.

0 (hodnocen0 x )

BK

Dordrecht : Springer, 2015

lvi, 1633 stran : ilustrace, portréty ; 25 cm

ISBN 978-3-662-43504-5 (vázáno)

Obsahuje bibliografie, bibliografické odkazy a rejstřík

001418999

List of Abbreviations XLV // 1 Introduction // Janusz Kacprzyk, Witold Pedrycz 1 // 1.1 Details of the Contents 2 // 1.2 Conclusions and Acknowledgments 4 // Part A Foundations // 2 Many-Valued and Fuzzy Logics // Siegfried Gottwald 7 // 2.1 Basic Many-Valued Logics 8 // 2.2 Fuzzy Sets 11 // 2.3 t-Norm-Based Logics 13 // 2.4 Particular Fuzzy Logics 16 // 2.5 Some Generalizations 21 // 2.6 Extensions with Graded Notions of Inference 23 // 2.7 Some Complexity Results 25 // 2.8 Concluding Remarks 27 // References 27 // 3 Possibility Theory and Its Applications: Where Do We Stand? // Didier Dubois, Henry Prode 31 // 3.1 Historical Background 32 // 3.2 Basic Notions of Possibility Theory 33 // 3.3 Qualitative Possibility Theory 38 // 3.4 Quantitative Possibility Theory 45 // 3.5 Some Applications 49 // 3.6 Some Current Research Lines 53 // References 54 // 4 Aggregation Functions on [0,1] // Radko Mesia r, Anna Kolesa rovo, Magda Komorníkova 61 // 4.1 Historical and Introductory Remarks 61 // 4.2 Classification of Aggregation Functions 63 // 4.3 Properties and Construction Methods 66 // 4.4 Concluding Remarks 71 // References 72 // 5 Monotone Measures-Based Integrals // Erich P. Klement, Radko Mestar 75 // 5.1 Preliminaries, Choquet, and Sugeno Integrals 76 // 5.2 Benvenuti Integral 80 // 5.3 Universal Integrals 82 // 5.4 General Integrals Which Are Not Universal 84 // 5.5 Concluding Remarks, Application Fields 86 // References 87 // 6 The Origin of Fuzzy Extensions // Humberto Bustince, Edurne Barrenechea, Javier Fernandez, // Miguel Pagala, Javier Monterŕ 89 // 6.1 Considerations Prior to the Concept of Extension of Fuzzy Sets. 90 // 6.2 Origin of the Extensions 83 // 6.3 Type-2 Fuzzy Sets  // 6.4 Interval-Valued Fuzzy Sets 98 // 6.5 Atanasssov’s Intuitionistic Fuzzy Sets or Bipolar Fuzzy Sets // of Type 2 or IF Fuzzy Sets 103 // 6.6 Atanassov’s Interval-Valued Intuitionistic Fuzzy Sets 105 //

6.7 Links Between the Extensions of Fuzzy Sets 106 // 6.8 Other Types of Sets 106 // 6.9 Conclusions 108 // References 108 // 7 F-Transform // Irina Perfilieva 113 // 7.1 Fuzzy Modeling 113 // 7.2 Fuzzy Partitions 11? // 7.3 Fuzzy Transform 117 // 7.4 Discrete F-Transform 119 // 7.5 F-Transforms of Functions of Two Variables 120 // 7.6 F-Transform 121 // 7.7 Applications 122 // 7.8 Conclusions 129 // References 129 // 8 Fuzzy Linear Programming and Duality // Jaroslav Rámik, Milan Vlach 131 // 8.1 Preliminaries 132 // 8.2 Fuzzy Linear Programming 135 // 8.3 Duality in Fuzzy Linear Programming 137 // 8.4 Conclusion 143 // References 1?3 // 9 Basic Solutions of Fuzzy Coalitional Games // Tomáš Kroupa, Milan Vlach 145 // 9.1 Coalitional Games with Transferable Utility 146 // 9.2 Coalitional Games with Fuzzy Coalitions 150 // 9.3 Final Remarks 155 // References 156 // Part B Fuzzy Logic // 10 Basics of Fuzzy Sets // János C. Fodor, Imre J. Rudas 159 // 10.1 Classical Mathematics and Logic 160 // 10.2 Fuzzy Logic, Membership Functions, and Fuzzy Sets 160 // 10.3 Connectives in Fuzzy Logic 161 // 10.4 Concluding Remarks 168 // References 168 // 11 Fuzzy Relations: Past, Present, and Future // Susana Montes, Ignacio Montes, Tania Iglesias 171 // 11.1 Fuzzy Relations 172 // 11.2 Cut Relations 174 // 11.3 Fuzzy Binary Relations 174 // 11.4 Particular Cases of Fuzzy Binary Relations 179 // 11.5 Present and Future of Fuzzy Relations 180 // References 180 // 12 Fuzzy Implications: Past, Present, and Future // Michal Baczynski, Balasubramaniam Jayaram, Sebastia Massanet, // Joan Torrens 183 // 12.1 Fuzzy Implications: Examples, Properties, and Classes 184 // 12.2 Current Research on Fuzzy Implications 187 // 12.3 Fuzzy Implications in Applications 193 // 12.4 Future of Fuzzy Implications 198 // References 199 // 13 Fuzzy Rule-Based Systems // Luis Magdalena 203 // 13.1 Components of a Fuzzy Rule Based-System 204 //

13.2 Types of Fuzzy Rule-Based Systems 209 // 13.3 Hierarchical Fuzzy Rule-Based Systems 213 // 13.4 Fuzzy Rule-Based Systems Design 214 // 13.5 Conclusions 216 // References 217 // 14 Interpretability of Fuzzy Systems: // Current Research Trends and Prospects // Jose M. Alonso, Ciro Castiello, Corrado Mencar 219 // 14.1 The Quest for Interpretability 220 // 14.2 Interpretability Constraints and Criteria 224 // 14.3 Interpretability Assessment 227 // 14.4 Designing Interpretable Fuzzy Systems 229 // 14.5 Interpretable Fuzzy Systems in the Real World 233 // 14.6 Future Research Trends on Interpretable Fuzzy Systems 234 // 14.7 Conclusions 234 // References 235 // 15 Fuzzy Clustering - Basic Ideas and Overview // Sadaaki Miyamoto 239 // 15.1 Fuzzy Clustering 239 // 15.2 Fuzzy c-Means 239 // 15.3 Hierarchical Fuzzy Clustering 245 // 15.4 Conclusion 246 // References 247 // 16 An Algebraic Model of Reasoning to Support Zadeh’s CWW // Enric Trillas 249 // 16.1 A View on Reasoning 249 // 16.2 Models 250 // 16.3 Reasoning 251 // 16.4 Reasoning and Logic 254 // 16.5 A Possible Scheme for an Algebraic Model // of Commonsense Reasoning 255 // 16.6 Weak and Strong Deduction: Refutations and Conjectures // in a ? FA (with a Few Restrictions) 260 // 16.7 Toward a Classification of Conjectures 262 // 16.8 Last Remarks 264 // 16.9 Conclusions 265 // References 266 // 17 Fuzzy Control // Christian Moewes, Ralf Mikut, Rudolf Kruse 269 // 17.1 Knowledge-Driven Control 269 // 17.2 Classical Control Engineering 270 // 17.3 Using Fuzzy Rules for Control 271 // 17.4 A Glance at Some Industrial Applications 276 // 17.5 Automatic Learning of Fuzzy Controllers 279 // 17.6 Conclusions 281 // References 281 // 18 Interval Type-2 Fuzzy PID Controllers // Tufan Kumbasar, Hani Hagras 285 // 18.1 Fuzzy Control Background 285 // 18.2 The General Fuzzy PID Controller Structure 286 // 18.3 Simulation Studies 291 // 18.4 Conclusion 292 // References 293 //

19 Soft Computing in Database and Information Management // Guy De Tré, Stawomir Zadrožny 295 // 19.1 Challenges for Modern Information Systems 295 // 19.2 Some Preliminaries 296 // 19.3 Soft Computing in Information Modeling 298 // 19.4 Soft Computing in Querying 302 // 19.5 Conclusions 309 // References 309 // 20 Application of Fuzzy Techniques to Autonomous Robots // Ismael Rodriguez Fdez, Manuel Mucientes, Alberto Bugarin Diz BIB // 20.1 Robotics and Fuzzy Logic 313 // 20.2 Wall-Following 314 // 20.3 Navigation 315 // 20.4 Trajectory Tracking 317 // 20.5 Moving Target Tracking 318 // 20.6 Perception 319 // 20.7 Planning 319 // 20.8 SLAM 320 // 20.9 Cooperation 320 // 20.10 Legged Robots 321 // 20.11 Exoskeletons and Rehabilitation Robots 322 // 20.12 Emotional Robots 323 // 20.13 Fuzzy Modeling 323 // 20.14 Comments and Conclusions 324 // References 325 // Part C Rough Sets // 21 Foundations of Rough Sets // Andrzej Skowron, Andrzej Jankowski, Roman W. Swiniarski 331 // 21.1 Rough Sets: Comments on Development 331 // 21.2 Vague Concepts 332 // 21.3 Rough Set Philosophy 333 // 21.4 Indiscernibility and Approximation 333 // 21.5 Decision Systems and Decision Rules 336 // 21.6 Dependencies 337 // 21.7 Reduction of Attributes 337 // 21.8 Rough Membership 338 // 21.9 Discernibility and Boolean Reasoning 339 // 21.10 Rough Sets and Induction 340 // 21.11 Rough Set-Based Generalizations 340 // 21.12 Rough Sets and Logic 343 // 21.13 Conclusions 347 // References 347 // 22 Rough Set Methodology for Decision Aiding // Roman Stowinski, Salvatore Greco, Benedetto Matarazzo 349 // 22.1 Data Inconsistency as a Reason for Using Rough Sets 350 // 22.2 The Need for Replacing the Indiscernibility Relation by the Dominance Relation when Reasoning About Ordinal Data 351 // 22.3 The Dominance-based Rough Set Approach to Multi-Criteria Classification 353 // 22.4 The Dominance-based Rough Set Approach to Multi-Criteria Choice and Ranking 361 //

22.5 Important Extensions of ORSA 366 // 22.6 ORSA to Operational Research Problems 366 // 22.7 Concluding Remarks on ORSA Applied to Multi-Criteria Decision Problems 367 // References 367 // 23 Rule Induction from Rough Approximations // Jerzy W. Grzymala-Busse 371 // 23.1 Complete and Consistent Data 371 // 23.2 Inconsistent Data 375 // 23.3 Decision Table with Numerical Attributes 377 // 23.4 Incomplete Data 378 // 23.5 Conclusions 384 // References 384 // 24 Probabilistic Rough Sets // Yiyu Yao, Salvatore Greco, Roman Stowinski 387 // 24.1 Motivation for Studying Probabilistic Rough Sets 388 // 24.2 Pawlak Rough Sets 388 // 24.3 A Basic Model of Probabilistic Rough Sets 390 // 24.4 Variants of Probabilistic Rough Sets 391 // 24.5 Three Fundamental Issues of Probabilistic Rough Sets 394 // 24.6 Dominance-Based Rough Set Approaches 398 // 24.7 A Basic Model of Dominance-Based Probabilistic Rough Sets 399 // 24.8 Variants of Probabilistic Dominance-Based Rough Set Approach 400 // 24.9 Three Fundamental Issues of Probabilistic Dominance-Based // Rough Sets 403 // 24.10 Conclusions 409 // References 409 // 25 Generalized Rough Sets // JingTao Yao, Davide Ciucci, Yan Zhang 413 // 25.1 Definition and Approximations of the Models 414 // 25.2 Theoretical Approaches 420 // 25.3 Conclusion 422 // References 423 // 26 Fuzzy-Rough Hybridization // Masahiro Inuiguchi, Wei-Zhi Wu, Chris Cornelis, Nele Verbiest 425 // 26.1 Introduction to Fuzzy-Rough Hybridization 425 // 26.2 Classification- Versus Approximation-Oriented // Fuzzy Rough Set Models 427 // 26.3 Generalized Fuzzy Belief Structures with Application // in Fuzzy Information Systems 437 // 26.4 Applications of Fuzzy Rough Sets 444 // References 447 // Part D Neural Networks // 27 Artificial Neural Network Models // Peter Tino, Lubica Benuskova, Alessandro Sperduti 455 // 27.1 Biological Neurons 455 // 27.2 Perceptron 456 // 27.3 Multilayered Feed-Forward ANN Models 458 //

27.4 Recurrent ANN Models 460 // 27.5 Radial Basis Function ANN Models 464 // 27.6 Self-Organizing Maps 465 // 27.7 Recursive Neural Networks 467 // 27.8 Conclusion 469 // References 470 // 28 Deep and Modular Neural Networks // Ke Chen 473 // 28.1 Overview 473 // 28.2 Deep Neural Networks 474 // 28.3 Modular Neural Networks 484 // 28.4 Concluding Remarks 492 // References 492 // 29 Machine Learning // James T. Kwok, Zhi-Hua Zhou, Lei Xu 495 // 29.1 Overview 495 // 29.2 Supervised Learning 497 // 29.3 Unsupervised Learning 502 // 29.4 Reinforcement Learning 510 // 29.5 Semi-Supervised Learning 513 // 29.6 Ensemble Methods 514 // 29.7 Feature Selection and Extraction 518 // References 519 // 30 Theoretical Methods in Machine Learning // Badong Chen, Weifeng Liu, José C. Principe 523 // 30.1 Background Overview 524 // 30.2 Reproducing Kernel Hilbert Spaces 525 // 30.3 Online Learning with Kernel Adaptive Filters 527 // 30.4 Illustration Examples 538 // 30.5 Conclusion 542 // References 542 // 31 Probabilistic Modeling in Machine Learning // Davide Bacciu, Paulo J.G. Lisboa, Alessandro Sperduti, Thomas Villmann . 545 // 31.1 Probabilistic and Information-Theoretic Methods 545 // 31.2 Graphical Models 552 // 31.3 Latent Variable Models 560 // 31.4 Markov Models 565 // 31.5 Conclusion and Further Reading 572 // References 573 // 32 Kernel Methods // Marco Signo retto, Johan A. ?. Suykens 577 // 32.1 Background 578 // 32.2 Foundations of Statistical Learning 580 // 32.3 Primal-Dual Methods 586 // 32.4 Gaussian Processes 593 // 32.5 Model Selection 596 // 32.6 More on Kernels 597 // 32.7 Applications 600 // References 601 // 33 Neurodynamics // Robert Kozma, Jun Wang, Zhigang Zeng 607 // 33.1 Dynamics of Attractor and Analog Networks 607 // 33.2 Synchrony, Oscillations, and Chaos in Neural Networks 611 // 33.3 Memristive Neurodynamics 629 // 33.4 Neurodynamic Optimization 634 // References 639 //

34 Computational Neuroscience - Biophysical Modeling of Neural Systems // Harrison Stratton, Jennie Si 649 // 34.1 Anatomy and Physiology of the Nervous System 649 // 34.2 Cells and Signaling Among Cells 652 // 34.3 Modeling Biophysically Realistic Neurons 656 // 34.4 Reducing Computational Complexity // for Large Network Simulations 660 // 34.5 Conclusions 662 // References 662 // 35 Computational Models of Cognitive and Motor Control // AHA. Minai 665 // 35.1 Overview 665 // 35.2 Motor Control 667 // 35.3 Cognitive Control and Working Memory 670 // 35.4 Conclusion 674 // References 674 // 36 Cognitive Architectures and Agents // Sebastien Hélie, Ron Sun 683 // 36.1 Background 683 // 36.2 Adaptive Control of Thought-Rational (ACT-R) 685 // 36.3 Soar 688 // 36.4 CLARION 690 // 36.5 Cognitive Architectures as Models of Multi-Agent Interaction. 693 // 36.6 General Discussion 694 // References 695 // 37 Embodied Intelligence // Angelo Congelasi, Josh Bongard, Martin H. Fischer, Stefano Noifi 697 // 37.1 Introduction to Embodied Intelligence 697 // 37.2 Morphological Computation for Body-Behavior Coadaptation 698 // 37.3 Sensory-Motor Coordination in Evolving Robots 701 // 37.4 Developmental Robotics for Higher Order Embodied Cognitive // Capabilities 703 // 37.5 Conclusion 709 // References 711 // 38 Neuromorphic Engineering // Giacomo Indiveri 715 // 38.1 The Origins 715 // 38.2 Neural and Neuromorphic Computing 716 // 38.3 The Importance of Fundamental Neuroscience 717 // 38.4 Temporal Dynamics in Neuromorphic Architectures 718 // 38.5 Synapse and Neuron Circuits 719 // 38.6 Spike-Based Multichip Neuromorphic Systems 721 // 38.7 State-Dependent Computation in Neuromorphic Systems 722 // 38.8 Conclusions 722 // References 723 // 39 Neuroengineering // Damien Coyle, Ronen Sosnik 727 // 39.1 Overview - Neuroengineering in General 728 // 39.2 Human Motor Control 732 // 39.3 Modeling the Motor System - Internal Motor Models 733 //

39.4 Sensorimotor Learning 736 // 39.5 MRI and the Motor System - Structure and Function 738 // 39.6 Electrocorticographic Motor Cortical Surface Potentials 741 // 39.7 MEG and EEG - Extra Cerebral Magnetic and Electric Fields of the Motor System 745 // 39.8 Extracellular Recording - Decoding Hand Movements from Spikes and Local Field Potential 748 // 39.9 Translating Brainwaves into Control Signals - BCIs 754 // 39.10 Conclusion 762 // References 764 // 40 Evolving Connectionist Systems: // From Neuro-Fuzzy-, to Spiking- and Neuro-Genetic // Nikola Kasabov 771 // 40.1 Principles of Evolving Connectionist Systems (EC0S) 771 // 40.2 Hybrid Systems and Evolving Neuro-Fuzzy Systems 772 // 40.3 Evolving Spiking Neural Networks (eSNN) 775 // 40.4 Computational Neuro-Genetic Modeling (CNGM) 778 // 40.5 Conclusions and Further Directions 779 // References 780 // 41 Machine Learning Applications // Piero P. Bonissone 78? // 41.1 Motivation 784 // 41.2 Machine Learning (ML) Functions 786 // 41.3 CI/ML Applications in Industrial Domains: // Prognostics and Health Management (PHM) 787 // 41.4 CI/ML Applications in Financial Domains: Risk Management 797 // 41.5 Model Ensembles and Fusion 807 // 41.6 Summary and Future Research Challenges 812 // References 817 // Part E Evolutionary Computation // 42 Genetic Algorithms // Jonathan E. Rowe 825 // 42.1 Algorithmic Framework 826 // 42.2 Selection Methods 828 // 42.3 Replacement Methods 831 // 42.4 Mutation Methods 832 // 42.5 Selection-Mutation Balance 834 // 42.6 Crossover Methods 836 // 42.7 Population Diversity 838 // 42.8 Parallel Genetic Algorithms 839 // 42.9 Populations as Solutions 841 // 42.10 Conclusions 842 // References 843 // 43 Genetic Programming // James McDermott, Una-May O’Reilly 845 // 43.1 Evolutionary Search for Executable Programs 845 // 43.2 History 846 // 43.3 Taxonomy of Al and GP 848 // 43.4 Uses of GP 853 // 43.5 Research Topics 857 // 43.6 Practicalities 861 // References 862 //

44 Evolution Strategies // Nikolaus Hansen, Dirk I/. Arnold, Anne Auger 871 // 44.1 Overview 871 // 44.2 Main Principles 873 // 44.3 Parameter Control 877 // 44.4 Theory 886 // References 895 // 45 Estimation of Distribution Algorithms // Martin Pelikan, Mark W. Hauschild, Fernando 0. Lobo 899 // 45.1 Basic EDA Procedure 900 // 45.2 Taxonomy of EDA Models 90S // 45.3 Overview of EDAs 908 // 45.4 EDA Theory 916 // 45.5 Efficiency Enhancement Techniques for EDAs 917 // 45.6 Starting Points for Obtaining Additional Information 920 // 45.7 Summary and Conclusions 921 // References 921 // 46 Parallel Evolutionary Algorithms // Dirk Sud holt 929 // 46.1 Parallel Models 931 // 46.2 Effects of Parallelization 935 // 46.3 On the Spread of Information in Parallel EAs 938 // 46.4 Examples Where Parallel EAs Excel 943 // 46.5 Speedups by Parallelization 949 // 46.6 Conclusions 956 // References 957 // 47 Learning Classifier Systems // Martin M. Butz 961 // 47.1 Background 962 // 47.2 XCS 965 // 47.3 XCSF 970 // 47.4 Data Mining 972 // 47.5 Behavioral Learning 973 // 47.6 Conclusions 977 // 47.7 Books and Source Code 978 // References 979 // 48 Indicator-Based Selection // Lothar Thiele 983 // 48.1 Motivation 983 // 48.2 Basic Concepts 984 // 48.3 Selection Schemes 987 // 48.4 Preference-Based Selection 990 // 48.5 Concluding Remarks 992 // References 993 // 49 Multi-Objective Evolutionary Algorithms // Kalyanmoy Deb 995 // 49.1 Preamble 995 // 49.2 Evolutionary Multi-Objective Optimization (EMO) 996 // 49.3 A Brief Timeline for the Development of EMO Methodologies 999 // 49.4 Elitist EMO: NSGA-II 1000 // 49.5 Applications of EMO 1002 // 49.6 Recent Developments in EMO 1004 // 49.7 Conclusions // References // 50 Parallel Multiobjective Evolutionary Algorithms // Francisco Luna, Enrique Alba 1017 // 50.1 Multiobjective Optimization and Parallelism 1017 // 50.2 Parallel Models for Evolutionary Multi-Objective Algorithms 101 //

50.3 An Updated Review of the Literature 1020 // 50.4 Conclusions and Future Works 1026 // References 1027 // 51 Many-Objective Problems: Challenges and Methods // Antonio Lopez Jaimes, Carlos A. Coello Coello 1033 // 51.1 Background 1033 // 51.2 Basic Concepts and Notation 1034 // 51.3 Sources of Difficulty to Solve Many-Objective // Optimization Problems 1036 // 51.4 Current Approaches to Deal with Many-Objective Problems 1038 // 51.5 Recombination Operators and Mating Restrictions 1042 // 51.6 Scalarization Methods 1043 // 51.7 Conclusions and Research Paths 1043 // References 1044 // 52 Memetic and Hybrid Evolutionary Algorithms // Jhon Edgar Amaya, Carlos Cotta Porras, Antonio J. Fernández Leiva 1047 // 52.1 Overview 1047 // 52.2 A Bird’s View of Evolutionary Algorithms 1049 // 52.3 From Hybrid Metaheuristics to Hybrid EAs 1050 // 52.4 Memetic Algorithms 1052 // 52.5 Cooperative Optimization Models 1055 // 52.6 Conclusions 1056 // References 1056 // 53 Design of Representations and Search Operators // Franz Roth lauf 1061 // 53.1 Representations 1061 // 53.2 Search Operators 1065 // 53.3 Problem-Specific Design of Representations and Search Operators. 1071 // 53.4 Summary and Conclusions 1079 // References 1080 // 54 Stochastic Local Search Algorithms: An Overview // Floiger H. Hoos, Thomas Stützte 1085 // 54.1 The Nature and Concept of SIS 1086 // 54.2 Greedy Construction Heuristics and Iterative Improvement 1089 // 54.3 S/mp/e SIS Methods 1091 // 54.4 Hybrid SIS Methods 1094 // 54.5 Population-Based SLS Methods 1095 // 54.6 Recent Research Directions 1097 // References 1100 // 55 Parallel Evolutionary Combinatorial Optimization // El-Ghazali Talbi 1107 // 55.1 Motivation 1107 // 55.2 Parallel Design of EAs 1108 // 55.? Parallel Implementation of EAs 1113 // 55.4 Parallel EAs Under ParadisEO 1122 // 55.5 Conclusions and Perspectives 1123 // References 1124 //

56 How to Create Generalizable Results // Thomas Bartz-Beielstein 1127 // 56.1 Test Problems in Computational Intelligence 1127 // 56.2 Features of Optimization Problems 1128 // 56.3 Algorithm Features 1130 // 56.4 Objective Functions 1131 // 56.5 Case Studies 1133 // 56.6 Summary and Outlook 1141 // References 1142 // 57 Computational Intelligence in Industrial Applications // Ekaterina Vladislavleva, Guido Smits, Mark Kotanchek 1143 // 57.1 Intelligence and Computation 1143 // 57.2 Computational Modeling for Predictive Analytics 1144 // 57.3 Methods 1147 // 57.4 Workflows 1149 // 57.5 Examples 1150 // 57.6 Conclusions 1155 // References 1156 // 58 Solving Phase Equilibrium Problems // by Means of Avoidance-Based Multiobjectivization // Mike Preuss, Simon Wessing, Günter Rudolph, Gabriele Sadowski 1159 // 58.1 Coping with Real-World Optimization Problems 1159 // 58.2 The Phase-Equilibrium Calculation Problem 1161 // 58.3 Multiobjectivization-Assisted Multimodal Optimization: ?0??0 1162 // 58.4 Solving General Phase-Equilibrium Problems 1165 // 58.5 Conclusions and Outlook 1169 // References 1169 // 59 Modeling and Optimization of Machining Problems // Dirk Biermann, Petra Kersting, Tobias Wagner, Andreas Zabel 1173 // 59.1 Elements of a Machining Process 1174 // 59.2 Design Optimization 1175 // 59.3 Computer-Aided Design and Manufacturing 1176 // 59.4 Modeling and Simulation of the Machining Process 1177 // 59.5 Optimization of the Process Parameters 1178 // 59.6 Process Monitoring 1179 // 59.7 Visualization 1179 // 59.8 Summary and Outlook 1180 // References 1180 // 60 Aerodynamic Design with Physics-Based Surrogates // Emiliano luliano, Domenico Quagliarella 1185 // 60.1 The Aerodynamic Design Problem 1186 // 60.2 Literature Review of Surrogate-Based Optimization 1187 // 60.3 POD-Based Surrogates 1190 // 60.4 Application Example of POD-Based Surrogates 1191 // 60.5 Strategies for Improving POD Model Quality: Adaptive Sampling 1199 //

60.6 Aerodynamic Shape Optimization by Surrogate Modeling and Evolutionary Computing 1201 // 60.7 Conclusions 1207 // References 1208 // 61 Knowledge Discovery in Bioinformatics // Julie Hamon, Julie Jacques, Laetitia Jourdan, Clarisse Dhaenens 1211 // 61.1 Challenges in Bioinformatics 1211 // 61.2 Association Rules by Evolutionary Algorithm in Bioinformatics. 1212 // 61.3 Feature Selection for Classification and Regression // by Evolutionary Algorithm in Bioinformatics 1215 // 61.4 Clustering by Evolutionary Algorithm in Bioinformatics 1218 // 61.5 Conclusion 1220 // References 1221 // 62 Integration of Metaheuristics and Constraint Programming // Luca Di Gaspero 1225 // 62.1 Constraint Programming and Metaheuristics 1225 // 62.2 Constraint Programming Essentials 1226 // 62.3 Integration of Metaheuristics and CP 1230 // 62.4 Conclusions 1234 // References 1235 // 63 Graph Coloring and Recombination // Rhyd Lewis 1239 // 63.1 Graph Coloring 1239 // 63.2 Algorithms for Graph Coloring 1240 // 63.3 Setup 1244 // 63.4 Experimenti 1246 // 63.5 Experiment 2 1249 // 63.6 Conclusions and Discussion 1251 // References 1252 // 64 Metaheuristic Algorithms and Tree Decomposition // Thomas Hammer!, Nysret Musliu, Werner Schafhauser 1255 // 64.1 Tree Decompositions 1256 // 64.2 Generating Tree Decompositions by Metaheuristic Techniques 1258 // 64.3 Conclusion 1268 // References 1269 // 65 Evolutionary Computation and Constraint Satisfaction // Jano I. van Hemert 1271 // 65.1 Informal Introduction to CSP 1271 // 65.2 Formal Definitions 1272 // 65.3 Solving CSP with Evolutionary Algorithms 1273 // 65.4 Performance Indicators 1275 // 65.5 Specific Constraint Satisfaction Problems 1277 // 65.6 Creating Rather than Solving Problems 1283 // 65.7 Conclusions and Future Directions 1284 // References 1284 // Part F Swarm Intelligence // 66 Swarm Intelligence in Optimization and Robotics // Christian Blum, Roderich Groß 1291 //

66.1 Overview 1291 // 66.2 SI in Optimization 1292 // 66.3 SI in Robotics: Swarm Robotics 1296 // 66.4 Research Challenges 1302 // References 1303 // 67 Preference-Based Multiobjective // Particle Swarm Optimization for Airfoil Design // Robert Garrese, Xiaodong Li 1311 // 67.1 Airfoil Design 1311 // 67.2 Shape Parameterization and Flow Solver 1317 // 67.3 Optimization Algorithm 1319 // 67.4 Case Study: Airfoil Shape Optimization 1323 // 67.5 Conclusion 1329 // References 1329 // 68 Ant Colony Optimization for the Minimum-Weight Rooted Arborescence Problem // Christian Blum, Sergi Mateo Bellido 1333 // 68.1 Introductory Remarks 1333 // 68.2 The Minimum-Weight Rooted Arborescence Problem 1334 // 68.3 DP-Heur: A Heuristic Approach to the MWRA Problem 1335 // 68.4 Ant Colony Optimization for the MWRA Problem 1335 // 68.5 Experimental Evaluation 1337 // 68.6 Conclusions and Future Work 1343 // References 1343 // 69 An Intelligent Swarm of Markovian Agents // Dario Bruneo, Marco Scarpa, Andrea Bobbio, Davide Cerotti, // Marco Gribaudo 1345 // 69.1 Swarm Intelligence: A Modeling Perspective 1345 // 69.2 Markovian Agent Models 1346 // 69.3 A Consolidated Example: WSN Routing 1349 // 69.4 Ant Colony Optimization 1354 // 69.5 Conclusions 1358 // References 1359 // 70 Honey Bee Social Foraging Algorithm for Resource Allocation // Jairo Alonso Giro Ido, Nicanor Quijano, Kevin M. Passino 1361 // 70.1 Honey Bee Foraging Algorithm 1363 // 70.2 Application in a Multizone Temperature Control Grid 1365 // 70.3 Results 1371 // 70.4 Discussion 1373 // 70.5 Conclusions 1374 // References 1374 // 71 Fundamental Collective Behaviors in Swarm Robotics // Vito Trianni, Alexandre Campo 1377 // 71.1 Designing Swarm Behaviours 1378 // 71.2 Getting Together: Aggregation 1379 // 71.3 Acting Together: Synchronization 1381 // 71.4 Staying Together: Coordinated Motion 1383 // 71.5 Searching Together: Collective Exploration 1386 //

71.6 Deciding Together: Collective Decision Making 1388 // 71.7 Conclusions 1390 // References 1391 // 72 Collective Manipulation and Construction // Lynne Parker 1395 // 72.1 Object Transportation 1395 // 72.2 Object Sorting and Clustering 1401 // 72.3 Collective Construction and Wall Building 1402 // 72.4 Conclusions 1404 // References 1404 // 73 Reconfigurable Robots // Kasper St0y 1407 // 73.1 Mechatronics System Integration 1409 // 73.2 Connection Mechanisms 1410 // 73.3 Energy 1411 // 73.4 Distributed Control 1412 // 73.5 Programmability and Debugging 1417 // 73.6 Perspective 1418 // 73.7 Further Reading 1419 // References 1419 // 74 Probabilistic Modeling of Swarming Systems // Nikolaus Correli, Heiko Hamann 1423 // 74.1 From Bioligical to Artificial Swarms 1423 // 74.2 The Master Equation 1424 // 74.3 Non-Spatial Probabilistic Models 1424 // 74.4 Spatial Models: Collective Optimization 1428 // 74.5 Conclusion 1431 // References 1431 // Part G Hybrid Systems // 75 A Robust Evolving Cloud-Based Controller // Plamen P. Angelov, Igor Škrjanc, Sašo Blažic 1435 // 75.1 Overview of Some Adaptive and Evolving Control Approaches 1435 // 75.2 Structure of the Cloud-Based Controller 1437 // 75.3 Evolving Methodology for RECCo 1439 // 75.4 Simulation Study 1442 // 75.5 Conclusions 1447 // References 1448 // 76 Evolving Embedded Fuzzy Controllers // Oscar H. Montiel Ross, Roberto Sepulveda Cruz 1451 // 76.1 Overview 1452 // 76.2 Type-1 and Type-2 Fuzzy Controllers 1454 // 76.3 Host Technology 1457 // 76.4 Hardware Implementation Approaches 1458 // 76.5 Development of a Standalone IT2FC 1461 // 76.6 Developing of IT2FC Coprocessors 1466 // 76.7 Implementing a GA in an FPGA 1468 // 76.8 Evolving Fuzzy Controllers 1470 // References 1475 // 77 Multiobjective Genetic Fuzzy Systems // Hisao Ishibuchi, Yusuke Nojima 1479 // 77.1 Fuzzy System Design 1479 // 77.2 Accuracy Maximization 1482 // 77.3 Complexity Minimization 1487 //

77.4 Single-Objective Approaches 1489 // 77.5 Evolutionary Multiobjective Approaches 1491 // 77.6 Conclusion 1494 // References 1494 // 78 Bio-Inspired Optimization of Type-2 Fuzzy Controllers // Oscar Castillo 1499 // 78.1 Related Work in Type-2 Fuzzy Control 1499 // 78.2 Fuzzy Logic Systems 1500 // 78.3 Bio-Inspired Optimization Methods 1503 // 78.4 General Overview of the Area and Future Trends 1505 // 78.5 Conclusions 1506 // References 1506 // 79 Pattern Recognition with Modular Neural Networks and Type-2 Fuzzy Logic // Patricia Melin 1509 // 79.1 Related Work in the Area 1509 // 79.2 Overview of Fuzzy Edge Detectors 1510 // 79.3 Experimental Setup 1512 // 79.4 Experimental Results 1513 // 79.5 Conclusions 1515 // References 1515 // 80 Fuzzy Controllers for Autonomous Mobile Robots // Patricia Melin, Oscar Castillo 1517 // 80.1 Fuzzy Control of Mobile Robots 1517 // 80.2 The Chemical Optimization Paradigm 1518 // 80.3 The Mobile Robot 1521 // 80.4 Fuzzy Logic Controller 1522 // 80.5 Experimental Results 1523 // 80.6 Conclusions 1530 // References 1530 // 81 Bio-Inspired Optimization Methods // Fevrier Valdez 1533 // 81.1 Bio-Inspired Methods 1533 // 81.2 Bio-Inspired Optimization Methods 1533 // 81.3 A Brief History of GPUs 1535 // 81.4 Experimental Results 1535 // 81.5 Conclusions 1538 // References 1538 // Acknowledgements 1539 // About the Authors 1543 // Detailed Contents 1569 // Index 1605