Evolutionary Learning: Advances in Theories and Algorithms (Hardcover, 2019)

Part I Introduction 1 Introduction 1.1 Machine Learning 1.2 Evolutionary Learning 1.3 Multi-objective Optimization 1.4 Organization of The Book 2 Preliminaries 2.1 Evolutionary Algorithms 2.2 Pseudo-Boolean Functions 2.3 Running Time Complexity 2.4 Markov Chain Modeling 2.5 Analysis Tools Part II Analysis Methodology 3 Running Time Analysis: Convergence-based Analysis 3.1 Convergence-based Analysis: Framework 3.2 Convergence-based Analysis: Application Illustration 3.3 Summary 4 Running Time Analysis: Switch Analysis 4.1 Switch Analysis: Framework 4.2 Switch Analysis: Application Illustration 4.3 Switch Analysis: Comparison 4.4 Summary 5 Approximation Analysis: SEIP 5.1 SEIP: Framework 5.2 SEIP: Application Illustration 5.3 Summary Part III Theoretical Perspectives 6 Boundary Problems of EAs 6.1 Boundary Problem Identification 6.2 Case Study 6.3 Summary 7 Recombination 7.1 Recombination Enabled MOEAs 7.2 Case Study: Artificial Problems 7.3 Case Study: Multi-Objective Minimum Spanning Trees 7.4 Empirical Study 7.5 Summary 8 Representation 8.1 Genetic Programming Representation 8.2 Case Study: Maximum Matchings 8.3 Case Study: Minimum Spanning Trees 8.4 Empirical Study 8.5 Summary 9 Inaccurate Fitness Evaluation 9.1 Noisy Optimization 9.2 Influence of Noisy Fitness 9.3 Denoise by Threshold Selection 9.4 Denoise by Sampling 9.5 Empirical Study 9.6 Summary 10 Population 10.1 Influence of Population 10.2 Robustness of Population to Noise 10.3 Summary 11 Constrained Optimization 11.1 Usefulness of Infeasible Solutions 11.2 Effectiveness of Pareto Optimization 11.3 Summary Part IV Learning Algorithms 12 Selective Ensemble 12.1 Selective Ensemble 12.2 The POSE Algorithm 12.3 Theoretical Analysis 12.4 Empirical Study 12.5 Summary 13 Subset Selection 13.1 Subset Selection 13.2 The POSS Algorithm 13.3 Theoretical Analysis 13.4 Empirical Study 13.5 Summary 14 Subset Selection: k -Submodular Maximization 14.1 Monotone k -Submodular Function Maximization 14.2 The PO k SS Algorithm 14.3 Theoretical Analysis 14.4 Empirical Study 14.5 Summary 15 Subset Selection: Ratio Minimization 15.1 Ratio Minimization of Monotone Submodular Functions 15.2 The PORM Algorithm 15.3 Theoretical Analysis 15.4 Empirical Study 15.5 Summary 16 Subset Selection: Noise 16.1 Noisy Subset Selection 16.2 The PONSS Algorithm 16.3 Theoretical Analysis 16.4 Empirical Study 16.5 Summary 17 Subset Selection: Acceleration 17.1 The PPOSS Algorithm 17.2 Theoretical Analysis 17.3 Empirical Study 17.4 Summary