Intro
Foreword
Preface
Contents
Contributors
1 Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs
1.1 Introduction
1.2 Tangled Program Graphs
1.2.1 Learners
1.2.2 Teams
1.2.3 Graphs
1.2.4 Memory
1.3 Mechanisms for Accelerating TPG Evolution
1.3.1 Rampant Mutation
1.3.2 Multi-actions
1.4 ViZDoom Subtask Selection and Performance Evaluation
1.5 Empirical Methodology
1.5.1 Task Domains
1.5.2 Parameters
1.6 Results
1.6.1 Fitness
1.6.2 Generalization
1.6.3 Complexity

1.6.4 Details of a RAPS Solution
1.7 Conclusions
References
2 Grammar-Based Vectorial Genetic Programming for Symbolic Regression
2.1 Introduction
2.2 State of the Art
2.2.1 Vectorial Genetic Programming
2.2.2 Grammar-Based Genetic Programming
2.2.3 Feature Engineering and Feature Extraction
2.2.4 Deep Learning
2.3 Grammar-Based Vectorial Genetic Programming
2.3.1 Vectorial Tree Interpretation
2.3.2 Vectorial Symbolic Regression Grammar
2.4 Experiment Setup
2.5 Results
2.5.1 Analysis Benchmarks Group A
2.5.2 Analysis Benchmarks Group B

2.6 Discussion and Next Steps
References
3 Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming
3.1 Introduction
3.2 Software Engineering Applications of Semantically-Constrained GP
3.2.1 Automated Program Repair
3.2.2 Automated Test Generation
3.2.3 Program Synthesis
3.3 Semantic Constraints in GP
3.3.1 Strongly-Typed GP (STGP)
3.3.2 Grammar-Guided GP (GGGP)
3.3.3 Refined-Typed GP (RTGP)
3.4 Correct-by-Construction Versus Generate-and-Validate
3.5 Direct Versus Indirect Representations
3.6 A Dynamic Grammar-Guided Mapping

3.6.1 GE Mapping
3.6.2 Semantic Filter of Valid Productions
3.6.3 Dynamic and Depth-Aware Dynamic Approaches
3.7 Evaluation
3.8 Conclusions
References
4 What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?
4.1 Introduction
4.2 Methods
4.2.1 Selection Methods
4.2.2 Problems
4.2.3 Computational Substrates
4.2.4 Other Parameters
4.2.5 Phylogenetic Diversity Metrics
4.2.6 Analysis Techniques
4.2.7 Code Availability
4.3 Results and Discussion
4.3.1 Do Phylogenetic Metrics Provide Novel Information?

4.3.2 Do Phylogenetic Metrics Predict Problem-Solving Success?
4.4 Conclusion
4.5 Author Contributions
References
5 An Exploration of Exploration: Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality
5.1 Introduction
5.2 Exploration Diagnostic
5.3 Lexicase Selection
5.3.1 Epsilon Lexicase Selection
5.3.2 Down-Sampled Lexicase Selection
5.3.3 Cohort Lexicase Selection
5.3.4 Novelty-Lexicase Selection
5.4 Diagnosing the Exploratory Capacity of Lexicase Selection and Its Variants