Epsilon-lexicase selection is a parent selection method in genetic programming that has been successfully applied to symbolic regression problems. Recently, the combination of random subsampling with lexicase selection significantly improved performance in other genetic programming domains such as program synthesis. However, the influence of subsampling on the solution quality of real-world symbolic regression problems has not yet been studied. In this paper, we propose down-sampled epsilon-lexicase selection which combines epsilon-lexicase selection with random subsampling to improve the performance in the domain of symbolic regression. Therefore, we compare down-sampled epsilon-lexicase with traditional selection methods on common real-world symbolic regression problems and analyze its influence on the properties of the population over a genetic programming run. We find that the diversity is reduced by using down-sampled epsilon-lexicase selection compared to standard epsilon-lexicase selection. This comes along with high hyperselection rates we observe for down-sampled epsilon-lexicase selection. Further, we find that down-sampled epsilon-lexicase selection outperforms the traditional selection methods on all studied problems. Overall, with down-sampled epsilon-lexicase selection we observe an improvement of the solution quality of up to 85% in comparison to standard epsilon-lexicase selection.
翻译:Epsilon-Lexic case 选择是基因方案编制中的一种母体选择方法,已经成功地应用于象征性回归问题。最近,随机子抽样与Lexiccase选择相结合,大大提高了其他基因方案编制领域(如方案合成)的绩效。然而,尚未研究对现实世界象征性回归问题的解决方案质量的影响。在本文中,我们建议采用低抽样的epsilon-lexicase选择,将epsilon-lexiciccase选择与随机缩微缩缩缩微缩微缩微缩选择相结合,以提高象征性回归领域的绩效。因此,我们将缩略微版的epsilon-lecase与传统选择方法进行比较,并分析其在基因方案运行过程中对人口特性的影响。我们发现,与标准的epsilon-lexlus-lection选择相比,多样性已经缩小了。我们发现,在降缩缩缩微缩微缩微缩微缩微缩微缩微缩微缩图选择方面,我们发现,在降缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩图中选择方法中,我们研究了全部选择方法。我们先行的压式选择方法。