In the present investigation, the output of the targeted single-variable function represents the total nMSE between the ANN predictions and actual experiments, with 𝐷 as the input variable, which was initially set within the range of This result shows a deviation of 0.13% from the value derived using the calculation script, indicating a very close match. However, the downside is that the GA required more time to complete the optimization, with the target function being called 2,500 times during the process. This extended optimization time is mainly due to the inherent characteristics of the algorithm. GA employs a probabilistic strategy by randomly generating candidate solutions and iteratively refining them through selection, crossover, and mutation operations. It is also noted that an exhaustive search for optimizing the GA algorithm is out of the scope of this paper and several GA algorithms are expected to perform more efficiently .