Evolutionary computation system for problem-tailored genetic optimization of catalytic materials

Abstract

The paper addresses key problems pertaining to the commonly used evolutionary approach to optimization of catalytic materials. These are on the one hand the narrow scope of genetic algorithms developed specifically for searching optimal catalyst, on the other hand the insufficient dealing in existing implementations of genetic algorithms with mixed optimization. The paper outlines a program generator automatically generating problem-tailored genetic algorithms from descriptions of optimization tasks in a specific description language. For constrained mixed optimization, the generated algorithms employ an approach based on formulating a separate linearly-constrained continuous optimization task for each combination of values of the discrete variables. On the set of nonempty polyhedra describing the feasible solutions of those tasks, discrete optimization is performed, followed by solving those tasks for each individual of the resulting population of polyhedra.