Genetic Programming: 26th European Conference, EuroGP 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings

Preface
Organization
Contents
Long Presentations
A Self-Adaptive Approach to Exploit Topological Properties of Different GAs' Crossover Operators
	1 Introduction
	2 Fundamental Concepts
		2.1 Crossover
		2.2 Convex Combination, Convex Hull, and Convex Search
	3 Related Works
	4 Methodology
		4.1 Dynamic Diversity Maintenance
		4.2 Self-adaptive Crossover
	5 Experimental Settings
	6 Experimental Results
	7 Conclusions
	References
A Genetic Programming Encoder for Increasing Autoencoder Interpretability
	1 Introduction
		1.1 Structure
	2 Background and Related Work
		2.1 Non-linear Dimensionality Reduction
		2.2 Evolutionary Computation for Dimensionality Reduction
		2.3 Genetic Programming for Autoencoding
	3 Proposed Method: GPE-AE
		3.1 GP Representation of Encoder
		3.2 Fitness Evaluation
		3.3 Decoder Architecture
	4 Experiment Design
		4.1 Comparison Methods
		4.2 Evaluation Measures
		4.3 Datasets
	5 Results
	6 Further Analysis
	7 Conclusions
	References
Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows
	1 Introduction
	2 Background and Related Work
		2.1 Genetic Programming in Physics Applications
		2.2 Machine Learning for Particle-Laden Flows
	3 Proposed Methods
		3.1 Graph Networks
		3.2 Genetic Programming
	4 Experiment Design
		4.1 Data Generation: Simulation of Particle-Laden Flows
		4.2 Data Preprocessing
		4.3 Algorithm Settings
	5 Results and Analysis
		5.1 Overall Algorithm Performance
		5.2 Explainability of Equations
		5.3 Validation of Symbolic Models
	6 Conclusion and Future Work
	References
Phenotype Search Trajectory Networks for Linear Genetic Programming
	1 Introduction
	2 The LGP System
		2.1 Boolean LGP Algorithm
		2.2 Genotype, Phenotype, and Fitness
	3 Kolmogorov Complexity
	4 Sampling and Metrics Estimation
	5 Search Trajectory Networks
		5.1 General Definitions
		5.2 The Proposed STN Models
		5.3 Network Visualisation
		5.4 Comparing Three Targets with Increasing Difficulty
	6 Discussion
	References
GPAM: Genetic Programming with Associative Memory
	1 Introduction
	2 Related Work
		2.1 Symbolic Regression and Genetic Programming
		2.2 Efficient Processing of DNNs
		2.3 Weight Compression
	3 Proposed Method
		3.1 The GPAM Approach
		3.2 GPAM for Weight Generation
	4 Results for Symbolic Regression Benchmarks
		4.1 Benchmarks
		4.2 Setup
		4.3 Memory Sizing
		4.4 Role of Constants in GPAM
	5 Results for Weight Generation
	6 Discussion and Conclusions
	References
MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning
	1 Introduction
	2 Related Work
		2.1 Semantic GP
		2.2 GP-Based Ensemble Learning
		2.3 Quality Diversity Optimization
	3 The Proposed Ensemble Learning Algorithm
		3.1 The Overall Framework
		3.2 Angle-Based Dimensionality Reduction
		3.3 Reference Semantic Points
	4 Experiment Settings
		4.1 Datasets
		4.2 Experimental Protocol
		4.3 Parameter Settings
		4.4 Benchmark Dimensionality Reduction Methods
	5 Experimental Results
		5.1 Comparisons of MAP-Elites Using Different Dimensionality Reduction Methods
		5.2 Impact of Using Reference Points
		5.3 Comparison with Other Machine Learning and Symbolic Regression Methods
	6 Conclusions
	References
Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming
	1 Introduction
	2 Related Work
	3 Denoising Autoencoder LSTM
		3.1 Model Building and Sampling
		3.2 A New Denoising Strategy: Levenshtein Tree Edit
	4 Experiments
		4.1 Experimental Setup
		4.2 Prediction Quality
		4.3 Analyzing the Search Behavior
	5 Conclusions and Future Work
	References
Context Matters: Adaptive Mutation for Grammars
	1 Introduction
	2 Background
		2.1 Grammar-Based Genetic Programming
		2.2 Adaptive Mutation Rate
		2.3 Grammar-Design
	3 Adaptive Facilitated Mutation
		3.1 Grammar Design for Adaptive Facilitated Mutation
	4 Experimental Setup
	5 Results
	6 Conclusion
		6.1 Future Work
	References
A Boosting Approach to Constructing an Ensemble Stack
	1 Introduction
	2 Related Work
	3 Evolving an Ensemble Stack Using Boosting
		3.1 The Boosting Ensemble Stack Algorithm
		3.2 Evaluating an Ensemble Stack Post Training
		3.3 Using an Extremely Large Number of Bins
	4 Experimental Methodology
	5 Results
		5.1 Small Scale Classification Tasks
		5.2 Large Scale Classification Task
	6 Conclusion
	References
Adaptive Batch Size CGP: Improving Accuracy and Runtime for CGP Logic Optimization Flow
	1 Introduction
	2 Cartesian Genetic Programming
		2.1 Representation
		2.2 Evolutionary Process
	3 Methodology
		3.1 Definitions
		3.2 Adaptive Batch Size CGP
		3.3 Experimental Protocol
	4 Results
	5 Conclusion
	References
Faster Convergence with Lexicase Selection in Tree-Based Automated Machine Learning
	1 Introduction
	2 Related Work
	3 Methods
		3.1 Review of TPOT
		3.2 Parent Selection Algorithms
	4 Experimental Set-Up
		4.1 Datasets
		4.2 Implementation
		4.3 Evaluating Convergence
		4.4 Exploration of Pipelines
	5 Results
		5.1 DIGEN Datasets
		5.2 ANGES Datasets
	6 Discussion
	References
Using FPGA Devices to Accelerate Tree-Based Genetic Programming: A Preliminary Exploration with Recent Technologies
	1 Introduction
	2 Related Work
	3 Accelerator Architecture
		3.1 Program Memory
		3.2 Program Compiler
		3.3 Program Evaluator
	4 Design of Experiments
		4.1 Comparison Metrics
		4.2 Primitive Sets
		4.3 Program Generation
		4.4 Fitness Cases
	5 Results
	6 Current Limitations and Potential Optimizations
		6.1 Current Limitations
		6.2 Potential Optimizations
	7 Conclusion
	References
Memetic Semantic Genetic Programming for Symbolic Regression
	1 Introduction
	2 Semantic GP
		2.1 Library Building and Searching
	3 Memetic Algorithms
	4 Memetic Semantic for Symbolic Regression
		4.1 Algorithm
		4.2 Local Tree Improvement
	5 Experimental Setup
	6 Results
	7 Related Work
	8 Conclusion
	References
Grammatical Evolution with Code2vec
	1 Introduction
	2 Background
		2.1 Grammatical Evolution
		2.2 Code2vec
	3 Methods
		3.1 ClusterBooster
		3.2 ClusterSelection
	4 Experiments
		4.1 Benchmarks
		4.2 Experimental Setup
		4.3 Results
	5 Conclusion
	References
Short Presentations
Domain-Aware Feature Learning with Grammar-Guided Genetic Programming
	1 Introduction
	2 Related Work
		2.1 Genetic-Programming-Based Feature Learning
		2.2 Domain-Aware Feature Learning and Aggregation Incorporation
	3 Method
		3.1 Domain Knowledge M3GP
		3.2 Domain Knowledge and Aggregation M3GP
	4 Evaluation
		4.1 Datasets
		4.2 Implementation Details
		4.3 Experiment Details
	5 Results
	6 Conclusion
	References
Genetic Improvement of LLVM Intermediate Representation
	1 Introduction
	2 Background
	3 Mutating LLVM IR
		3.1 Representation
		3.2 LLVM IR define Functions
		3.3 Mutable LLVM IR
		3.4 Compiling C/C++ etc. to Generate LLVM IR
		3.5 Selecting Which LLVM IR to Optimise
		3.6 Deleting LLVM IR
	4 Fitness Function
		4.1 Test Cases for Google's OLC and Uber's H3: GB Post Codes
		4.2 Counting Instructions with perf stat -e instructions -x,
		4.3 Sandboxing to Prevent Running Mutations Causing Harm
		4.4 Timeouts to Stop Poor Mutants Delaying Search
		4.5 Limiting Output Size to Avoid Filling Disk or Exceeding Disk Quota
	5 Hillclimbing Search
	6 Results
	7 Discussion
		7.1 Types of Improvement Found
		7.2 Discussion: Future Work, Co-evolution, Perf, Fitness Landscape
	8 Conclusions
	References
Spatial Genetic Programming
	1 Introduction
	2 Related Literature
	3 Spatial Genetic Programming
		3.1 The Cost Function
		3.2 Outputs, Termination Conditions and Model Execution
		3.3 Evolution of Models and the Genetic Operators
		3.4 Conditional Return Statements
	4 Experiments and Results
		4.1 Case Study: Classic Control Problems
		4.2 Case Study: Custom Toy Problems
		4.3 Impact of a Spatial Crossover on the Evolution of Programs
	5 Conclusion
	References
All You Need is Sex for Diversity
	1 Introduction
	2 The PIMP Approach
	3 Motivation
	4 Methodology
		4.1 Measures
		4.2 Statistical Tests
	5 Results
	6 Discussion and Additional Remarks
	7 Conclusion
	References
On the Effects of Collaborators Selection and Aggregation in Cooperative Coevolution: An Experimental Analysis
	1 Introduction and Related Works
	2 A General Scheme for CC
	3 Case Studies
		3.1 Toy Problems
		3.2 Symbolic Regression
		3.3 Neuroevolution
	4 Experimental Analysis
		4.1 Toy Problems
		4.2 Symbolic Regression
		4.3 Neuroevolution
	5 Concluding Remarks
	References
To Bias or Not to Bias: Probabilistic Initialisation for Evolving Dispatching Rules
	1 Introduction
	2 Background
		2.1 Unrelated Machines Environment
		2.2 Designing Dispatching Rules with Genetic Programming
	3 Probabilistic Individual Initialisation
	4 Experimental Analysis
		4.1 Benchmark Setup
		4.2 Results
	5 Analysis
		5.1 Node Probabilities
		5.2 Method Ranking
	6 Conclusion
	References
MTGP: Combining Metamorphic Testing and Genetic Programming
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Metamorphic Testing
		3.2 Benchmark Problems and Metamorphic Relations
		3.3 Incorporating Metamorphic Testing in GP
	4 Experiments and Discussion
		4.1 Experimental Setup
		4.2 Results and Discussion
	5 Conclusion
	References
Interacting Robots in an Artificial Evolutionary Ecosystem
	1 Introduction
	2 Related Work
	3 Method
		3.1 Evolutionary Algorithm
		3.2 The Robots
		3.3 Genotype Encodings
	4 Experimental Setup
	5 Analysis
		5.1 Fitness Analysis
		5.2 Trait Analysis
		5.3 Differences Between Offspring Spawn Location
	6 Conclusion
	References
Author Index