• Genetic: Different genetic algorithm implementations are available . In this paper, we consider the evolutionary algorithm implemented in the Distributed Evolutionary Algorithm in Python (DEAP) package . DEAP handles categorical variables by label encoding them. The solving process involves two phases -an initial sampling and an iterative sampling phase. It first initiates samples randomly within the search domain. Iterative samples are then generated through crossover, mutation, or a combination of both, depending on whether the randomly generated probabilities for the execution of crossover or mutation exceed the default threshold. Evolutionary algorithms often balance exploitation and exploration through crossover and mutation, without explicitly utilizing the input-output correlations. Therefore, when the design space is large, it may require a large number of experiments or simulations to attain desired outcomes, making it not suitable for expensive-to-evaluate problems . To address this issue, different surrogate-assisted evolutionary algorithms have been proposed . Nevertheless, surrogate model selection and relevant parameter tuning remain challenging . Moreover, incorporating constraints within the framework can be nontrivial .