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