Areas of Interest and Contributions

Topics to be covered include, but is not limited to:

Methodological advances:
Algorithms, representations, and operators for GP
Surrogate models and fitness approximation in GP
Explainability and interpretability in GP
GP representations: tree-based, linear, graph-based, grammar-based
Evolutionary design
Multiple populations, coevolution, and modularity in GP
Multi-objective GP
Theoretical developments
Infrastructure and Evaluation Methodologies:
Benchmarking frameworks and reproducibility in GP
Scalable, parallel, and distributed GP systems
GP in embedded and resource-constrained environments
Tools and techniques for visualization and analysis tools of GP systems
Meta-evolution and Self-adaptation:
Self-adaptive and self-configuring GP
AutoML and GP-based pipeline optimization
Meta-learning and evolution of GP parameters and operators
Applications:
GP for scientific discovery and symbolic modeling
GP for continuous control and evolutionary robotics
Real-world applications of GP in medicine, finance, etc.
Evolvable hardware
GP for data privacy, security, and adversarial robustness
Genetic improvement and GP for software engineering
Program synthesis and inductive programming via GP
Hybrid and Unconventional Approaches:
Hybridization of GP with other machine learning methods
Neuroevolution and neural-GP hybrids
Integration of GP with swarm intelligence or other evolutionary paradigms
Unconventional GP paradigms
Open-ended GP systems