Evolutionary computing (EC) has proven to be effective in solving complex optimization and robotics problems. Unfortunately, typical Evolutionary Algorithms (EAs) are constrained by the computational capacity available to researchers. More recently, GPUs have been extensively used in speeding up workloads across a variety of fields in AI. This led us to the idea of considering utilizing GPUs for optimizing ECs, particularly for complex problems such as the evolution of artificial creatures in physics simulations. In this study, we compared the CPU and GPU performance across various simulation models, from simple box environments to more complex models. Additionally, we create and investigate a novel hybrid CPU + GPU scheme that aims to fully utilize the idle hardware capabilities present on most consumer devices. The strategy involves running simulation workloads on both the GPU and the CPU, dynamically adjusting the distribution of workload between the CPU and the GPU based on benchmark results. Our findings suggest that while the CPU demonstrates superior performance under most conditions, the hybrid CPU + GPU strategy shows promise at higher workloads. However, overall performance improvement is highly sensitive to simulation parameters such as the number of variants, the complexity of the model, and the duration of the simulation. These results demonstrate the potential of creative, dynamic resource management for experiments running physics simulations on workstations and consumer devices that have both GPUs and CPUs present.

翻译：进化计算（EC）已被证明在解决复杂优化与机器人学问题方面具有显著效果。然而，典型的进化算法（EAs）常受限于研究者可用的计算资源。近年来，GPU已在人工智能多个领域中被广泛用于加速计算任务，这启发我们探索利用GPU优化进化计算的方法，特别是在物理模拟中人工生命演化等复杂问题中的应用。本研究对比了CPU与GPU在不同模拟模型中的性能表现，涵盖从简单箱体环境到复杂模型的多种场景。此外，我们设计并研究了一种新颖的CPU+GPU混合方案，旨在充分利用大多数消费级设备中闲置的硬件能力。该策略通过在GPU和CPU上并行运行模拟任务，并依据基准测试结果动态调整两者间的工作负载分配。研究结果表明，尽管CPU在多数条件下表现更优，但CPU+GPU混合策略在高负载任务中展现出潜力。然而，整体性能提升高度依赖于模拟参数，如变异体数量、模型复杂度和模拟时长。这些发现揭示了在配备GPU与CPU的工作站及消费级设备上，通过创新且动态的资源管理策略来提升物理模拟实验效率的潜在价值。