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Intro; Foreword; Preface; Acknowledgements; Contents; Contributors; 1 Exploiting Subprograms in Genetic Programming; 1.1 Introduction; 1.2 Related Work; 1.3 Exploiting Subprograms; 1.3.1 BGP Strategy; 1.3.2 Exploring Model Bias; 1.3.3 Identifying Useful Subprograms; 1.4 Experiments; 1.4.1 Experimental Data, Parameters; 1.4.2 Sensitivity to Model Bias; 1.4.3 Aggregate Trace Matrices; 1.5 Conclusions and Future Work; References; 2 Schema Analysis in Tree-Based Genetic Programming; 2.1 Introduction; 2.1.1 Diversity and Evolutionary Dynamics; 2.1.2 Genetic Programming Schemata; 2.2 Methodology
2.2.1 Schema Generation2.2.2 Schema Matching; 2.3 Experimental Setup; 2.3.1 Algorithm Parameters; 2.3.2 Problem Instances; 2.3.3 Analysis Methods; 2.4 Empirical Results; 2.4.1 Standard GP; Poly-10 Problem; Tower Problem; 2.4.2 Offspring Selection GP; Poly-10 Problem; 2.5 Conclusion; References; 3 Genetic Programming Symbolic Classification: A Study; 3.1 Introduction; 3.2 AMAXSC in Brief; 3.3 MDC in Brief; 3.4 M2GP in Brief; 3.5 LDA Background; 3.6 LDA Matrix Math; 3.7 LDA Assisted Fitness Implementation; 3.7.1 Converting to Basis Space; 3.7.2 Class Clusters and Centroids
3.7.3 LDA Coefficients3.8 Artificial Test Problems; 3.9 Performance on Test Problems; 3.10 Conclusion; References; 4 Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System; 4.1 Introduction; 4.2 Methods; 4.2.1 ExSTraCS; 4.2.2 GP Integration; 4.2.2.1 GP Population Initialization; 4.2.2.2 GP Parent Selection; 4.2.2.3 GP Mating; 4.2.2.4 GP Fitness and Evaluation; 4.2.3 Datasets and Evaluation; 4.3 Preliminary Results; 4.4 Conclusions and Ongoing Work; References; 5 Applying Ecological Principles to Genetic Programming; 5.1 Introduction
5.1.1 Motivation5.1.2 Ecological Approaches in Evolutionary Algorithms; 5.1.3 Limited Resources and Eco-EA; 5.1.4 Complexifying Environments; 5.2 Methods; 5.2.1 10-Dimensional Box Problem; 5.2.2 Eco-EA Implementation; 5.2.3 Lexicase Selection Implementation; 5.2.4 Tournament Selection Implementation; 5.2.5 Configuration Details; 5.2.6 Statistical Methods; 5.2.7 Code Availability; 5.3 Results and Discussion; 5.4 Conclusions and Future Work; References; 6 Lexicase Selection with Weighted Shuffle; 6.1 Introduction; 6.2 Lexicase Selection; 6.3 Weighted Shuffle; 6.3.1 Shuffling Methods
6.3.2 Bias Metrics6.4 Experimental Setup; 6.4.1 Problems; 6.4.2 Push and PushGP; 6.5 Results; 6.6 Discussion; 6.7 Related Work; 6.8 Conclusions and Future Work; References; 7 Relaxations of Lexicase Parent Selection; 7.1 Introduction; 7.2 Lexicase Selection; 7.3 Epsilon Lexicase Selection; 7.4 Random Threshold Lexicase Selection; 7.5 MADCAP Epsilon Lexicase Selection; 7.6 Truncated Lexicase Selection; 7.7 Experimental Results; 7.8 Relation to Many-Objective Optimization; 7.9 Discussion; References; 8 A System for Accessible Artificial Intelligence; 8.1 Introduction; 8.2 The Human Engine
2.2.1 Schema Generation2.2.2 Schema Matching; 2.3 Experimental Setup; 2.3.1 Algorithm Parameters; 2.3.2 Problem Instances; 2.3.3 Analysis Methods; 2.4 Empirical Results; 2.4.1 Standard GP; Poly-10 Problem; Tower Problem; 2.4.2 Offspring Selection GP; Poly-10 Problem; 2.5 Conclusion; References; 3 Genetic Programming Symbolic Classification: A Study; 3.1 Introduction; 3.2 AMAXSC in Brief; 3.3 MDC in Brief; 3.4 M2GP in Brief; 3.5 LDA Background; 3.6 LDA Matrix Math; 3.7 LDA Assisted Fitness Implementation; 3.7.1 Converting to Basis Space; 3.7.2 Class Clusters and Centroids
3.7.3 LDA Coefficients3.8 Artificial Test Problems; 3.9 Performance on Test Problems; 3.10 Conclusion; References; 4 Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System; 4.1 Introduction; 4.2 Methods; 4.2.1 ExSTraCS; 4.2.2 GP Integration; 4.2.2.1 GP Population Initialization; 4.2.2.2 GP Parent Selection; 4.2.2.3 GP Mating; 4.2.2.4 GP Fitness and Evaluation; 4.2.3 Datasets and Evaluation; 4.3 Preliminary Results; 4.4 Conclusions and Ongoing Work; References; 5 Applying Ecological Principles to Genetic Programming; 5.1 Introduction
5.1.1 Motivation5.1.2 Ecological Approaches in Evolutionary Algorithms; 5.1.3 Limited Resources and Eco-EA; 5.1.4 Complexifying Environments; 5.2 Methods; 5.2.1 10-Dimensional Box Problem; 5.2.2 Eco-EA Implementation; 5.2.3 Lexicase Selection Implementation; 5.2.4 Tournament Selection Implementation; 5.2.5 Configuration Details; 5.2.6 Statistical Methods; 5.2.7 Code Availability; 5.3 Results and Discussion; 5.4 Conclusions and Future Work; References; 6 Lexicase Selection with Weighted Shuffle; 6.1 Introduction; 6.2 Lexicase Selection; 6.3 Weighted Shuffle; 6.3.1 Shuffling Methods
6.3.2 Bias Metrics6.4 Experimental Setup; 6.4.1 Problems; 6.4.2 Push and PushGP; 6.5 Results; 6.6 Discussion; 6.7 Related Work; 6.8 Conclusions and Future Work; References; 7 Relaxations of Lexicase Parent Selection; 7.1 Introduction; 7.2 Lexicase Selection; 7.3 Epsilon Lexicase Selection; 7.4 Random Threshold Lexicase Selection; 7.5 MADCAP Epsilon Lexicase Selection; 7.6 Truncated Lexicase Selection; 7.7 Experimental Results; 7.8 Relation to Many-Objective Optimization; 7.9 Discussion; References; 8 A System for Accessible Artificial Intelligence; 8.1 Introduction; 8.2 The Human Engine