Finally, we identify two significant points for coupling GAs with ML models to design new polymer membranes. First, and most importantly, the fitness function drives the evolutionary process of the GA. It is therefore of utmost importance to select the correct fitness function to direct the GA towards the targeted design area. It is customary to train ML models on the logarithmic permeability values since it narrows the range of the data, hence resulting in more accurate models. Thus we tried the fitness function log (P CO 2 ) × α CO 2 /N 2 × α CO 2 /O 2 , aiming to maximize both the gas permeability and selectivity throughout the evolutionary process. However, this fitness function was not able to push the GA towards the targeted design area. Even though ML models trained on logarithmic permeability have slightly higher predictive power, the small numerical value of the logarithmic permeability diminishes the importance of the permeability contribution to the fitness function. On the other hand, with a fitness function that includes the absolute value of the permeability P CO 2 × α CO 2 /N 2 × α CO 2 /O 2 , we were able to push the evolutionary process toward the targeted design area and identified more than 20 new polymers that are above both CO 2 /N 2 and CO 2 /O 2 upper bounds. We attribute the superior performance of this fitness function to the fact that the absolute value of the permeability is usually two orders of magnitude higher than selectivity values. This analysis also shows improving the selectivity with the GA is much harder than the permeability since the fitness function becomes insensitive to the selectivity values when we include the gas permeability. In the future this can be avoided by normalizing the parameters where the normalization would negate this effect. Next we emphasize that, our GA was able to converge within 100 generations, since running the algorithm for an additional 100 generations did not result in any superior polymer membranes. With 100 generations and 4 initial building blocks in the first generation, a total of 17571 new unique polymer structures were created. With 79 unique genes, the number of sequences that can be generated by the GA is at least 79 4 (since longer sequences are generated throughout the evolutionary process). This suggests that the GA converges very fast, only exploring less than 1% of the possible polymer material space. We decided to use 4 genes with the initial generation because we have a relatively small gene pool. Using more building blocks with the initial generation could have created more complicated structures throughout the evolutionary process. This can be further explored when we have a larger gene pool.