Evolutionary algorithms (EAs) are increasingly implemented on graphics processing units (GPUs) to leverage parallel processing capabilities for enhanced efficiency. However, existing studies largely emphasize the raw speedup obtained by porting individual algorithms from CPUs to GPUs. Consequently, these studies offer limited insight into when and why GPU parallelism fundamentally benefits EAs. To address this gap, we investigate how GPU parallelism alters the behavior of EAs beyond simple acceleration metrics. We conduct a systematic empirical study of 16 representative EAs on 30 benchmark problems. Specifically, we compare CPU and GPU executions across a wide range of problem dimensionalities and population sizes. Our results reveal that the impact of GPU acceleration is highly heterogeneous and depends strongly on algorithmic structure. We further demonstrate that conventional fixed-budget evaluation based on the number of function evaluations (FEs) is inadequate for GPU execution. In contrast, fixed-time evaluation uncovers performance characteristics that are unobservable under small or practically constrained FE budgets, particularly for adaptive and exploration-oriented algorithms. Moreover, we identify distinct scaling regimes in which GPU parallelism is beneficial, saturates, or degrades as problem dimensionality and population size increase. Crucially, we show that large populations enabled by GPUs not only improve hardware utilization but also reveal algorithm-specific convergence and diversity dynamics that are difficult to observe under CPU-constrained settings. Consequently, our findings indicate that GPU parallelism is not strictly an implementation detail, but a pivotal factor that influences how EAs should be evaluated, compared, and designed for modern computing platforms.

翻译：进化算法（EAs）日益在图形处理器（GPUs）上实现，以利用并行处理能力提升效率。然而，现有研究主要关注将单个算法从CPU移植到GPU所获得的原始加速比。因此，这些研究对于GPU并行化何时以及为何从根本上使EAs受益提供了有限的见解。为填补这一空白，我们研究了GPU并行化如何改变EAs的行为，而不仅仅是简单的加速指标。我们对30个基准问题上的16个代表性EAs进行了系统的实证研究。具体而言，我们在广泛的问题维度和种群规模范围内比较了CPU和GPU的执行情况。我们的结果表明，GPU加速的影响具有高度异质性，且强烈依赖于算法结构。我们进一步证明，基于函数评估次数（FEs）的传统固定预算评估方法不适用于GPU执行。相比之下，固定时间评估揭示了在小规模或实际受限的FE预算下（特别是对于自适应和面向探索的算法）无法观察到的性能特征。此外，我们识别出不同的扩展机制：随着问题维度和种群规模的增加，GPU并行化可能有益、饱和或退化。关键的是，我们表明GPU支持的大规模种群不仅提高了硬件利用率，还揭示了在CPU受限设置下难以观察到的算法特定的收敛性和多样性动态。因此，我们的发现表明，GPU并行化不仅仅是一个实现细节，而且是影响如何为现代计算平台评估、比较和设计EAs的关键因素。