Genetic algorithm-based selection of optimal Monte Carlo simulations

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.