Genetic Programming. Theory and Practice XX

Preface
Acknowledgments
Contents
Contributors
1 TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning
	1.1 Introduction
	1.2 Related Work
	1.3 Evolutionary Algorithm
	1.4 GraphPipelineIndividual Representation
		1.4.1 Mutation
		1.4.2 Crossover
	1.5 TPOT2 API
		1.5.1 TPOTEstimator
		1.5.2 Ensembling
	1.6 Experiment Set-Up
		1.6.1 TPOT1 Versus TPOT2
	1.7 Results and Discussion
	1.8 Conclusions
		1.8.1 Future Work
	References
2 Analysis of a Pairwise Dominance Coevolutionary Algorithm with Spatial Topology
	2.1 Introduction
	2.2 Preliminaries
		2.2.1 Coevolutionary Algorithms
		2.2.2 Spatial Topologies for PDCoEA
		2.2.3 Error Thresholds
		2.2.4 Problems
		2.2.5 MaximinHill—A Problem for Error Thresholds
	2.3 Experimental Methodology
	2.4 Experiments
		2.4.1 Setup
		2.4.2 Spatial Topology PDCoEA
		2.4.3 Payoff and Genotypic Diversity
		2.4.4 Error Threshold in STPDCoEA
	2.5 Related Work
	2.6 Conclusion
	References
3 Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming
	3.1 Introduction
	3.2 Data Sets
		3.2.1 KOMATSUNA
		3.2.2 Cell Classification
	3.3 Active Learning
	3.4 AL-GP Applied to Decision Tree GP
		3.4.1 Decision Tree GP (DT-GP)
		3.4.2 Active Learning Implementation
		3.4.3 KOMATSUNA Multi-image Results
		3.4.4 KOMATSUNA Single-Image Results
		3.4.5 Cell Classification
	3.5 AL-GP Applied to SEE-Segment
		3.5.1 SEE-Segment
		3.5.2 AL Implementation for SEE-Segment
		3.5.3 KOMATSUNA Results
	3.6 Conclusions
	References
4 How the Combinatorics of Neutral Spaces Leads Genetic Programming to Discover Simple Solutions
	4.1 Introduction
	4.2 Related Work
		4.2.1 I/O Systems
		4.2.2 RNA Studies
		4.2.3 GP on Boolean Functions
		4.2.4 Neutral Networks
		4.2.5 Our Earlier Work
	4.3 Genotypes, Phenotypes, Behavior, Fitness
		4.3.1 Discrimination of Genotypes and Phenotypes
		4.3.2 The Difference of Structural and Semantic Neutrality
	4.4 Methods
		4.4.1 Linear Genetic Programming
		4.4.2 Boolean Function Programs/Circuits
		4.4.3 Visualization Method
	4.5 The Role of Neutrality
		4.5.1 Longer Programs
		4.5.2 A New Fitness Function
	4.6 Results
		4.6.1 A Comparison of Success Rates
		4.6.2 Comparison of Search Trajectory Networks for Three Targets
		4.6.3 Simpler Solutions
	4.7 Discussion and Future Work
	References
5 The Impact of Step Limits on Generalization and Stability in Software Synthesis
	5.1 Introduction
	5.2 Background
		5.2.1 The Push Language and Interpreter
		5.2.2 Step Limits and Infinite Loops
		5.2.3 Success, Generalization, and Stability
	5.3 Methodology and Experimental Design
	5.4 Results
		5.4.1 Last Index of Zero
		5.4.2 Fuel Cost
		5.4.3 Middle Character
		5.4.4 GCD
	5.5 Discussion
		5.5.1 Stability of Evolved Programs
		5.5.2 Stability and (mis)match with Instruction Set
		5.5.3 Finding Additional Generalizing Solutions
		5.5.4 Saving Computational Effort
	5.6 Future Work
	5.7 Conclusions
	References
6 Genetic Programming Techniques  for Glucose Prediction in People  with Diabetes
	6.1 Introduction
	6.2 The Problem of Glucose Management
	6.3 Background
		6.3.1 Grammatical Evolution for Glucose Prediction
		6.3.2 Recent Techniques for Glucose Prediction Based on Grammatical Evolution
	6.4 Proposed Framework for Glucose Control
		6.4.1 Framework Description
		6.4.2 Experimental Results
	6.5 Conclusions
	References
7 Methods for Rich Phylogenetic Inference Over Distributed Sexual Populations
	7.1 Introduction
	7.2 Methods
		7.2.1 Genome Instrumentation
		7.2.2 Genealogical Inference
		7.2.3 Population Size Inference
		7.2.4 Positive Selection Inference
		7.2.5 Software and Data
	7.3 Results and Discussion
		7.3.1 Genealogical Inference
		7.3.2 Population Size Inference
		7.3.3 Positive Selection Inference
	7.4 Conclusion
	References
8 A Melting Pot of Evolution and Learning
	8.1 Introduction
	8.2 Machine Learning
		8.2.1 Binary and Multinomial Classification Through Evolutionary Symbolic Regression ch8Sipper2022esr
		8.2.2 Classy Ensemble: A Novel Ensemble Algorithm  for Classification ch8sipper2022classy
		8.2.3 EC-KitY: Evolutionary Computation Tool Kit  in Python ch8eckity2023
	8.3 Deep Learning
		8.3.1 Evolution of Activation Functions for Deep Learning-Based Image Classification ch8Lapid2022
		8.3.2 Adaptive Combination of a Genetic Algorithm  and Novelty Search for Deep Neuroevolution ch8SegalS22
	8.4 Adversarial Deep Learning
		8.4.1 An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks ch8Lapid2022Query
		8.4.2 Foiling Explanations in Deep Neural Networks ch8Vitrack2023
		8.4.3 Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors ch8Lapid2023
	8.5 Concluding Remark
	References
9 Particularity
	9.1 Overview
	9.2 Lexicase
	9.3 Variance
	9.4 Epsilon
	9.5 Batched
	9.6 Downsampled
	9.7 Informed
	9.8 Weighted
	9.9 Gradient
	9.10 Plexicase
	9.11 Hidden
	9.12 Living
	9.13 Honor
	References
10 The OpenELM Library: Leveraging Progress in Language Models for Novel Evolutionary Algorithms
	10.1 Introduction
	10.2 Background: Evolution and LLMs
	10.3 OpenELM Evolutionary Algorithms
	10.4 Language Models as Evolutionary Operators
		10.4.1 Diff Models
		10.4.2 LMX: Language Model Crossover
	10.5 Engineering Challenges
		10.5.1 OpenELM Inference Optimizations
		10.5.2 Execution of Generated Code
	10.6 OpenELM Domains
		10.6.1 Sodarace
		10.6.2 Image Generation
		10.6.3 Prompts
		10.6.4 Programming Puzzles
	10.7 Discussion
	10.8 Conclusion
	References
11 GP for Continuous Control: Teacher or Learner? The Case of Simulated Modular Soft Robots
	11.1 Introduction
	11.2 Related Works
	11.3 Background: Simulated Voxel-Based Soft Robots
		11.3.1 VSR Morphology
		11.3.2 VSR Controller
	11.4 Evolutionary Optimization of VSR Controllers
		11.4.1 Multi-layer Perceptron Optimized with a Genetic Algorithm
		11.4.2 Array of Regression Trees Optimized with GP
		11.4.3 Regression Graphs Optimized with GraphEA
	11.5 Experiments and Results
		11.5.1 Direct Evolution of the Controller
		11.5.2 Offline Imitation Learning
	11.6 Discussion
	11.7 Concluding Remarks
	References
12 Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation
	12.1 Introduction
	12.2 Related Work
	12.3 Shape-constrained Symbolic Regression
		12.3.1 Interaction Transformation Evolutionary Algorithm
	12.4 Shape Constraint Handling
		12.4.1 Single-Objective Approach
		12.4.2 Multi-objective Approach
		12.4.3 Feasible-Infeasible Two-Population Approach
	12.5 Constraint Evaluation
		12.5.1 Optimistic Approach
		12.5.2 Pessimistic Approach
	12.6 Real World Problems
		12.6.1 Twin-Screw Extruder Modeling
		12.6.2 Data Validation for Industrial Friction Performance Measurements
		12.6.3 Magnetization Curves
	12.7 Conclusion
	References
13 Phylogeny-Informed Fitness Estimation for Test-Based Parent Selection
	13.1 Introduction
	13.2 Phylogeny-Informed Fitness Estimation
		13.2.1 Phylogeny Tracking
	13.3 Methods
		13.3.1 Lexicase Selection
		13.3.2 Diagnostic Experiments
		13.3.3 Genetic Programming Experiments
		13.3.4 Statistical Analyses
		13.3.5 Software and Data Availability
	13.4 Results and Discussion
		13.4.1 Phylogeny-Informed Estimation Reduces Diversity Loss Caused by Subsampling
		13.4.2 Phylogeny-Informed Estimation Improves Poor Exploration Caused by Down-Sampling
		13.4.3 Phylogeny-Informed Estimation Can Enable Extreme Subsampling for Some Genetic Programming Problems
	13.5 Conclusion
	References
14 Origami: (un)folding the Abstraction of Recursion Schemes for Program Synthesis
	14.1 Introduction
	14.2 Recursion Schemes
		14.2.1 Fixed Point of a Linked List
		14.2.2 Functor Algebra
		14.2.3 Well-Known Recursion Schemes
	14.3 Origami
		14.3.1 How to Choose a Template
		14.3.2 Jokers to the Right: Catamorphism
		14.3.3 When You Started Off with Nothing: Anamorphism
		14.3.4 Stuck in the Middle with You: Hylomorphism
		14.3.5 Clowns to the Left of Me: Accumorphism
	14.4 Preliminary Results
	14.5 Discussion and Final Remarks
	References
15 Reachability Analysis for Lexicase Selection via Community Assembly Graphs
	15.1 Introduction
	15.2 Approach
		15.2.1 Community Assembly Graphs
		15.2.2 Calculating Stability
		15.2.3 Assumptions
		15.2.4 Reachability Analysis
	15.3 Background
		15.3.1 Lexicase Selection
		15.3.2 Community Assembly Graphs
	15.4 Proof of Concept in NK Landscapes
		15.4.1 Methods
		15.4.2 Results
	15.5 Proof of Concept in Genetic Programming
		15.5.1 Methods
		15.5.2 Results
	15.6 Conclusion
	References
16 Let's Evolve Intelligence, Not Solutions
	16.1 Introduction
	16.2 What Should We Strive For?
	16.3 What Assumptions Are Limiting Us?
		16.3.1 Posit#1: Impossible to Engineer Intelligence
		16.3.2 Posit #2: No Occam's Razor for Intelligence
		16.3.3 Posit #3: Intelligence Is Grounded
		16.3.4 Posit #4: Intelligence Is Transferable
		16.3.5 Posit #5: Intelligence Is Intrinsically Self-reinforcing
	16.4 What Do We Need?
		16.4.1 A Caveat: Intelligence == Process and/or Intelligence == Capabilities and/or Intelligence == Individual(s)
		16.4.2 The World
		16.4.3 The Drivers
		16.4.4 Models of Understanding
		16.4.5 Process of Intelligence Self-Reinforcement
	16.5 How Should We Approach It?
		16.5.1 Revisiting Reproducibility
		16.5.2 Back to the Intelligence Function
		16.5.3 Genetic Programming of Intelligence
	16.6 Conclusions
	References
Appendix  Index
Index