An Optimised Cellular Automata Model Based on Adaptive Genetic Algorithm for Urban Growth Simulation

This paper presents an improved cellular automata (CA) model optimized using an adaptive genetic algorithm (AGA) to simulate the spatio-temporal process of urban growth. The AGA technique can be used to optimize the transition rules of the CA model defined through conventional methods such as logistic regression approach, resulting in higher simulation efficiency and improved results. Application of the AGA-CA model in Shanghai’s Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics.