The EMI Group has officially launched EvoGP (https://github.com/EMI-Group/evogp), a cutting-edge, GPU-accelerated framework for Tree-Based Genetic Programming (TGP). Built on PyTorch and leveraging custom CUDA kernels, EvoGP redefines the efficiency and scalability of genetic programming for applications in symbolic regression, classification, and policy optimization.
Revolutionizing Genetic Programming with GPU Acceleration
EvoGP is engineered to address the computational limitations of traditional Tree-Based Genetic Programming by utilizing parallel computing on GPUs. Key evolutionary operations, such as tree generation, mutation, crossover, and fitness evaluation, are fully optimized using CUDA, enabling EvoGP to achieve up to 100x speedup compared to CPU-based implementations.
Key Features of EvoGP
- Custom CUDA Kernels for Evolutionary Operations – Enhances efficiency in large-scale optimization.
- Seamless PyTorch Integration – Combines the flexibility of Python with high-performance GPU computation.
- Multi-Output Tree Support – Expands application potential in complex tasks like classification and policy optimization.
- Comprehensive Benchmark Suite – Includes Symbolic Regression, Classification, and Robotics Control (Brax).
- Advanced Genetic Operators – Supports diverse selection, mutation, and crossover methods.
A Significant Leap for Genetic Programming Research
EvoGP provides researchers and practitioners with a robust, scalable platform to explore novel TGP methodologies. By integrating evolutionary algorithms with GPU acceleration, EvoGP unlocks new possibilities in machine learning, artificial intelligence, and automated programming.
Installation & Community Engagement
The framework is open-source and available on GitHub under EMI-Group/EvoGP. Researchers and developers can contribute, share insights, and enhance the framework through GitHub Issues and Pull Requests. Future enhancements include additional GP variants, extended multi-output methods, and further computational optimizations.
Acknowledgments & Future Outlook
EvoGP builds upon foundational Genetic Programming principles pioneered by John R. Koza and incorporates advancements from PyTorch, CUDA, and symbolic regression libraries. The EMI-Group envisions EvoGP evolving into a leading GPU-accelerated platform for evolutionary computation, significantly expanding its impact in AI-driven automation and optimization.
For more details, visit the EvoGP GitHub repository: https://github.com/EMI-Group/evogp.