In 2013, our research group published a contribution in which a new version of genetic programming, called Geometric Semantic Genetic Programming (GSGP), was fostered as an appropriate computational intelligence method for predicting the strength of high-performance concrete. That successful work, in which GSGP was shown to outperform the existing systems, allowed us to promote GSGP as the new state-of-the-art technology for high-performance concrete strength prediction. In this paper, we propose, for the first time, a novel genetic programming system called Nested Align Genetic Programming (NAGP). NAGP exploits semantic awareness in a completely different way compared to GSGP. The reported experimental results show that NAGP is able to significantly outperform GSGP for high-performance concrete strength prediction. More specifically, not only NAGP is able to obtain more accurate predictions than GSGP, but NAGP is also able to generate predictive models with a much smaller size, and thus easier to understand and interpret, than the ones generated by GSGP. Thanks to this ability of NAGP, we are able here to show the model evolved by NAGP, which was impossible for GSGP.

中文翻译：

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使用基于对齐的遗传编程系统进行准确的高性能混凝土预测

2013 年，我们的研究小组发表了一篇论文，其中培育了一种新版本的遗传编程，称为几何语义遗传编程 (GSGP)，作为一种合适的计算智能方法来预测高性能混凝土的强度。这项成功的工作表明 GSGP 优于现有系统，使我们能够将 GSGP 推广为用于高性能混凝土强度预测的最新技术。在本文中，我们首次提出了一种称为嵌套对齐遗传编程（NAGP）的新型遗传编程系统。与 GSGP 相比，NAGP 以完全不同的方式利用语义意识。报告的实验结果表明，在高性能混凝土强度预测方面，NAGP 能够显着优于 GSGP。进一步来说，NAGP 不仅能够获得比 GSGP 更准确的预测，而且 NAGP 还能够生成比 GSGP 生成的模型尺寸小得多的预测模型，因此更容易理解和解释。由于NAGP的这种能力，我们能够在这里展示由NAGP进化的模型，这对于GSGP是不可能的。