Genetic Algorithm to Estimate Parameters of Indonesian Population Growth Model
Maya Rayungsari, Akhsanul In’am, Muhammad Aufin · 2020
In this study, the genetic algorithm is implemented to determine the most suitable growth models for Indonesian population data.The tested models are the simple models of Malthus and Verhulst.Parameters estimated in Malthus model include birth rate, death rate, and migration rate.Meanwhile, Parameters estimated in Verhulst model are intrinsic growth rate (birth rate minus death rate), carrying capacity, and migration rate.The model selection is based on the lowest average cost function value of each model.The value of the cost function is determined based on the distance between the population number in the model with the estimated parameters and the population number reported by worldbank.org.After determining the most appropriate model based on parameter estimation, simulation of the Indonesian population will be conducted for the upcoming years.