Evolutionary and Swarm Intelligence Algorithms (Studies in Computational Intelligence (779))

"This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike." -- prové de l'editor Preface 6 Contents 8 About the Editors 9 Swarm and Evolutionary Computation 11 1 Swarm Intelligence 12 1.1 Self-Organization 13 1.2 Division of Labor 13 2 Evolutionary Computation 14 2.1 Members of Evolutionary Computation 16 3 Discussion 17 4 Concluding Remarks 18 References 19 Particle Swarm Optimization 20 1 Particle Swarm Optimization 20 1.1 Motivation 21 1.2 Particle Swarm Optimization Process 23 1.3 Understanding Update Equations 24 2 Particle Swarm Optimization Parameters 26 3 A Worked-Out Example 26 References 32 Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization 33 1 Introduction 33 2 ABC Algorithm 35 2.1 ABC Algorithm for Single-Objective Constrained Optimization 37 2.2 ABC Algorithm for Multi-objective Optimization 39 2.3 ABC Algorithm for Combinatorial Optimization 41 3 An Application of ABC Algorithm to Colormap Quantization 42 3.1 Experiments 43 4 Conclusion 47 References 47 Spider Monkey Optimization Algorithm 50 1 Spider Monkey Optimization 50 1.1 Motivation 51 1.2 Spider Monkey Optimization Process 52 2 Analyzing SMO 57 3 Parameters of SMO 58 4 Performance Analysis of SMO 58 5 A Worked-Out Example 58 6 Conclusion 65 References 66 Genetic Algorithm and Its Advances in Embracing Memetics 67 1 Introduction 68 2 Preliminary 69 2.1 Genetic Algorithm 69 2.2 Memetic Algorithm 72 2.3 Memetic Computation 73 3 Probablistic Memetic Algorithm 74 3.1 Theoretical Upper Bound on Local Search 74 3.2 Estimation of Local Search Upper Bound 75 4 Meme-Centric Computing Paradigm for Search 76 4.1 Meme as Instruction for Task Assignment 77 4.2 Learning and Selection of the Identified Meme 78 5 Case Studies 79 5.1 Capacitated Arc Routing Problem 80 5.2 Experimental Configuration 81 5.3 Results 84 6 Transferrable Memes in Evolutionary Bilevel Optimization 84 7 Conclusion 87 References 88 Constrained Multi-objective Evolutionary Algorithm 91 1 Introduction 91 2 Evolutionary Multi-objective Optimization (EMO) 93 2.1 EMO Algorithms 94 3 Constrained EMO Algorithms 95 3.1 Penalty Function Method 95 3.2 Deb's Parameter-Less Method 96 3.3 Fonseca and Fleming's Method 98 4 Constrained Multi-objective Test Problems 99 4.1 Specific Two-Objective Problems 99 4.2 Two-Objective CTP Problems 104 4.3 Scalable Constrained Test Problem Generator 109 4.4 Scalable Constrained DTLZ Problems Using Constraint Surface Concept 111 4.5 Constrained Test Problems for Many-Objective Optimization 114 4.6 Constrained Problems of Type-2 116 4.7 Constrained Problems of Type-3 118 4.8 Other Constrained Problems 119 5 Future Studies in Constrained EMO 120 6 Conclusions 121 References 122 Genetic Programming for Classification and Feature Selection 125 1 Introduction 125 1.1 The Emergence of Genetic Programming 125 1.2 Genetic Programming: The Special Encoding Scheme 126 1.3 Classification and Feature Selection 127 1.4 Genetic Programming for Classification and Feature Selection: A Simple Example 128 2 Genetic Programming for Classification and Feature Selection 132 2.1 GP-Based Schemes for Feature Selection and Classification 133 2.2 Multi-tree Classifiers Using Genetic Programming muni2004 134 2.3 Genetic Programming for Simultaneous Feature Selection and Classifier Design muni2006 136 2.4 A Multiobjective GP-Based Ensemble for Feature Selection and Classification nag2015b 137 3 Some Remarks 141 3.1 GP for Classification and Feature Selection 141 3.2 Parameter Dependency 144 4 Conclusion 146 References 146 Genetic Programming for Job Shop Scheduling 148 1 Introduction 148 2 Background 150 2.1 Dispatching Rules 151 2.2 Meta-Heuristic Methods 151 3 Genetic Programming for Job Shop Scheduling 153 3.1 Representations of Dispatching Rules 153 3.2 Search Mechanisms 157 4 Performance Enhancement Revisited 160 4.1 Experimental Settings 160 4.2 Training Simulation Replications 161 4.3 Smooth and Nonsmooth Evolved Dispatching Rules 162 4.4 Surrogate Model 163 4.5 Multi-objective 166 4.6 Simplification 166 5 Conclusions 168 References 169 Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems 173 1 Introduction 174 2 Evolutionary Fuzzy Systems: Taxonomy and Analysis 175 2.1 Evolutionary Learning and Tuning of FRBSs' Components 176 2.2 Approaches for Optimizing Several Objectives 178 2.3 Novel Fuzzy Representations 180 3 The Use of Evolutionary Fuzzy Systems for Intrusion Detection Systems 181 3.1 Background on Intrusion Detection Systems 181 3.2 Related Work for Fuzzy Systems in IDS 182 4 Case Study: Addressing Intrusion Detection Systems with Multi-objective Evolutionary Fuzzy Systems 184 4.1 Benchmark Data: KDDCUP'99 Problem 184 4.2 Algorithms and Parameters 185 4.3 Performance Metrics for IDS 186 4.4 Experimental Results 188 5 Conclusions and Future Perspectives 189 References 190 Front Matter ....Pages i-x Swarm and Evolutionary Computation (Jagdish Chand Bansal, Nikhil R. Pal)....Pages 1-9 Particle Swarm Optimization (Jagdish Chand Bansal)....Pages 11-23 Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization (Bahriye Akay, Kader Demir)....Pages 25-41 Spider Monkey Optimization Algorithm (Harish Sharma, Garima Hazrati, Jagdish Chand Bansal)....Pages 43-59 Genetic Algorithm and Its Advances in Embracing Memetics (Liang Feng, Yew-Soon Ong, Abhishek Gupta)....Pages 61-84 Constrained Multi-objective Evolutionary Algorithm (Kalyanmoy Deb)....Pages 85-118 Genetic Programming for Classification and Feature Selection (Kaustuv Nag, Nikhil R. Pal)....Pages 119-141 Genetic Programming for Job Shop Scheduling (Su Nguyen, Mengjie Zhang, Mark Johnston, Kay Chen Tan)....Pages 143-167 Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems (S. Elhag, A. Fernández, S. Alshomrani, F. Herrera)....Pages 169-190