Many objective quality metrics have been developed during the last decade. A simple way to improve the efficiency of assessing the visual quality of images is to fuse several metrics into some combined ones. The goal of the fusion approach is to exploit the advantages of the used metrics and diminish the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets, by the combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP can determine the appropriate image quality metrics, from 21 used metrics, whose objective scores employed as predictors, in the symbolic regression model, by optimizing simultaneously two competing objectives of model ’goodness of fit’ to data and model ’complexity’. Six largest publicly available image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.