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