Using characteristics of the optimisation problem to determine the Genetic Algorithm population size when the number of evaluations is limited

The Genetic Algorithm (GA) parameter values that result in the best possible solutions being found are generally problem specific, and therefore expected to be related to the characteristics of the fitness function. In this work, statistics that characterise the fitness function have been related to the convergence of a GA population due to the repetitive application of tournament selection. Assuming that this operator has the dominant influence on the variance of the population, and that the computational time available is limited, the result can be used to determine a suitable population size. The methodology developed has been compared to other GA calibration methodologies, and was found to be the best of the different methods considered across a range of stopping criteria and problem formulations. This result demonstrates the potential usefulness of fitness function characteristics to inform the configuration of GAs, and in turn find the best possible solutions.