Enabling Population-Level Parallelism in Tree-Based Genetic Programming for Comprehensive GPU Acceleration

Abstract: Tree-based Genetic Programming (TGP) is a widely used evolutionary algorithm for tasks such as symbolic regression, classification, and robotic control. Due to the intensive computational demands of running TGP, GPU acceleration is crucial for achieving scalable performance. However, efficient GPU-based execution of TGP still remains challenging, primarily due to three core issues: (1) the structural heterogeneity of program individuals, (2) the complexity of integrating multiple levels of parallelism, and (3) the incompatibility between high-performance CUDA execution and flexible Python-based environments. To address these issues, we propose EvoGP, a high-performance framework tailored for comprehensive GPU acceleration of TGP via population-level parallel execution. First, EvoGP introduces a tensorized representation that encodes variable-sized trees into fixed-shape, memory-aligned arrays, enabling uniform memory access and parallel computation across diverse individuals. Second, EvoGP adopts an adaptive parallelism strategy that dynamically combines intra- and inter-individual parallelism based on dataset size, ensuring high GPU utilization across a broad spectrum of tasks. Third, EvoGP embeds custom CUDA kernels into the PyTorch runtime, achieving seamless integration with Python-based environments such as Gym, MuJoCo, Brax, and Genesis. Comprehensive experiments show that EvoGP achieves up to 140x speedup over state-of-the-art GPU-based TGP implementations, while maintaining competitive accuracy and significantly improving scalability under large population sizes. EvoGP is open source and accessible at: https://github.com/EMI-Group/evogp.