title: Optimization of simulation models with GADELO: a multi-population genetic algorithm.
abstract: In this paper, a new Genetic Algorithm based on the Dynamic Exploration of Local Optima (GADELO) was used to estimate the parameters of the MRD (Micro-population model of Risk-group Dynamics) micro-population model for smoking cessation by minimizing a deviation function between the model's predictions and the smoking cessation data of the Multiple Risk Factor Intervention Trial (MRFIT). The efficiency and accuracy of the GADELO estimations were consistently superior to those obtained using the standard genetic algorithm or the simplex algorithm of Nelder-Mead.
authors.affiliation: Control Science and Dynamical Systems Center, St. Paul, MN.
authors.forename: M
authors.initials: M
authors.lastname: Elketroussi
authors.forename: D P
authors.initials: DP
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D002875:Chromosomes; D003198:Computer Simulation; D003434:Crossing Over, Genetic / Q000235:genetics; D014644:Genetic Variation / Q000235:genetics; D005828:Genetics, Population; D006801:Humans; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D010641:Phenotype; D011157:Population Dynamics; D011336:Probability; D012307:Risk Factors; D012641:Selection, Genetic; D012907:Smoking / Q000235:genetics; D016540:Smoking Cessation / Q000706:statistics & numerical data; D000074606:Smoking Prevention; D013269:Stochastic Processes