Genetic Algorithms for the Calibration of Cellular Automata Urban Growth Modeling

This paper discusses the use of genetic algorithms to enhance the efficiency of transition rule calibration in cellular automata urban growth modeling. The cellular automata model is designed as a function of multitemporal satellite imagery and population density. Transition rules in the model identify the required neighborhood urbanization level for a test pixel to develop to urban. Calibration of the model is initially performed by exhaustive search, where the entire solution space is examined to find the best set of rule values. This method is computationally extensive and needs to consider all possible combinations for the transition rules. The rise in the number of variables will exponentially increase the time required for running and calibrating the model. This study introduces genetic algorithms as an effective solution to the calibration problem. It is shown that the genetic algorithms are able to produce modeling results close to the ones obtained from the exhaustive search in a time effective manner. Optimal rule values can be reached within the early generations of genetic algorithms. It is expected that genetic algorithms will significantly benefit urban modeling problems with larger set of input data and bigger solution spaces.