includes index.
• Nota di contenuto: Intro -- Title -- Preface -- Organization -- Table of Contents -- Main Track (Regular Papers) -- Multivariate Statistical Tests for Comparing Classification Algorithms -- Introduction -- Pairwise Comparison -- Univariate Case -- Multivariate Case -- Analysis of Variance -- Univariate Case -- Multivariate Case -- Experiments -- Setup -- Results -- Conclusions -- References -- Using Hyperheuristics under a GP Framework for Financial Forecasting -- Introduction -- Presentation of EDDIE 8 -- Hyperheuristics Framework -- Heuristics and Operators -- The Framework -- Experimental Setup -- Results -- Conclusion -- References -- On the Effect of Connectedness for Biobjective Multiple and Long Path Problems -- Introduction -- Background -- Multiobjective Combinatorial Optimization -- Local Search and Connectedness -- The Single-Objective Long k-Path Problem -- The Biobjective Long k-Path Problem -- Definition -- Experimental Analysis -- The Biobjective Multiple k-Path Problem -- Definition -- Experimental Analysis -- Conclusions and Future Works -- References -- Improving Parallel Local Search for SAT -- Introduction -- Background -- The Propositional Satisfiability Problem -- Local Search for SAT -- Refinements -- Previous Work -- Complete Methods for Parallel SAT -- Incomplete Methods for Parallel SAT -- Cooperative Algorithms -- Knowledge Sharing in Parallel Local Search for SAT -- Using Best Known Configurations -- Weighting Best Known Configurations -- Restart Policy -- Experiments -- Experimental Settings -- Practical Performances with 4 Cores -- Practical Performances with 8 Cores -- Hardware Impact -- Conclusions and Future Work -- References -- Variable Neighborhood Search for the Time-Dependent Vehicle Routing Problem with Soft Time Windows -- Introduction -- Problem Description -- Solution Method -- Initial Solution -- Shaking. ; Local Search -- Acceptance Decision -- Computational Results -- Conclusion -- References -- Solving the Two-Dimensional Bin Packing Problem with a Probabilistic Multi-start Heuristic -- Introduction -- Organization of the Paper -- Related Work -- A New ILP Model -- The Proposed Algorithm -- Probabilistic LGFi -- Multi-start Algorithm -- Experimental Evaluation -- Problem Instances -- Parameter Setting -- Computational Results -- Conclusions -- References -- Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization -- Introduction -- Analysis of Pareto Optimal Solutions in Many-objective 0/1 Knapsack Problem -- Mating Based on Proximity in Objective Space -- Related Works -- Local Recombination -- Controlling Crossed Genes for Crossover -- Problem of Local Recombination in MaOPs -- CCG for Two-Point Crossover (CCGTX) -- CCG for Uniform Crossover (CCGUX) -- Preparation -- Algorithms and Selection Methods -- Problems, Parameters and Metrics -- Experimental Results and Discussion -- Diversity of Genes in the Population Obtained by Conventional Crossover -- Effects of Local Recombination in MaOPs -- Effects of CCGTX in MaOPs -- Effects of CCGUX in MaOPs -- Conclusions -- References -- An Optimal Stopping Strategy for Online Calibration in Local Search -- Introduction -- The Bruss Algorithm -- The Estimation of the Probability of Success in Local Exploration -- Illustration of the Approach -- Experiments -- Conclusion and Future Work -- References -- Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes -- Introduction -- Background -- Multiobjective Combinatorial Optimization -- Metaheuristics for Multiobjective Combinatorial Optimization -- NK- and MNK-Landscapes -- MNK-Landscapes: Multiobjective NK-Landscapes with Correlation -- Definition -- Correlation between Objective Functions. ; Analysis of the Efficient Set Properties -- Cardinality of the Efficient Set -- Number of Supported Efficient Solutions -- Connectedness of the Efficient Set -- Discussion -- References -- Instance-Based Parameter Tuning via Search Trajectory Similarity Clustering -- Introduction -- Preliminaries -- Automated Parameter Configuration Problem -- One-Size-Fits-All Configurator -- Instance-Based Configurator -- Performance Metric -- Solution Approach -- Search Trajectory Similarity -- Search Trajectory Representation -- Similarity Calculation -- Clustering Method -- Training and Testing Phases -- Experimental Design -- Experiment Settings -- Validity and Statistical Significant Measurement -- Experimental Setup -- Empirical Evaluation -- Performance Comparison -- Comparison on Feature Selection -- Sensitivity Analysis on Different Initial Sequence Configurations -- Computational Results -- Discussion -- Conclusion and Future Works -- References -- Effective Probabilistic Stopping Rules for Randomized Metaheuristics: GRASP Implementations -- Introduction and Motivation -- GRASP and Experimental Environment -- Normal Approximation for GRASP Iterations -- Probabilistic Stopping Rule -- Concluding Remarks -- References -- A Classifier-Assisted Framework for Expensive Optimization Problems: A Knowledge-Mining Approach -- Introduction -- Background -- Expensive Optimization Problems -- Simulator Infeasible Vectors -- Proposed Framework -- The Model -- The Classifier -- The Framework -- Performance Analysis -- Test Problem and Benchmarks -- Knowledge-Mining the Classifier -- Summary -- References -- Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion -- Introduction -- Efficient Global Optimization -- The Expected Improvement Sampling Criterion for a Gaussian Process. ; Classical Parametrized Covariance Functions -- The EGO Algorithm -- The Case of Deceptive Functions -- Fully Bayesian One-Step Lookahead Optimization -- Student EI -- Numerical Experiments -- Optimization of a Deceptive Function -- Comparison on Sample Paths of a Gaussian Process -- References -- Hierarchical Hidden Conditional Random Fields for Information Extraction -- Introduction -- Hierarchical Hidden Conditional Random Fields -- Information Extraction -- Paper Organization -- HHMMs -- Representing an HHMM as a DBN -- HHCRFs -- Model -- Parameter Estimation -- Sentence Representation -- Hierarchical Models for Information Extraction -- Upper and Lower Levels -- Model Learning -- Inference -- Experiments -- Data -- Retrieved Results -- Performance Evaluation -- Results -- Conclusion -- References -- Solving Extremely Difficult MINLP Problems Using Adaptive Resolution Micro-GA with Tabu Search -- Introduction -- Related Work -- GAs for Solving MINLP Problems -- The Proposed Algorithm -- Variables Encoding and Genetic Operators -- Constraint Handling -- Micro GA -- Adaptive Resolution Approach -- Local Search -- Avoiding Redundancy -- Results -- Environment and Parameters -- Results and Discussion -- Conclusions and Future Work -- References -- Adaptive Abnormality Detection on ECG Signal by Utilizing FLAC Features -- Introduction -- Architecture of the Proposed Framework -- Preprocessing in Frequency Domain -- Local Auto-correlation on Complex Fourier Values (FLAC) for ECG -- Complex Subspace Method -- Experiments -- Conclusions -- References -- Gravitational Interactions Optimization -- Introduction -- Review GSA GIO and CSS -- Newton's Law of Universal Gravitation -- Gravitational Interactions Optimization -- Gravitational Interactions for Unimodal Optimization -- Gravitational Interactions for Multimodal Optimization -- Experiments. ; Test Functions -- Results -- Conclusions -- References -- On the Neutrality of Flowshop Scheduling Fitness Landscapes -- Motivations -- Background -- Definition of the Permutation Flowshop Scheduling Problem -- Neighborhood and Local Search -- Fitness Landscape -- Neutral Networks Analysis for the Permutation Flowshop Scheduling Problem -- Experimental Design -- Neutral Degree -- Typology of Neutral Networks -- Exploiting Neutrality to Solve the FSP -- Reaching Portals -- How to Guide the Search? -- Discussion -- References -- A Reinforcement Learning Approach for the Flexible Job Shop Scheduling Problem -- Introduction -- Flexible Job Shop Scheduling Problem -- Problem Formulation -- Previous Approaches -- Dispatching Rules -- Reinforcement Learning -- Q-Learning -- The Proposed Approach: Learning / Optimization -- Pseudo-code of the Algorithm -- Example -- Experimental Results -- Instances -- Parameters -- Comparative Study -- Conclusions and Future Work -- References -- Supervised Learning Linear Priority Dispatch Rules for Job-Shop Scheduling -- Introduction -- Priority Dispatch Rules for Job-Shop Scheduling -- Logistic Regression -- Experimental Study -- Data Generation -- Training Size and Accuracy -- Comparison with Single Priority Dispatching Rules -- Robustness towards Data Distributions -- Fixed Weights -- Summary and Conclusion -- References -- Fine-Tuning Algorithm Parameters Using the Design of Experiments Approach -- Introduction -- Automated Tuning Framework -- Screening Phase -- Exploration Phase -- Exploitation Phase -- Experimental Results -- Traveling Salesman Problem (TSP) -- Quadratic Assignment Problem (QAP) -- Conclusion -- References -- MetaHybrid: Combining Metamodels and Gradient-Based Techniques in a Hybrid Multi-Objective Genetic Algorithm -- GA Elements: Focus on Elitism -- SQP Elements: Focus on Constraints. ; Metamodels Derivatives.
• Sommario/riassunto: This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Learning and Intelligent Optimization, LION 5, held in Rome, Italy, in January 2011. The 32 revised regular and 3 revised short papers were carefully reviewed and selected from a total of 99 submissions. In addition to the contributions to the general track there are 11 full papers and 3 short papers presented at the following four special sessions; IMON: Intelligent Multiobjective OptimizatioN, LION-PP: Performance Prediction Self* EAs: Self-tuning, self-configuring and self-generating evolutionary algorithms LION-SWAP: Software and Applications.
• Titolo autorizzato: Learning and Intelligent Optimization
• ISBN: 3-642-25566-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996465924003316
• Lo trovi qui: Univ. di Salerno
• Serie: Theoretical Computer Science and General Issues, . 2512-2029 ; ; 6683