Note: Intro -- Preface -- Contents -- Part I Function Approximation -- Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap -- Automatic Approximation of Expensive Functions with Active Learning -- New Multi-Objective Algorithms for Neural Network Training Applied to Genomic Classification Data -- An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy -- Part II Connectionist Learning -- Meta-learning and Neurocomputing - A New Perspective for Computational Intelligence -- Three-Term Fuzzy Back-Propagation -- Entropy Guided Transformation Learning -- Artificial Development -- Robust Training of Artificial Feedforward Neural Networks -- Workload Assignment in Production Networks by Multi Agent Architecture -- Part III Knowledge Representation and Acquisition -- Extensions to Knowledge Acquisition and Effect of Multimodal Representation in Unsupervised Learning -- A New Implementation for Neural Networks in Fourier-Space -- Part IV Learning and Visualization -- Dissimilarity Analysis and Application to Visual Comparisons -- Dynamic Self-Organising Maps: Theory, Methods and Applications -- Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization -- Author Index.

Note: Title Page -- Preface -- Contents -- Part I Global Optimization Algorithms: Theoretical Foundations and Perspectives -- Genetic Algorithms for the Use in Combinatorial Problems -- Introduction -- Evolutionary Optimization -- {\it Evolutionary Search Process} -- {\it Genetic Operators} -- {\it Genetic Algorithms} -- Crossover Challenging Problems -- {\it The Role of Crossover in GA} -- {\it Traditional Approaches to Crossover Challenging Tasks} -- Turbo Codes -- {\it Interleaver Evaluation} -- Genetic Algorithms for Linear Ordering Problem -- {\it Higher Level Chromosome Genetic Algorithms} -- HLCGAExperiments -- {\it Fitness Function Based on Average BER} -- {\it Fitness Function Based on Maximum Free Distance} -- Conclusions -- References -- Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications -- Introduction -- The Bacteria Foraging Optimization Algorithm -- Analysis of the Chemotactic Dynamics in BFOA -- {\it Derivation of Expression for Velocity} -- {\it Experimental Verification of Expression for Velocity} -- {\it Chemotaxis and the Classical Gradient Decent Search} -- {\it Oscillation Problem: Need for Adaptive Chemotaxis} -- {\it A Special Case} -- Analysis of the Reproduction Step in BFOA -- {\it Analytical Treatment} -- {\it Physical Significance} -- {\it Avoiding Premature Convergence} -- Hybridization of BFOA with Other Approaches -- Applications of BFOA -- Conclusions -- References -- Global Optimization Using Harmony Search: Theoretical Foundations and Applications -- Introduction -- Harmony Search Algorithm -- {\it Problem Formulation} -- {\it Initialization of Harmony Memory} -- {\it Improvisation of New Harmony} -- {\it Stochastic Derivative} -- {\it Optional Operations} -- {\it Update of Harmony Memory} -- {\it Termination of Computation} -- {\it Pseudo Code of the Algorithm}. , Examples of Global Optimization -- {\it Design of Water Distribution Networks} -- {\it Scheduling of Multiple Dams} -- {\it Layout of Fluid-Transport Branched Pipelines} -- Conclusions -- References -- Hybrid GRASP Heuristics -- Introduction -- ABasicGRASP -- Hybrid Construction Mechanisms -- GRASP and Path-Relinking -- GRASP and Other Metaheuristics -- {\it GRASP and Tabu Search} -- {\it GRASP and Simulated Annealing} -- {\it GRASP, Genetic Algorithms, and Population-Based Heuristics} -- {\it GRASP and Variable Neighborhood Search} -- {\it GRASP and Iterated Local Search} -- {\it GRASP and Very-Large Scale Neighborhood Search} -- {\it Other Hybridizations} -- Concluding Remarks -- References -- Particle Swarm Optimization: Performance Tuning and Empirical Analysis -- Introduction -- Particle Swarm Optimization -- Modified Version of Particle Swarm Optimization -- {\it Efficient Initialization Particle Swarm Optimization} -- {\it Diversity Guided Particle Swarm Optimization} -- {\it Crossover Based Particle Swarm Optimization} -- Numerical Problems -- {\it Benchmark Problems} -- {\it Real Life Problems} -- Experimental Settings -- Numerical Results -- Conclusions -- References -- Tabu Search to Solve Real-Life Combinatorial Optimization Problems: A Case of Study -- Introduction -- Tabu Search Principles -- AEOSProblem -- A Tabu Search Algorithm for AEOS Problem -- {\it Search Space Definition} -- {\it Neighborhood Definition} -- {\it Tabu List Management and Move Heuristic} -- {\it Intensification and Diversification Phases} -- {\it Global Tabu Resolution} -- Computational Results -- Conclusion -- References -- Reformulations in Mathematical Programming: A Computational Approach -- Introduction -- General Framework -- {\it A Data Structure for Mathematical Programming Formulations} -- {\it A Data Structure for Mathematical Expressions}. , {\it Standard Forms in Mathematical Programming} -- Reformulations -- {\it Reformulation Definitions} -- {\it Elementary Reformulations} -- {\it Exact Linearizations} -- {\it Advanced Reformulations} -- {\it Advanced Examples} -- Relaxations -- {\it Definitions} -- {\it Elementary Relaxations} -- {\it Advanced Relaxations} -- {\it Valid Cuts} -- Reformulation/Optimization Software Engine -- {\it Development History} -- {\it Software Architecture} -- {\it Ev3} -- {\it Validation Examples} -- Conclusion -- References -- Graph-Based Local Elimination Algorithms in Discrete Optimization -- Introduction -- Local Elimination Algorithms for Solving Discrete Problems -- Discrete Optimization Problems and Their Graph Representations -- {\it Notions and Definitions} -- Local Variable Elimination Algorithms in Discrete Optimization -- {\it Nonserial Dynamic Programming and Classification of DP Formulations} -- {\it Discrete Optimization Problem with Constraints} -- {\it Elimination Game, Combinatorial Elimination Process, and Underlying DAG of the LAE Computational Procedure} -- {\it Bucket Elimination} -- Block Local Elimination Scheme -- {\it Partitions, Clustering, and Quotient Graphs} -- Tree Structural Decompositions in Discrete Optimization -- {\it Tree Decomposition and Methods of Its Computing} -- {\it Computing Tree Decompositions for NSDP Schemes} -- {\it Applying the Local Decomposition Algorithm to Solving Do Problem} -- Conclusion -- References -- Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems -- Introduction -- General Optimization Problem -- Dynamic Multi-Objective Problem Defined on a Class of Test Functions -- {\it Dynamic Multi-Objective Test Problem} -- {\it Non-stationary Multi-objective Test Problem} -- Conclusion and Future Works -- References. , Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning -- Introduction -- Particle Swarm Optimization -- {\it Standard Particle Swarm Model} -- {\it Velocities Analysis in Particle Swarm} -- Turbulent Swarm Optimization -- {\it Velocity Update of the Particles} -- {\it Fuzzy Parameter Control} -- Convergence Analysis of TPSO -- Experiments and Discussions -- Conclusions -- References -- Part II Global Optimization Algorithms: Applications -- An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy -- Introduction -- The SVM Learning Scheme -- {\it A Viewpoint on Learning} -- {\it SVM Separating Hyperplanes} -- {\it Addressing Multi-class Problems through SVMs} -- {\it SVM Regression Hyperplanes} -- {\it Solving the Optimization Problem within SVMs} -- Evolutionary Adaptation of the Hyperplane Coefficients to the Training Data -- {\it Motivation and Aim} -- {\it Literature Review: Previous EA-SVM Interactions} -- {\it Evolving the Coefficients of the Hyperplane} -- {\it Preexperimental Planning: The Test Cases} -- DiscoveringESVMs -- {\it A Na\"{ı}ve Design} -- {\it Chunking within ESVMs} -- {\it A Pruned Variant} -- {\it A Crowding Variant} -- {\it Integration of SVM Hyperparameters} -- {\it ESVMs Versus SVMs} -- Conclusions and Outlook -- References -- Evolutionary Computing in Statistical Data Analysis -- Introduction -- GAs and EDAs Implementations -- {\it The GAs Procedure} -- {\it The EDAs Procedure} -- Variable Selection in Linear Regression and ARMA Models -- {\it Subset Regression} -- {\it Autoregressive Moving Average Models} -- {\it An Example of Subset ARMA Fitted to a Real Data Set} -- The Logistic Regression Model -- Multi-regimes Model Parameter Estimation -- {\it The Exponential Autoregressive Model} -- {\it The Generalized EXPAR Model}. , {\it Threshold Autoregressive Models} -- {\it Double Threshold ARCH and GARCH Models} -- {\it An Application to the Daily Hong Kong Stock Exchange (Hang Seng) Index} -- {\it An Application to the Daily Exchange Rate Yen/Dollar} -- Multiple Outliers in Data Sets -- {\it The Outlier Problem in Time Series} -- {\it Genetic Algorithms for Outlier Detection in Time Series} -- Genetic Algorithms for Cluster Analysis -- {\it Genetic Clustering Algorithms} -- {\it Cluster of Time Series} -- Concluding Remarks -- References -- Meta-heuristics for System Design Engineering -- Introduction -- The Multi-process Point of View of the Ieee 15288 and Eia 632 Standards -- {\it The IEEE Standard} -- {\it The EIA 632 Standard} -- Towards a Close Collaboration between System Design, Technical Management and Acquisition, and Supply Processes -- {\it Modeling Proposition for the System Design Process} -- {\it Mapping of the System Design Process with the Technical Management Process} -- {\it Mapping with the Design of Network of Partners} -- Generation and Selection of the Scenarios -- {\it Generic Representation of Scenarios} -- {\it Searching for "Good" Scenarios} -- {\it Hybrid Methods to Select the Best Scenarios} -- {\it Setting Up the Algorithm} -- {\it Detailed Description of the Hybrid Algorithm with Ant Colony Optimization} -- {\it Detailed Description of the Hybrid Algorithm} -- {\it Intensification} -- {\it Stopping Criterion} -- {\it Classification of the Found Scenarios} -- Experimental Results -- {\it Measure of Quality} -- {\it Interpretation of Our Results} -- Conclusion -- References -- Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms -- Introduction -- Biological Fundamentals -- Evolutionary Algorithms Based on Correlated Biological Concepts -- Transgenetic Algorithms. , {\it Basic Components of the Transgenetic Algorithms}.

Note: Title -- Preface -- Organization -- Table of Contents -- Plenary Papers and Reports -- Affordance Relations -- Introduction -- Affordance Relations -- Affordances and Robots -- Affordances and Concepts -- A Closer Look at the Affordance Relations -- Summary -- References -- Discovering Concurrent Process Models in Data: A Rough Set Approach -- Introduction -- Data Representation and Interpretation -- Research Methodology and Knowledge Representation -- Maximal Consistent Extensions of Information Systems -- Structures of Concurrent Models -- Computer Tool -- Applications -- Concluding Remarks -- References -- Intelligent Science -- Introduction -- A Framework of Intelligence Science -- Ten Big Issues of Intelligence Science -- Basic Process of Neural Activity -- Synaptic Plasticity -- Perceptual Representation and Feature Binding -- Coding and Retrieval of Memory -- Linguistic Cognition -- Learning -- Thought -- Emotion -- Nature of Consciousness -- Mind Modeling -- Tolerance Granular Space Model -- Perspective on Intelligence Science -- References -- Pattern Structures for Analyzing Complex Data -- Introduction -- Concept Lattices and Concept-Based Learning -- Main Definitions -- Many-Valued Contexts and Their Interordinal Scaling -- Pattern Structures -- Main Definitions and Results -- Learning with Pattern Structures -- Projections and Learning in Projections -- Pattern Structures on Closed Sets of Labeled Graphs -- Pattern Structures on Intervals -- Main Definitions -- Interval Patterns and Interordinal Scaling -- Computing in Pattern Structures -- Conclusion -- References -- Fuzzy Sets and Rough Sets for Scenario Modelling and Analysis -- Introduction -- Plausible Scenario-Based Approach -- Flexible Composition Modelling -- Plausible Scenario-Based Intelligence Monitoring -- Illustrative Component Approaches. , Fuzzy Learning and Feature Selection -- Fuzzy Interpolative Reasoning -- Fuzzy Risk Assessment -- Conclusion -- References -- Innovation Game as a Tool of Chance Discovery -- Introduction: Chance Discovery as Value Sensing -- Using a Scenario Map for Chance Discovery -- Innovation Game -- The Outline of Innovation Game with Data Crystallization -- Findings from Players' Communications -- Niche of Idea Activations as Source of Innovations: Another Finding from the Innovation Game -- Conclusions -- References -- Rough Set Year in India 2009 -- Foundations of Rough Sets and Beyond -- An Algebraic Semantics for the Logic of Multiple-Source Approximation Systems -- Introduction -- LMSAS -- Algebraic Semantics for LMSAS -- Conclusions -- References -- Towards an Algebraic Approach for Cover Based Rough Semantics and Combinations of Approximation Spaces -- Introduction -- Granules and Equalities in AUAI Rough Set Theory -- Synchronal Approximation Spaces -- Multiple Approximation Spaces -- Generalisation of AUAI-Approximation Systems -- Further Directions -- References -- New Approach in Defining Rough Approximations -- Introduction -- Preliminaries -- Some Structures on the Ordered Set of Rough Approximations -- New Approaches in Defining the Rough Approximations -- References -- Rough Set Approximations Based on Granular Labels -- Introduction -- Functions and Labelled Blocks -- Block-Label Approximations of Sets -- Properties of Block-Label Approximations -- Conclusion -- References -- On a Criterion of Similarity between Partitions Based on Rough Set Theory -- Introduction -- Rough Set -- Evaluation of Relative Reducts Using Partitions -- A Criterion of Similarity between Partitions -- Conclusion -- References -- On Rough Sets with Structures and Properties -- Introduction -- Principles of Rough Sets Paradigm and Relations. , Sets with Structures and Properties -- Rough Approximations of Sets with Structures and Properties -- Compound Properties and Mixed Approximations -- Final Comment -- References -- A Formal Concept Analysis Approach to Rough Data Tables -- Introduction -- Partial Formal Contexts -- Partial Contexts Obtained from Streams -- The Contest Data Set -- Soft Granulation -- Conclusion -- References -- Covering Based Approaches to Rough Sets and Implication Lattices -- Introduction -- Various Types of Lower and Upper Approximations -- Implication Lattices -- Concluding Remarks -- References -- A Logical Reasoning System of Before-after Relation Based on Bf-EVALPSN -- Introduction -- Bf-EVALPSN -- Bf-Relation Reasoning System -- Conclusions -- References -- Some Proof Theoretic Results Depending on Context from the Perspective of Graded Consequence -- Introduction -- Introduction to Graded Consequence -- Proof Theoretic Properties of Graded Consequence -- Significance of Proof Theoretic Properties -- An Example -- Conclusion -- References -- Rough Set Algorithms and Applications -- IQuickReduct: An Improvement to Quick Reduct Algorithm -- Introduction -- QuickReduct Algorithm -- Limitations of Quick Reduct Algorithm and Relevance of VPRS Heuristic -- IQuickReduct Algorithm -- Experiments and Results -- Conclusions and Future Work -- References -- Dynamic Reduct from Partially Uncertain Data Using Rough Sets -- Introduction -- Rough Sets -- Belief Function Theory -- Dynamic Reduct under Uncertainty -- Basic Concepts of Rough Sets under Uncertainty -- Dynamic Reduct from Uncertain Data -- Conclusion and Future Work -- References -- Approximation of Loss and Risk in Selected Granular Systems -- Introduction -- Risk in Statistical Learning Theory -- Approximation of Loss Function and Its Integral -- The Rough Set Case -- The Fuzzy Set Case. , Classifiers, Neighbourhoods and Granulation -- Conclusion -- References -- An Efficient Gene Selection Algorithm Based on Tolerance Rough Set Theory -- Introduction -- Preliminaries -- Rough Set Theory -- T-Test -- Gene Selection Algorithm Based on Rough Set Theory -- Gene Selection Algorithm Based on Tolerance Rough Set Theory -- Similarity Measures -- Tolerance Rough Set Theory-Based Gene Selection Method -- A Simple Example -- Experiments -- Conclusions -- References -- Rough Set Based Social Networking Framework to Retrieve User-Centric Information -- Introduction -- Related Work -- Social Networking Based Information Retrieval System - A Rough Set Based Approach -- Concept Combination Ranking Algorithm -- Document Ranking Algorithm -- Experimental Results and Discussion -- Conclusion and Future Scope -- References -- Use of Fuzzy Rough Set Attribute Reduction in High Scent Web Page Recommendations -- Introduction -- Information Scent Computation -- Information Scent Metric -- Fuzzy Rough Approach for Attribute Reduction -- Fuzzy Rough Set Attribute Reduction (FRSAR) -- Use of FRSAR in Information Retrieval -- Query Sessions Representation -- Fuzzy Rough Set Attribute Reduction of Clustered Query Sessions Keyword Vector -- High Scent Web Page Recommendations in Information Retrieval Using FRSAR -- Experimental Study -- Conclusion -- References -- A Rough Sets Approach for Personalized Support of Face Recognition -- Introduction -- Sorting Facial Photographs for Similarity -- Pre-processing -- Rough Set Attribute Reduction Methodology -- Conclusion and Future Work -- References -- Taking Fuzzy-Rough Application to Mars Fuzzy-Rough Feature Selection for Mars Terrain Image Classification -- Introduction -- McMurdo Panorama Image -- Feature Extraction -- Color Statistics-Based Features -- Local Histogram-Based Features. , Fuzzy-Rough Set-Based Feature Selection -- Image Classifiers -- Experimental Results -- Comparison with the Use of All Original Features -- Comparison with the Use of PCA-Returned Features -- Classified and Segmented Images -- Conclusion -- References -- Rough Image Colour Quantisation -- Introduction -- Rough Colour Quantisation -- Experimental Results -- Conclusions -- References -- Part of Speech (POS) Tag Sets Reduction and Analysis Using Rough Set Techniques -- Introduction -- Similarity Calculation Using LCS and POS Tagging -- Similarity Calculation -- POS Tagging and Tag-Sets -- Rough Sets -- Approximation Space -- Rough Approximations -- Experiments and Discussions -- Reuters's Collection: 9-Tags Tag Set: Resulting Reducts and Validations -- Reuters's Collection: 19-Tags Tag Set: Resulting Reducts and Validations -- Reuters's Collection: 55-Tags Tag Set: Resulting Reducts and Validations -- Performance -- Conclusion -- Reference -- Fuzzy Set Foundations and Applications -- More on Intuitionistic Fuzzy Soft Sets -- Introduction -- Theory of Soft Sets -- Intuitionistic Fuzzy Sets -- Intuitionistic Fuzzy Soft Sets -- The Necessity Operation on Intuitionistic Fuzzy Soft Set -- An Application of Newly Defined Operation on IFSS -- Conclusion -- References -- On Fuzzy Contra α-Irresolute Maps -- Introduction -- Preliminaries -- On Fap -Irresolute, Fap -Closed andFc -Irresolute Maps -- On Fap α-Irresolute, Fap α-Closed and Fc α-Irresolute Maps -- A Characterization of F\alpha-T_{1/2} Spaces -- References -- On Fuzzy Strongly α-I-Open Sets -- Introduction -- Preliminaries -- Fuzzy Strongly \alpha-I-Open Sets -- Fuzzy Strongly \alpha-I-Continuous Maps -- References -- Lattice Structures of Rough Fuzzy Sets -- Introduction -- Rough Fuzzy Sets -- Properties of Rough Fuzzy Sets -- Lattice from Rough Fuzzy Sets. , Characterization of Borderline Region.