Computational Intelligence	3
	Contents	9
	Figures	21
	Tables	25
	Algorithms	27
	Preface	31
	Part I INTRODUCTION	33
	1 Introduction to Computational Intelligence	35
	1.1 Computational Intelligence Paradigms	36
	1.1.1 Artificial Neural Networks	37
	1.1.2 Evolutionary Computation	40
	1.1.3 Swarm Intelligence	41
	1.1.4 Artificial Immune Systems	41
	1.1.5 Fuzzy Systems	42
	1.2 Short History	43
	1.3 Assignments	45
	Part II ARTIFICIAL NEURAL NETWORKS	47
	2 The Artificial Neuron	49
	2.1 Calculating the Net Input Signal	49
	2.2 Activation Functions	50
	2.3 Artificial Neuron Geometry	52
	2.4 Artificial Neuron Learning	53
	2.4.1 Augmented Vectors	55
	2.4.2 Gradient Descent Learning Rule	56
	2.4.3 Widrow-Hoff Learning Rule	57
	2.4.4 Generalized Delta Learning Rule	57
	2.4.5 Error-Correction Learning Rule	57
	2.5 Assignments	57
	3 Supervised Learning Neural Networks	59
	3.1 Neural Network Types	59
	3.1.1 Feedforward Neural Networks	60
	3.1.2 Functional Link Neural Networks	61
	3.1.3 Product Unit Neural Networks	62
	3.1.4 Simple Recurrent Neural Networks	64
	3.1.5 Time-Delay Neural Networks	66
	3.1.6 Cascade Networks	67
	3.2 Supervised Learning Rules	68
	3.2.1 The Supervised Learning Problem	68
	3.2.2 Gradient Descent Optimization	70
	3.2.3 Scaled Conjugate Gradient	77
	3.2.4 LeapFrog Optimization	81
	3.2.5 Particle Swarm Optimization	81
	3.3 Functioning of Hidden Units	81
	3.4 Ensemble Neural Networks	83
	3.5 Assignments	86
	4 Unsupervised Learning Neural Networks	87
	4.1 Background	87
	4.2 Hebbian Learning Rule	88
	4.3 Principal Component Learning Rule	90
	4.4 Learning Vector Quantizer-I	91
	4.5 Self-Organizing Feature Maps	94
	4.5.1 Stochastic Training Rule	94
	4.5.2 Batch Map	97
	4.5.3 Growing SOM	97
	4.5.4 Improving Convergence Speed	99
	4.5.5 Clustering and Visualization	101
	4.5.6 Using SOM	103
	4.6 Assignments	103
	5 Radial Basis Function Networks	105
	5.1 Learning Vector Quantizer-II	105
	5.2 Radial Basis Function Neural Networks	105
	5.2.1 Radial Basis Function Network Architecture	106
	5.2.2 Radial Basis Functions	107
	5.2.3 Training Algorithms	108
	5.2.4 Radial Basis Function Network Variations	112
	5.3 Assignments	113
	6 Reinforcement Learning	115
	6.1 Learning through Awards	115
	6.2 Model-Free Reinforcement Learning Model	118
	6.2.1 Temporal Di.erence Learning	118
	6.2.2 Q-Learning	118
	6.3 Neural Networks and Reinforcement Learning	119
	6.3.1 RPROP	119
	6.3.2 Gradient Descent Reinforcement Learning	120
	6.3.3 Connectionist Q-Learning	121
	6.4 Assignments	123
	7 Performance Issues (Supervised Learning)	125
	7.1 Performance Measures	125
	7.1.1 Accuracy	125
	7.1.2 Complexity	130
	7.1.3 Convergence	130
	7.2 Analysis of Performance	130
	7.3 Performance Factors	131
	7.3.1 Data Preparation	131
	7.3.2 Weight Initialization	138
	7.3.3 Learning Rate and Momentum	139
	7.3.4 Optimization Method	141
	7.3.5 Architecture Selection	141
	7.3.6 Adaptive Activation Functions	147
	7.3.7 Active Learning	148
	7.4 Assignments	156
	Part III EVOLUTIONARY COMPUTATION	157
	8 Introduction to Evolutionary Computation	159
	8.1 Generic Evolutionary Algorithm	160
	8.2 Representation – The Chromosome	161
	8.3 Initial Population	164
	8.4 Fitness Function	165
	8.5 Selection	166
	8.5.1 Selective Pressure	167
	8.5.2 Random Selection	167
	8.5.3 Proportional Selection	167
	8.5.4 Tournament Selection	169
	8.5.5 Rank-Based Selection	169
	8.5.6 Boltzmann Selection	170
	8.5.7 (µ +, ?)-Selection	171
	8.5.8 Elitism	171
	8.5.9 Hall of Fame	171
	8.6 Reproduction Operators	171
	8.7 Stopping Conditions	172
	8.8 Evolutionary Computation versus Classical Optimization	173
	8.9 Assignments	173
	9 Genetic Algorithms	175
	9.1 Canonical Genetic Algorithm	175
	9.2 Crossover	176
	9.2.1 Binary Representations	177
	9.2.2 Floating-Point Representation	178
	9.3 Mutation	185
	9.3.1 Binary Representations	186
	9.3.2 Floating-Point Representations	187
	9.3.3 Macromutation Operator – Headless Chicken	188
	9.4 Control Parameters	188
	9.5 Genetic Algorithm Variants	189
	9.5.1 Generation Gap Methods	190
	9.5.2 Messy Genetic Algorithms	191
	9.5.3 Interactive Evolution	193
	9.5.4 Island Genetic Algorithms	194
	9.6 Advanced Topics	196
	9.6.1 Niching Genetic Algorithms	197
	9.6.2 Constraint Handling	201
	9.6.3 Multi-Objective Optimization	202
	9.6.4 Dynamic Environments	205
	9.7 Applications	206
	9.8 Assignments	207
	10 Genetic Programming	209
	10.1 Tree-Based Representation	209
	10.2 Initial Population	211
	10.3 Fitness Function	212
	10.4 Crossover Operators	212
	10.5 Mutation Operators	214
	10.6 Building Block Genetic Programming	216
	10.7 Applications	216
	10.8 Assignments	217
	11 Evolutionary Programming	219
	11.1 Basic Evolutionary Programming	219
	11.2 Evolutionary Programming Operators	221
	11.2.1 Mutation Operators	221
	11.2.2 Selection Operators	225
	11.3 Strategy Parameters	227
	11.3.1 Static Strategy Parameters	227
	11.3.2 Dynamic Strategies	227
	11.3.3 Self-Adaptation	230
	11.4 Evolutionary Programming Implementations	232
	11.4.1 Classical Evolutionary Programming	232
	11.4.2 Fast Evolutionary Programming	233
	11.4.3 Exponential Evolutionary Programming	233
	11.4.4 Accelerated Evolutionary Programming	233
	11.4.5 Momentum Evolutionary Programming	234
	11.4.6 Evolutionary Programming with Local Search	235
	11.4.7 Evolutionary Programming with Extinction	235
	11.4.8 Hybrid with Particle Swarm Optimization	236
	11.5 Advanced Topics	238
	11.5.1 Constraint Handling Approaches	238
	11.5.2 Multi-Objective Optimization and Niching	238
	11.5.3 Dynamic Environments	238
	11.6 Applications	239
	11.6.1 Finite-State Machines	239
	11.6.2 Function Optimization	240
	11.6.3 Training Neural Networks	241
	11.6.4 Real-World Applications	242
	11.7 Assignments	242
	12 Evolution Strategies	245
	12.1 (1+1)-ES	245
	12.2 Generic Evolution Strategy Algorithm	247
	12.3 Strategy Parameters and Self-Adaptation	248
	12.3.1 Strategy Parameter Types	248
	12.3.2 Strategy Parameter Variants	250
	12.3.3 Self-Adaptation Strategies	251
	12.4 Evolution Strategy Operators	253
	12.4.1 Selection Operators	253
	12.4.2 Crossover Operators	254
	12.4.3 Mutation Operators	256
	12.5 Evolution Strategy Variants	258
	12.5.1 Polar Evolution Strategies	258
	12.5.2 Evolution Strategies with Directed Variation	259
	12.5.3 Incremental Evolution Strategies	260
	12.5.4 Surrogate Evolution Strategy	261
	12.6 Advanced Topics	261
	12.6.1 Constraint Handling Approaches	261
	12.6.2 Multi-Objective Optimization	262
	12.6.3 Dynamic and Noisy Environments	265
	12.6.4 Niching	265
	12.7 Applications of Evolution Strategies	267
	12.8 Assignments	267
	13 Differential Evolution	269
	13.1 Basic Differential Evolution	269
	13.1.1 Difference Vectors	270
	13.1.2 Mutation	271
	13.1.3 Crossover	271
	13.1.4 Selection	272
	13.1.5 General Differential Evolution Algorithm	273
	13.1.6 Control Parameters	273
	13.1.7 Geometrical Illustration	274
	13.2 DE/x/y/z	274
	13.3 Variations to Basic Differential Evolution	277
	13.3.1 Hybrid Differential Evolution Strategies	277
	13.3.2 Population-Based Differential Evolution	282
	13.3.3 Self-Adaptive Differential Evolution	282
	13.4 Differential Evolution for Discrete-Valued Problems	284
	13.4.1 Angle Modulated Differential Evolution	285
	13.4.2 Binary Differential Evolution	286
	13.5 Advanced Topics	287
	13.5.1 Constraint Handling Approaches	288
	13.5.2 Multi-Objective Optimization	288
	13.5.3 Dynamic Environments	289
	13.6 Applications	291
	13.7 Assignments	291
	14 Cultural Algorithms	293
	14.1 Culture and Artificial Culture	293
	14.2 Basic Cultural Algorithm	294
	14.3 Belief Space	295
	14.3.1 Knowledge Components	296
	14.3.2 Acceptance Functions	297
	14.3.3 Adjusting the Belief Space	298
	14.3.4 Influence Functions	299
	14.4 Fuzzy Cultural Algorithm	300
	14.4.1 Fuzzy Acceptance Function	301
	14.4.2 Fuzzified Belief Space	301
	14.4.3 Fuzzy Influence Function	302
	14.5 Advanced Topics	303
	14.5.1 Constraint Handling	303
	14.5.2 Multi-Objective Optimization	304
	14.5.3 Dynamic Environments	305
	14.6 Applications	306
	14.7 Assignments	306
	15 Coevolution	307
	15.1 Coevolution Types	308
	15.2 Competitive Coevolution	308
	15.2.1 Competitive Fitness	309
	15.2.2 Generic Competitive Coevolutionary Algorithm	311
	15.2.3 Applications of Competitive Coevolution	312
	15.3 Cooperative Coevolution	313
	15.4 Assignments	315
	Part IV COMPUTATIONAL SWARM INTELLIGENCE	317
	16 Particle Swarm Optimization	321
	16.1 Basic Particle Swarm Optimization	321
	16.1.1 Global Best PSO	322
	16.1.2 Local Best PSO	323
	16.1.3 gbest versus lbest PSO	324
	16.1.4 Velocity Components	325
	16.1.5 Geometric Illustration	326
	16.1.6 Algorithm Aspects	328
	16.2 Social Network Structures	332
	16.3 Basic Variations	335
	16.3.1 Velocity Clamping	335
	16.3.2 Inertia Weight	338
	16.3.3 Constriction Coeffcient	341
	16.3.4 Synchronous versus Asynchronous Updates	342
	16.3.5 Velocity Models	342
	16.4 Basic PSO Parameters	344
	16.5 Single-Solution Particle Swarm Optimization	346
	16.5.1 Guaranteed Convergence PSO	348
	16.5.2 Social-Based Particle Swarm Optimization	349
	16.5.3 Hybrid Algorithms	353
	16.5.4 Sub-Swarm Based PSO	358
	16.5.5 Multi-Start PSO Algorithms	365
	16.5.6 Repelling Methods	369
	16.5.7 Binary PSO	372
	16.6 Advanced Topics	374
	16.6.1 Constraint Handling Approaches	374
	16.6.2 Multi-Objective Optimization	375
	16.6.3 Dynamic Environments	378
	16.6.4 Niching PSO	382
	16.7 Applications	386
	16.7.1 Neural Networks	386
	16.7.2 Architecture Selection	388
	16.7.3 Game Learning	388
	16.8 Assignments	389
	17 Ant Algorithms	391
	17.1 Ant Colony Optimization Meta-Heuristic	392
	17.1.1 Foraging Behavior of Ants	392
	17.1.2 Stigmergy and Artificial Pheromone	395
	17.1.3 Simple Ant Colony Optimization	396
	17.1.4 Ant System	400
	17.1.5 Ant Colony System	404
	17.1.6 Max-Min Ant System	407
	17.1.7 Ant-Q	410
	17.1.8 Fast Ant System	411
	17.1.9 Antabu	412
	17.1.10 AS-rank	412
	17.1.11 ANTS	413
	17.1.12 Parameter Settings	415
	17.2 Cemetery Organization and Brood Care	416
	17.2.1 Basic Ant Colony Clustering Model	417
	17.2.2 Generalized Ant Colony Clustering Model	418
	17.2.3 Minimal Model for Ant Clustering	423
	17.3 Division of Labor	423
	17.3.1 Division of Labor in Insect Colonies	424
	17.3.2 Task Allocation Based on Response Thresholds	425
	17.3.3 Adaptive Task Allocation and Specialization	427
	17.4 Advanced Topics	428
	17.4.1 Continuous Ant Colony Optimization	428
	17.4.2 Multi-Objective Optimization	430
	17.4.3 Dynamic Environments	434
	17.5 Applications	437
	17.5.1 Traveling Salesman Problem	438
	17.5.2 Quadratic Assignment Problem	439
	17.5.3 Other Applications	443
	17.6 Assignments	443
	Part V ARTIFICIAL IMMUNE SYSTEMS	445
	18 Natural Immune System	447
	18.1 Classical View	447
	18.2 Antibodies and Antigens	448
	18.3 The White Cells	448
	18.3.1 The Lymphocytes	449
	18.4 Immunity Types	453
	18.5 Learning the Antigen Structure	453
	18.6 The Network Theory	454
	18.7 The Danger Theory	454
	18.8 Assignments	456
	19 Artificial Immune Models	457
	19.1 Artificial Immune System Algorithm	458
	19.2 Classical View Models	460
	19.2.1 Negative Selection	460
	19.2.2 Evolutionary Approaches	461
	19.3 Clonal Selection Theory Models	463
	19.3.1 CLONALG	463
	19.3.2 Dynamic Clonal Selection	465
	19.3.3 Multi-Layered AIS	465
	19.4 Network Theory Models	468
	19.4.1 Artificial Immune Network	468
	19.4.2 Self Stabilizing AIS	470
	19.4.3 Enhanced Artificial Immune Network	472
	19.4.4 Dynamic Weighted B-Cell AIS	473
	19.4.5 Adapted Artificial Immune Network	474
	19.4.6 aiNet	474
	19.5 Danger Theory Models	477
	19.5.1 Mobile Ad-Hoc Networks	477
	19.5.2 An Adaptive Mailbox	478
	19.5.3 Intrusion Detection	480
	19.6 Applications and Other AIS models	480
	19.7 Assignments	480
	Part VI FUZZY SYSTEMS	483
	20 Fuzzy Sets	485
	20.1 Formal Definitions	486
	20.2 Membership Functions	486
	20.3 Fuzzy Operators	489
	20.4 Fuzzy Set Characteristics	491
	20.5 Fuzziness and Probability	494
	20.6 Assignments	495
	21 Fuzzy Logic and Reasoning	497
	21.1 Fuzzy Logic	497
	21.1.1 Linguistics Variables and Hedges	498
	21.1.2 Fuzzy Rules	499
	21.2 Fuzzy Inferencing	500
	21.2.1 Fuzzification	501
	21.2.2 Inferencing	502
	21.2.3 Defuzzification	503
	21.3 Assignments	504
	22 Fuzzy Controllers	507
	22.1 Components of Fuzzy Controllers	507
	22.2 Fuzzy Controller Types	509
	22.2.1 Table-Based Controller	509
	22.2.2 Mamdani Fuzzy Controller	509
	22.2.3 Takagi-Sugeno Controller	510
	22.3 Assignments	510
	23 Rough Sets	513
	23.1 Concept of Discernibility	514
	23.2 Vagueness in Rough Sets	515
	23.3 Uncertainty in Rough Sets	516
	23.4 Assignments	517
	References	519
	A Optimization Theory	583
	A.1 Basic Ingredients of Optimization Problems	583
	A.2 Optimization Problem Classifications	584
	A.3 Optima Types	585
	A.4 Optimization Method Classes	586
	A.5 Unconstrained Optimization	587
	A.5.1 Problem Definition	587
	A.5.2 Optimization Algorithms	587
	A.5.3 Example Benchmark Problems	591
	A.6 Constrained Optimization	592
	A.6.1 Problem Definition	592
	A.6.2 Constraint Handling Methods	593
	A.6.3 Example Benchmark Problems	598
	A.7 Multi-Solution Problems	599
	A.7.1 Problem Definition	599
	A.7.2 Niching Algorithm Categories	600
	A.7.3 Example Benchmark Problems	601
	A.8 Multi-Objective Optimization	601
	A.8.1 Multi-objective Problem	602
	A.8.2 Weighted Aggregation Methods	603
	A.8.3 Pareto-Optimality	604
	A.9 Dynamic Optimization Problems	607
	A.9.1 Definition	607
	A.9.2 Dynamic Environment Types	608
	A.9.3 Example Benchmark Problems	610
	Index	613