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Title: Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System

Author(s): Leonardo Vanneschi,Mauro Castelli,Kristen Scott,Aleš Popovič

Publication: IJCSM

Volume: 12


Appears on pages(s):

Keywords: high performance concrete, strength prediction, artificial intelligence, genetic programming, semantic awareness

DOI: https://doi.org/10.1186/s40069-018-0300-5

Date: 11/30/2018

In 2013, our research group published a contribution in which a new version of genetic programming, called Geo-metric 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 outper-form 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 pre-dictions 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.