A statistical selection mechanism of GA for stochastic programming problems

We propose a new genetic algorithm to solve complex stochastic programming problems, in which possible combinations of parameters are provided as scenarios. The algorithm finds a solution efficiently using a statistical selection mechanism in addition to a sampling approach. In the algorithm, to reduce the computational demand, individuals are evaluated based on mean fitness in some scenarios sampled at random. Furthermore, to limit the probability that good individuals are excluded from the population by sampling error, selection in the algorithm is carried out based on statistical theory (i.e., Welch´s test). Our approach significantly reduces computing time required to find high quality solutions for stochastic facility location problems