Genetic algorithms (GAs) are metaheuristic optimization algorithms inspired by Darwinian evolution. Performing crossover, mutation and selection operations, the algorithm progresses a population of evolving candidate solutions. Selecting well designed operators and optimal parameters, GAs have exhibited a high degree of robustness in terms of finding ideal solutions to difficult optimization problems . The robustness results from the evolutionary process being able to advance solutions that would have been very difficult to predict a priori. Though, GAs often require a large number of function evaluations, resulting from typical offspring not being very "fit" solutions. Modern machine learning (ML) methods have the capacity to fit complex functions in high-dimensional feature spaces while controlling overfitting . However, the high-dimensional feature space means that finding an optimum in an ML model is not a simple task. The robustness of the GA is analyzed while accelerating its convergence through integration with an on-the-fly established Gaussian process (GP) regression model of the feature space.