标题： EvoGP：一种基于树的遗传编程的GPU加速框架 显示英文标题

标题： EvoGP: A GPU-accelerated Framework for Tree-Based Genetic Programming

摘要： 基于树的遗传编程（TGP）是一种关键的进化算法，广泛用于符号回归、特征工程和科学建模。 其高计算需求使得GPU加速对于可扩展和高性能的进化计算至关重要。 然而，TGP的GPU加速面临三个关键挑战：树编码效率低下、高度异构的遗传操作以及适应度评估中的并行性有限。 为了解决这些挑战，我们引入了EvoGP，一个全面的GPU加速TGP框架。 首先，我们设计了一种张量化编码方案，将不同结构的树表示为相同形状的张量，优化内存访问并实现高效的并行执行。 其次，我们通过利用共享的计算原语，提出了一种统一的遗传操作并行框架，并实现了专用的CUDA内核以实现可扩展的性能。 第三，我们提出了一种完全并行的符号回归适应度评估策略，利用种群级和数据级的并行性以最大化GPU利用率。 此外，我们实现了一个全面的库，提供丰富的算法算子和基准问题。 EvoGP在各种任务中进行了广泛测试，包括符号回归、分类和机器人控制，展示了其在多种应用场景中的通用性和有效性。 实验结果表明，EvoGP在最先进的基于GPU的TGP实现上实现了高达140.89倍的速度提升，同时保持或超过了基线方法的准确性。 EvoGP是开源的，可在以下地址获取：https://github.com/EMI-Group/evogp。 显示英文摘要

摘要： Tree-based Genetic Programming (TGP) is a key evolutionary algorithm widely used in symbolic regression, feature engineering, and scientific modeling. Its high computational demands make GPU acceleration essential for scalable and high-performance evolutionary computation. However, GPU acceleration of TGP faces three key challenges: inefficient tree encoding, highly heterogeneous genetic operations, and limited parallelism in fitness evaluation. To address these challenges, we introduce EvoGP, a comprehensive GPU-accelerated TGP framework. First, we design a tensorized encoding scheme to represent tree with different structures as tensors with the same shape, optimizing memory access and enabling efficient parallel execution. Second, we propose a unified parallel framework for genetic operations by leveraging shared computational primitives and implementing dedicated CUDA kernels for scalable performance. Third, we present a fully parallel fitness evaluation strategy for symbolic regression, exploiting both population-level and data-level parallelism to maximize GPU utilization. Moreover, we implement a comprehensive library to provide rich algorithm operators and benchmark problems. EvoGP is extensively tested on various tasks, including symbolic regression, classification, and robotics control, demonstrating its versatility and effectiveness across diverse application scenarios. Experimental results show that EvoGP achieves up to a 140.89x speedup over the state-of-the-art GPU-based TGP implementation, while maintaining or exceeding the accuracy of baseline methods. EvoGP is open-source and accessible at: https://github.com/EMI-Group/evogp.