A gene expression programming algorithm for population prediction problems

Predicting the size or development tendency of population is a complicated geographical problem. This kind of problem often involves multiple geographical components that interact in a complex way. In this article, a new technique based on a gene expression programming (GEP) algorithm is presented, which can be used to address population prediction problems. In the context of GEP algorithm, population prediction problems are formulated by designing encoding strategies, evolutionary operations and fitness function. The population prediction model based on GEP approach is finally constructed and applied to predict population of Dongguan city. Compared with grey model and artificial neural network model, the predicting precision is improved by 18.34% and 30.54%, respectively. GEP model has better accurateness of predicting the size and development tendency of population. It can accurately fit nonlinear population development tendency and avoid overfitting to a certain extent. Gene expression programming algorithm can be used to effectively solve population prediction problems.