## 1 Introduction  
Hexaloid (or common) wheat (_Tritcium aestivum_ L.) is the third largest important cereal after rice and maize and occupies almost one-fifth of the world's cultivated land. The nearly 1% annual genetic gains in five major food crops including wheat has been the result of conventional breeding methodologies [1], which exploited the existing genetic variation. [PERSON] and [PERSON] [2] indicated that the current genetic gains are insufficient to increase crop production by 70% by 2050 that is needed to feed the increasingly growing demand. To address challenges such as the increasing global population, and global climate change with changes in the intensity and patterns of abiotic and biotic stresses, plant breeders must leverage information from recent advancement in rapid and precise plant breeding, facilitated by genome-editing. Biotechnological tools such as in vitro culture can be considered as a solution for this aim. Therefore, there is a need to adjust in vitro genotype-independent shoot regeneration in wheat [3]. In vitro plant regeneration is mainly dependent on exogenous and endogenous phytohormones [4]. Genotypes and the type of explant are different in their levels of endogenous phytohormones [4,5]. Indeed, in vitro shoot regeneration is controlled by the exogenous cytokinin and auxin balances, and also by concentrations of endogenous phytohormones [4,5]. The levels of endogenous phytohormones regulate the in vitro explant differentiation and are assumed to be the major variation between various genotypes and explants with different degrees of competence [4]. The interactions among the exogenous and endogenous phytohormones and their effects on in vitro organogenesis need to be extensively studied [6,7], with the goal of understanding phytohormone metabolism signaling and their roles in in vitro organogenesis. In the context of multiple endogenous and exogenous phytohormones, in vitro organogenesis can be viewed as a multi-variable procedure impacted by different phytohormones such as auxins, cytokinin, and their interaction [8]. Also, in vitro organogenesis consists of non-linear and non-deterministic developmental processes [9]. Conventional computational techniques are inefficient to model non-linearity in complex systems that exist in plant tissue culture [10, 11, 12]. Artificial intelligence (AI) models such as artificial neural networks (ANNs) and fuzzy logic have proven to be appropriate approaches for modeling the non-linearity and ill-defined systems in in vitro culture [12]. Examples include the use of the adaptive neuro-fuzzy inference system (ANFIS) in modeling somatic embryogenesis of chrysanthemum [12] and the use of radial basis function (RBF) for modeling of in vitro shoot proliferation in pear rootstock [13]. Among ANN models, the generalized regression neural network (GRNN), proposed by [PERSON] [13], can efficiently solve the non-linear problems due to the simplicity of network structure, the strong non-linear mapping capability, and high fault tolerance and robustness [14]. While the GRNN model has been frequently used in several fields to solve short-term load forecasting [15], pattern recognition [16], the modeling and monitoring of batch processes [17], medicinal chemistry [18], exchange rates forecasting [19], and wind speed forecasting [20], it has been used in plant tissue culture modeling very rarely. Establishment of working concentrations of tissue culture medium ingredients is a tedious task and requires execution of complex experimental designs with numerous independent factors. This research investigated whether AI can predict the outcomes of shoot regeneration based on influencing factors stably and accurately. We further elucidated whether a genetic algorithm (GA) can optimize a solution for wheat shoot regeneration. This study shows high stability and accuracy of using the GRNN in modeling in vitro shoot regeneration of wheat. However, the weakness of using ANNs such as GRNN is that it is hard to obtain an optimized solution. [PERSON], et al.