Verfügbarkeit: Springer handbook of computational intelligence

Springer handbook of computational intelligence:

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Weitere Verfasser:	Kacprzyk, Janusz 1947- (HerausgeberIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Berlin [u.a.] Springer 2015
Schlagworte:	Soft Computing Aufsatzsammlung
Online-Zugang:	Inhaltstext Inhaltsverzeichnis
Beschreibung:	LVI, 1633 S. Ill., graph. Darst. 25 cm
ISBN:	3662435047 9783662435045

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Datensatz im Suchindex

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adam_text	CONTENTS LIST OF ABBREVIATIONS XLV 1 INTRODUCTION JANUSZ KACPRZYK, WITOLD PEDRYCZ 1 1.1 DETAILS OF THE CONTENTS 2 1.2 CONCLUSIONS AND ACKNOWLEDGMENTS K 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.K 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 PRADE 31 3.1 HISTORICAL BACKGROUND 32 3.2 BASIC NOTIONS OF POSSIBILITY THEORY 33 3.3 QUALITATIVE POSSIBILITY THEORY 38 3.^ QUANTITATIVE POSSIBILITY THEORY H5 3.5 SOME APPLICATIONS K9 3.6 SOME CURRENT RESEARCH LINES 53 REFERENCES 5^ 4 AGGREGATION FUNCTIONS ON [0,1] RADKO MESIAR, ANNA KOLESAROVA, MAGDA KOMORM'KOVA 61 K.L HISTORICAL AND INTRODUCTORY REMARKS 61 K.2 CLASSIFICATION OF AGGREGATION FUNCTIONS 63 PROPERTIES AND CONSTRUCTION METHODS 66 K.K CONCLUDING REMARKS 71 REFERENCES 72 5 MONOTONE MEASURES-BASED INTEGRALS ERICH P. KLEMENT, RADKO MESIAR 75 5.1 PRELIMINARIES, CHOQUET, AND SUGENO INTEGRALS 76 5.2 BENVENUTI INTEGRAL 80 HTTP://D-NB.INFO/105180003X 5.3 UNIVERSAL INTEGRALS 82 5.A- 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 PAGOLA, JAVIER MONTERO 89 6.1 CONSIDERATIONS PRIOR TO THE CONCEPT OF EXTENSION OF FUZZY SETS 90 6.2 ORIGIN OF THE EXTENSIONS 93 6.3 TYPE-2 FUZZY SETS 94 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 114 7.3 FUZZY TRANSFORM 117 7 .4 DISCRETE F-TRANSFORM 119 7.5 F-TRANSFORMS OF FUNCTIONS OF TWO VARIABLES 120 7.6 F 1 -TRANSFORM 121 7.7 APPLICATIONS 122 7.8 CONCLUSIONS 129 REFERENCES 129 8 FUZZY LINEAR PROGRAMMING AND DUALITY JAROSLAV RAMIK, 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 143 9 BASIC SOLUTIONS OF FUZZY COALITIONAL GAMES TOMAS 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 JANOS 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.^ 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 MICHAT 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 ENRICTRILLAS 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 BFA (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, RALFMIKUT, 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 TRE, SIAWOMIRZADROZNY 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 313 20.1 ROBOTICS ARID 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 \N. SWINIARSKI 331 21.1 ROUGH SETS: COMMENTS ON DEVELOPMENT 331 21.2 VAGUE CONCEPTS 332 21.3 ROUGH SET PHILOSOPHY 333 21.^ 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 DRSA 366 22.6 DRSA TO OPERATIONAL RESEARCH PROBLEMS B66 22.7 CONCLUDING REMARKS ON DRSA APPLIED TO MULTI-CRITERIA DECISION PROBLEMS 367 REFERENCES B67 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 STOWIRISKI 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 CORNELLS, 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.^ 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.^ 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, JOSE 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 SIGNORETTO, IOHAN A. K. 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 ALI A. 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 HELIE, 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 ANGELA CANGELOSI, JOSH BONGARD, MARTIN H. FISCHER, STEFANO NOLFI 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 DA MIEN 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 783 41.1 MOTIVATION 784 IFL.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 *1-1.5 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 1/1/. HAUSCHILD, FERNANDO G. LOBO 899 45.1 BASIC EDA PROCEDURE 900 45.2 TAXONOMY OF EDA MODELS 903 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 DIRKSUDHOLT 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 V. 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 LOTHARTHIELE 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 1010 REFERENCES 1011 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 1018 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. FERNANDEZ 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 ROTHLAUF 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 HOLGERH. HOOS, THOMAS STUTZLE 1085 54.1 THE NATURE AND CONCEPT OF SLS 1086 54.2 GREEDY CONSTRUCTION HEURISTICS AND ITERATIVE IMPROVEMENT 1089 54.3 SIMPLE SLS METHODS 1091 54.4 HYBRID SLS 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.B 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.^ 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, GUNTER RUDOLPH, GABRIEIE 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: M0AM0. 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.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.^ 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 EXPERIMENT 1 1246 63.5 EXPERIMENT 2 1249 63.6 CONCLUSIONS AND DISCUSSION 1251 REFERENCES 1252 64 METAHEURISTIC ALGORITHMS AND TREE DECOMPOSITION THOMAS HAMMERL, 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 OROFI 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 CARRESE, 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.IF ANT COLONY OPTIMIZATION 1354 69.5 CONCLUSIONS 1358 REFERENCES 1359 70 HONEY BEE SOCIAL FORAGING ALGORITHM FOR RESOURCE ALLOCATION JAIRO ALONSO GIRALDO, 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 NIKOLA US CORRELL, 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 SKRJANC, SASO BLAZIC 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.^ 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
any_adam_object	1
author2	Kacprzyk, Janusz 1947-
author2_role	edt
author2_variant	j k jk
author_GND	(DE-588)110363248
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dewey-tens	000 - Computer science, information, general works
discipline	Maschinenbau / Maschinenwesen Informatik
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title	Springer handbook of computational intelligence
title_alt	Handbook of computational intelligence
title_auth	Springer handbook of computational intelligence
title_exact_search	Springer handbook of computational intelligence
title_full	Springer handbook of computational intelligence Janusz Kacprzyk ... (Eds.)
title_fullStr	Springer handbook of computational intelligence Janusz Kacprzyk ... (Eds.)
title_full_unstemmed	Springer handbook of computational intelligence Janusz Kacprzyk ... (Eds.)
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