This book introduces the linkage between evolutionary computation and complex networks and the advantages of cross-fertilising ideas from both fields. Instead of introducing each field individually, the authors focus on the research that sits at the interface of both fields. The book is structured to address two questions: (1) how complex networks are used to analyze and improve the performance of evolutionary computation methods? (2) how evolutionary computation methods are used to solve problems in complex networks? The authors interweave complex networks and evolutionary computing, using evolutionary computation to discover community structure, while also using network analysis techniques to analyze the performance of evolutionary algorithms. The book is suitable for both beginners and senior researchers in the fields of evolutionary computation and complex networks. Brings together the fields of evolutionary computation and complex networks, discussing them both qualitatively and quantitatively; Provides novel and efficient computational problem solving techniques that require little computer memory; Provides the pseudo-codes for the computational methods used in the book to enable readers to adopt the codes to their own problems Preface......Page 3 Contents......Page 5 Figures......Page 8 Tables......Page 12 1.1 The Basic Evolutionary Algorithm......Page 14 1.2 Differential Evolution......Page 18 1.2.1 Basic Concepts......Page 19 1.2.2 Important Variants......Page 21 1.2.3 Potential Future Research Directions......Page 22 1.3 Memetic Algorithm......Page 23 1.4 Particle Swarm Optimization......Page 25 1.4.2 Studies on PSO......Page 26 1.5 Multi-objective Evolutionary Algorithm......Page 28 References......Page 30 2.1.1 Basic Concepts and Notations of Networks......Page 34 2.1.2 Network Topology......Page 36 2.1.3 Modularity......Page 38 2.1.4 Power Law Degree Distribution......Page 40 2.1.6 Small-World Networks......Page 41 2.1.8 Community Structure......Page 42 2.2 Network Generation Methods......Page 43 2.2.2 Small-World Network Generation Model......Page 44 2.2.4 Community Network Generation Model......Page 45 References......Page 46 --- Complex Networks for EAs......Page 48 3.1 Fitness Landscapes......Page 49 3.2 Network-Based Problem Difficulty Analysis......Page 50 3.3 Local Optima Networks of Resource-Constrained Project Scheduling Problems......Page 52 3.3.1 Resource-Constrained Project Scheduling Problem......Page 53 3.3.2 Local Optima Networks of RCPSPs......Page 54 3.3.3 Properties of LONs for RCPSPs......Page 56 References......Page 61 4.1 Nonnetwork-Based Problem Difficulty Prediction Measures......Page 63 4.2 Network-Based Problem Difficulty Prediction Measures......Page 65 4.2.1 Motifs in Fitness Landscape Networks......Page 66 4.2.2 Network-Level Prediction Measure......Page 71 4.2.3 Node-Level Prediction Measure......Page 73 4.2.4 Measure Combining Network and Node-Level Prediction Measures......Page 74 4.2.5 Performance of Network-Based Prediction Measures......Page 75 References......Page 83 --- EAs for Complex Networks......Page 85 5.1 Community Detection Problems......Page 86 5.1.1 Community Structure......Page 87 5.1.2 6.1.2 Communities on Different Types of Networks......Page 89 5.1.3 Measures for Evaluating Community Structure......Page 90 5.2.1 Representation and Operators......Page 97 5.2.2 A Multi-agent Genetic Algorithm for Community Detection......Page 99 5.3 Multi-objective Evolutionary Algorithms for Community Detection......Page 109 5.3.1 MEAs-SN......Page 110 5.3.2 The Experiments of MEAs-SN......Page 113 References......Page 122 6.1 Network Robustness and Analysis......Page 125 6.1.1 Robustness Measures Based on Connectivity......Page 126 6.1.3 Robustness Measures R and Its Extensions......Page 127 6.1.5 Comparison Among These Measures......Page 129 6.2 Evolutionary Algorithms for Networks Robustness Optimization......Page 132 6.2.1 Introduction of MA-RSFMA......Page 133 6.2.2 Experimental Results of MA-RSFMA and Discussions......Page 138 6.3.1 Robustness Measures Correlations......Page 139 6.3.2 Introduction of MOEA-RSFMMA......Page 141 6.3.3 Experimental Results of MOEA-RSFMMA and Discussions......Page 143 References......Page 146 7.1 Improving Cooperation Level of Evolutionary Games on Networks......Page 149 7.2 Network Reconstruction......Page 150 7.2.1 Network Reconstruction from Profit Sequences......Page 151 References......Page 153 Index......Page 155
