Corrosive environments for reinforcing steel bars in concrete structures may lead to severe deterioration. Controlling the corrosion of steel reinforcement has been a major concern for researchers and engineers. To deal with this issue, a number of studies have been focused on replacing steel rebars with fiber reinforcement polymer (FRP) bars [

1,

2,

3]. The FRP bars offer several advantages including a non-corrosive nature, high tensile strength, light weight, fatigue resistance, nonmagnetic electrical insulation, and small creep deformation [

4]. The bond strength between the FRP bars and concrete is one of the crucial factors in reinforced concrete structures. Accurate prediction of the bond strength between FRP bars and concrete is important to design reliable concrete structures. Over the past few decades, numerous studies have been conducted to determine the primary factors that affect the FRP bond behavior via either beam tests or direct pullout tests [

5,

6,

7,

8,

9,

10]. These studies have presented practical models for estimating the bond strength of FRP bars in concrete. Some of the key parameters in these models are concrete compressive strength, concrete cover, bar diameter, bar position, bar surface, and embedment length. Reports have shown that the bond strength predicted by the standard design equations such as the American Concrete Institute (ACI) model is a conservative estimation of the real values [

11].

Over the last two decades, various soft computing techniques such as Artificial Neural Networks (ANNs), Decision Trees, Fuzzy Logic (FL), Adaptive Neuro Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM) have been increasingly implemented to tackle civil engineering problems [

12,

13,

14,

15,

16,

17,

18]. Some of these techniques have also been utilized for the prediction of the bond strength and shear capacity of the FRP bars in concrete. Coelho et al. [

19] presented the effectiveness of two soft computing algorithms (ANN and SVM) in analyzing the bond behavior of FRP systems inserted in the cover of concrete elements, which is commonly known as the near-surface mounted (NSM) technique. The ANN and SVM models were found to be robust and more accurate than the guideline models. Koroglu [

20] developed an ANN and the regression analysis model to predict the bond strength of FRP bars in concrete. The ANN model made accurate estimations of the bond strength of FRP bars in concrete and gave better result than the ACI and the Canadian Standards Association (CSA) models. Bashir and Ashour [

21] deployed ANNs to estimate the shear capacity of concrete members. These members were reinforced longitudinally with FRP bars without any shear reinforcement. It was found that the ANN model can predict the shear capacity of FRP reinforced concrete members with acceptable accuracy [

21]. However, one of the major disadvantages of the previously published soft computing methods is that they are not capable of providing practical prediction equations. Genetic Programming (GP) is an alternative approach that can overcome this limitation [

22,

23]. GP generates simplified prediction equations without assuming a prior form of the relationship [

24,

25,

26,

27,

28,

29]. This method has been successfully applied to the behavioral modeling of FRP concrete. For instance, Kara [

30] proposed a GP model to predict the concrete shear strength of FRP-reinforced concrete slender beams without stirrups. The GP model outperformed nearly all of the shear strength equations provided by current standard codes [

30].

Multi-Gene Genetic Programming (MGGP) is a fairly new branch of the classical GP [

31]. Unlike standard regression methods, MGGP does not require simplifying assumptions in developing the models. Despite the remarkable prediction capabilities of the MGGP approach [

31], there is only limited research focusing on the application of MGGP to civil engineering tasks. The main goal of this study is to explore the potential of the MGGP method for predicting the bond strength of FRP bars in concrete. A number of predictor variables were identified to formulate the bond strength [

32,

33,

34,

35,

36,

37]. The proposed MGGP model was compared with the widely-used ACI model. Based on the results, the MGGP model can reliably be employed for pre-design purposes.