In an experimental study [17], nano-MgO was utilized in cement mortar to investigate its effect on the compressive strength of cement mortar samples. They reached the result that samples with nano-MgO have a denser microstructure, and for this reason, they have a higher compressive strength than plain samples. In the current research, nano-MgO as an additive is mixed with the normal concrete paste. According to previous research studies [18; 19], it has expanding properties, and an appropriate amount of this nanomaterial can have an efficient role in enhancing the mechanical properties of concrete such as permeability. In previous research studies about nano-MgO, the freeze-thaw condition was not studied, while this study covers this matter by investigating the freeze-thaw resistivity of concrete. In addition to the experimental work, it is important to propose a mathematical model that is able to predict concrete features. Gene expression programming (GEP) is a powerful numerical algorithm that is selected in the present research to fulfill this aim. GEP is basically similar to genetic algorithms (GAs) and genetic programming (GP) [20]. However, it is an extended version of GA and GP, which are inspired by [PERSON]'s theory of \"survival of the fittest\". Similar to GA and GP, GEP uses different evolutionary mechanisms such as cross over, mutation, and reproduction to generate new individuals [21]. The differences among the three algorithms stem from the nature of the individuals. In GAs, the individual has a fixed length in linear strings (chromosomes); in contrast, the individuals in the GP are nonlinear entities of different sizes and shapes (parse trees), while the individuals in GEP are made as linear strings of fixed length (the genome or chromosomes) that are then expressed as nonlinear entities of different sizes and shapes (simple diagram representations or expression trees) [22]. It has been reported that the model generated by GEP is superior to other regression techniques. In regression techniques, the function is analyzed once it is defined, whereas, in the GEP method, there is no predefined function. Moreover, GEP has a more powerful performance compared to other regression methods and Artificial Neural Network (ANN), [23]. Many studies utilize GEP for engineering problems. Deriving a model for the flow number of dense asphalt-aggregate mixtures was the topic of a research study in 2011 [24]. The results indicated that the proposed model has a high ability of evaluation of the flow number of asphalt mixtures. In Ref. [25], the GEP formulation for predicting the splitting strength of cylindrical samples was developed. Data gained from predicting the formula were compared with real experimental data and other formulas from some well-known standards. In 2012, a prediction model for the compressive strength of concrete under triaxial loading was proposed by [PERSON] et al. [26]. They used GEP for this purpose, and the derived model had a superior performance comparing with empirical models. In 2014, a design equation for the shear strength of slender RC beams was derived by GEP [27]. The proposed formulation was compared to the formulation that was recommended by several building codes, and the GEP code was found to be more accurate. In 2015, [PERSON] and [PERSON] [28] used a GEP-based method to predict the punching shear strength of normal and high strength reinforced concrete flat slabs. They applied 58 case stories that measured the punching shear strength; it was shown that the model proposed by GEP has high accuracy and ability to define the important parameters, which significantly influenced the punching shear strength. In the other study [29], different cement strength classes were used to prepare the samples, and their compressive strengths were measured. The results indicated the strong potential of GEP in predicting the compressive strength of cement mortar. In [PERSON] et al. [30], GEP is used to predict the compressive strength and electrical resistivity of concrete with natural zeolite.