A genetic algorithm applied to optimal allocation in stratified sampling

Abstract

Over the last decades, many researchers have studied and proposed new methods for the solution of the multivariate optimal allocation problem, which can be performed by choosing one of the following goals: (i) minimizing the weighted combination of relative variances, considering the sample size fixed or (ii) minimizing the sample size in a way that the coefficients of variation are lower or equal to the previously fixed coefficients of variation. Taking each goal into account, the present article proposes two heuristic algorithms that were developed by studying the optimization technique called biased random key genetic algorithm. The computational experiments reported in the end of this work indicate that the proposed algorithms can be a good alternative for the solution of this problem, when compared with the two methods from literature.