Genetic algorithms for determining the parameters of cellular automata in urban simulation

This paper demonstrates that cellular automata (CA) can be a useful tool for analyzing the process of many geographical phenomena. There are many studies on using CA to simulate the evolution of cites. Urban dynamics is determined by many spatial variables. The contribution of each spatial variable to the simulation is quantified by its parameter or weight. Calibration procedures are usually required for obtaining a suitable set of parameters so that the realistic urban forms can be simulated. Each parameter has a unique role in controlling urban morphology in the simulation. In this paper, these parameters for urban simulation are determined by using empirical data. Genetic algorithms are used to search for the optimal combination of these parameters. There are spatial variations for urban dynamics in a large region. Distinct sets of parameters can be used to represent the unique features of urban dynamics for various subregions. A further experiment is to evaluate each set of parameters based on the theories of compact cities. It is considered that the better set of parameters can be identified according to the utility function in terms of compact development. This set of parameters can be cloned to other regions to improve overall urban morphology. The original parameters can be also modified to produce more compact urban forms for planning purposes. This approach can provide a useful exploratory tool for testing various planning scenarios for urban development.