Genetic Programming

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

Gisele Pappa Mario Giacobini Zdenek Vasicek(Editor)

Springer (Publisher)

Published on 28. March 2023

XI, 356 pages

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978-3-031-29573-7 (ISBN)

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Content

Intro
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
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Genetic Programming

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