Genetic algorithm-based selection of optimal Monte Carlo simulations

Abstract

The aim of this work is to propose the use of a genetic algorithm to solve the problem of the optimal subsampling of Monte Carlo simulations to obtain desired statistical properties. It is designed to optimally select the best $m$ Monte Carlo simulations from a larger pool of $N>m$ simulations. The concept of an “optimal selection” is defined through a target metric, in this work the first and second moments of the distribution, from the set of $N$ simulations, to which the subset of $m$ simulations should closely converge. The implementation employs an objective function, allowing the algorithm to balance computational efficiency and optimization performance, achieving fast and precise selection of the $m$ simulations.