The genetic algorithm (GA) is a widely used optimization algorithm that mimics the process of natural selection to search and find useful solutions among sets of generations. In GA, generations are sets of possible solutions, known as chromosomes, which consist of a set of manipulating parameters, known as genes. In this study we introduce a new optimization algorithm called 'Prenatal Genetic Screening' Genetic Algorithm (PGS-GA) and investigate the comparative performance of this new algorithm against standard GA. This new evolutionary computation technique mimics the PGS procedure whereby a trained surrogate model is used to estimate the performance of each possible generated individual in GA generations and then diagnoses and replaces weak fetuses with stronger individuals. Firstly, the performance of this new algorithm is investigated on a set of known benchmark functions to assess the closeness to the global optimum in least number of generations as compared to GA. The results reveal that PGS-GA shows an efficient performance for optimization of multivariable multimodal functions and leads to noticeable improvements in convergence speed and closeness of the solution to the global optimum in all studied benchmark functions. This new optimization technique is then implemented for our automated process synthesis algorithm to generate the optimum process flowsheet for a membrane based CO2 capture process. It is shown that using PGS-GA leads to 2.3% improvement in the value of the objective function (product CO2 purity) over the GA algorithm. In addition, the presence of repeated flowsheets (structure, operating and design condition) among different solutions achieved using the algorithm starting from different randomly generated starting points that provide higher objective function values, approximately implies closeness of the solution to the global optimum. This consistency of the algorithm brings about a more robust flowsheeting algorithm that can provide higher performance solutions. Implementing more robust surrogate models, will facilitate the use of this algorithm in numerous other process design applications and beyond. (C) 2017 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.