Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design. While, over the years, many techniques have been developed to mitigate this issue, data vectorization, in particular, is arguably still the most attractive strategy due to the parallel nature of GP. In this work, we employ a series of benchmarks meant to compare both the performance and evolution capabilities of different vectorized and iterative implementation approaches across several existing frameworks. Namely, TensorGP, a novel open-source engine written in Python, is shown to greatly benefit from the TensorFlow library to accelerate the domain evaluation phase in GP. The presented performance benchmarks demonstrate that the TensorGP engine manages to pull ahead, with relative speedups above two orders of magnitude for problems with a higher number of fitness cases. Additionally, as a consequence of being able to compute larger domains, we argue that TensorGP performance gains aid the discovery of more accurate candidate solutions.
翻译:已知基因方案(GP)在设计上承受着昂贵的计算负担,尽管多年来开发了许多技术来缓解这一问题,但数据矢量化,特别是由于GP的平行性质,可以说仍然是最有吸引力的战略。在这项工作中,我们采用了一系列基准,旨在比较不同传量化和迭代执行方法在几个现有框架中的性能和演进能力。也就是说,TensorGP,一个在Python书写的全新的开源引擎,被证明大大受益于TensorFlow图书馆,以加速GP的域评价阶段。 提出的业绩基准表明,TensorGP发动机在加速前行,相对加速速度超过两个级别,以应对更多健身案例的问题。此外,由于能够计算更大的领域,我们辩称TensorGP业绩增益有助于发现更准确的候选解决方案。