Xu et al. have applied the thermodynamic model COSMO-SAC in a genetic algorithm to 55 design double-salt ionic liquid solvents. Scheffczyk et al. have introduced the COSMO-CAMD framework, which integrates the COSMO-RS model for obtaining quantum mechanical information about molecules. And the genetic algorithm in use, LEA3D, creates 3D molecular structure information as input for COSMO-RS. The framework COSMO-CAMD has been originally developed for extraction solvent selection in separation processes. Since its original publication, the framework has been extended by various features, including the consideration of process characteristics, as well as environmental 28 and economic aspects. Furthermore, the applicability of the framework has been extended from solvent selection for separation tasks to reaction solvent selection in reactors and even catalyst design. Despite their broad applications in CAMD, genetic algorithms, due to their evolutionary nature, often experience time-consuming, difficult-to-achieve convergence, and easy-to-get premature convergence, which can lead to infeasible and low-quality solutions within a limited number of generations. Consequently, executing genetic algorithms often requires highperformance computing clusters, or alternatively, may necessitate compromising on solution quality.