Genetic Programming Theory and Practice XVIII

✍ Scribed by Wolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, Bill Worzel

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 220
• Series: Genetic and Evolutionary Computation
• Edition: 1
• Category: Library

✦ Synopsis

This book, written by the foremost international researchers and practitioners of genetic programming (GP), explores the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP.  In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

✦ Table of Contents

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
5.4.1 Lexicase Selection Out-Explores Tournament Selection
5.4.2 The Exploratory Capacity of Lexicase Selection Degrades as We Increase Diagnostic Cardinality
5.4.3 Increasing Population Size Can Improve Lexicase Selection's Exploratory Capacity
5.4.4 Relaxing Lexicase Selection's Elitism Can Improve Exploration
5.4.5 Down-Sampling Degrades Lexicase Selection's Exploratory Capacity
5.4.6 Cohort Partitioning Degrades Lexicase Selection's Exploratory Capacity
5.4.7 Cohort Lexicase Out-Explores Down-Sampled Lexicase
5.4.8 Novelty Test Cases Degrade Lexicase Selection's Exploratory Capacity
5.5 Conclusion
5.6 Data and Software Availability
References
6 Feature Discovery with Deep Learning Algebra Networks
6.1 Introduction
6.2 ARC Background
6.3 Regression in Brief
6.4 Classification in Brief
6.5 Industrial Regression Classification
6.6 Theoretical Test Problems—Classification
6.7 Base Performance on the Theoretical Classification Problems
6.8 Thin 2-Layer ARC Performance on the Theoretical Classification Problems
6.9 Ultrathin 8-Layer ARC Performance on the Theoretical Classification Problems
6.10 Wide 2-Layer ARC Performance on the Theoretical Classification Problems
6.11 Wide 8-Layer ARC Performance on the Theoretical Classification Problems
6.12 Conclusion
References
7 Back to the Future—Revisiting OrdinalGP and Trustable Models After a Decade
7.1 Introduction
7.2 In the Beginning
7.2.1 Model Complexity—Getting What You Measure
7.2.2 ParetoGP—Simplicity and Accuracy
7.2.3 Secondary and Alternating Objectives
7.2.4 OrdinalGP—Failing Forward
7.2.5 Ensembles—Trustable Models and Active Design-of-Experiments
7.2.6 Data Balancing
7.3 BalancedGP
7.3.1 DataSubsetSize
7.3.2 BalancedSample
7.3.3 BalancedGP
7.4 Ensembles
7.4.1 Introduction to Ensembles
7.4.2 Ensembles of the Future
7.5 Conclusions
References
8 Fitness First
8.1 Introduction
8.2 Faster Genetic Programming via Parallel Hardware
8.2.1 Multiple CPU Cores
8.2.2 Multiple Fitness Cases Simultaneously
8.2.3 Fitness First
8.3 Avoiding Effort Wasted on Poor Fitness Individuals
8.4 Asymmetry of GP Subtree Crossover
8.4.1 Last Child Inplace Dad-Less Crossover
8.5 Efficiency of Memmove V. Memcpy
8.6 Speed of Fitness First and Incremental Fitness
8.7 Mathematical Model of Number of Parents
8.7.1 Number of Parents Initially and in Diverse Populations
8.8 Multi-threading Implementation Issues
8.8.1 Idle Threads
8.8.2 Future Work: Predicting Thread Execution Time
8.9 Conclusions
References
9 Designing Multiple ANNs with Evolutionary Development: Activity Dependence
9.1 Introduction
9.2 Multiple Problem Solving ANNs
9.3 The Neuron Model
9.3.1 Soma Program Inputs and Outputs
9.3.2 Dendrite Program Inputs and Outputs
9.3.3 Developing the Brain and Evaluating the Fitness
9.3.4 Extracting Conventional ANNs from the Brain
9.3.5 Activity Dependence
9.3.6 Model Parameters
9.4 Experiments
9.5 Discussion and Further Work
References
10 Evolving and Analyzing Modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules)
10.1 Introduction
10.2 Evolving Modules in Genetic Programming
10.3 GLEAM
10.3.1 Initializing the Library
10.3.2 Referencing the Modules
10.3.3 Updating the Library
10.4 GLEAM as a Platform for Testing
10.5 Experiments and Analysis
10.5.1 Experimental Set-Up
10.5.2 Using GLEAM to Evolve Modular Programs
10.5.3 Using GLEAM as a Testing Platform
10.5.4 Modular Usage in GLEAM
10.6 Conclusions
References
11 Evolution of the Semiconductor Industry, and the Start of X Law
11.1 Introduction
11.2 Human Knowledge Constraint
11.3 Evolutionary Concepts
11.3.1 What Evolutionary Components Can Be Applied to the Semiconductor Industry?
11.3.2 What Else Does Evolution, and Economic Models Tell Us?
11.3.3 How Can Ascension Occur?
11.3.4 What About the Human Element?
11.4 Final Discussion and Thoughts
11.4.1 What are the Mechanisms for Continued Exponential Growth?
11.5 Conclusion
References
Index