Generator approach to evolutionary optimization of catalysts and its integration with surrogate modeling

摘要: This paper presents some unpublished aspects and ongoing developments of the recently elaborated generator approach to evolutionary optimization catalytic materials, purpose which is obtain algorithms precisely tailored problem being solved. It briefly recalls principles approach, then it describes how employed operations reflect specificity involved mixed constrained tasks, tackles checking feasibility large polytope systems, frequently resulting from constraints. Finally, discusses integration with surrogate modeling, paying particular attention models enhanced boosting. The usefulness modeling in general boosted documented on a case study data high-temperature synthesis hydrocyanic acid.