Using characteristics of the optimisation problem to determine the Genetic Algorithm population size when the number of evaluations is limited. (July 2015)

• Record Type: Journal Article
• Authors: Gibbs, Matthew S.; Maier, Holger R.; Dandy, Graeme C.
• Is Part Of: Environmental modelling & software. Volume 69(2015:Jul.)
• Page Start: 226
• Page End: 239
• Publication Date: 2015-07
• Subjects: Genetic Algorithms -- Water resources -- Calibration -- Fitness function analysis -- Convergence -- Water distribution systems; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.08.023
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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.