application of genetic algorithm and cellular automata for simulation of land use and land cover changes; case of karaj city, iran

this study emphasized the ability of genetic algorithm and cellular automate to simulate urban land use changes by integrating adaptive model. the most important part of modeling is to define transition rules.  in this research, a cellular automata model in dinamica ego software was used coupled with genetic algorithm. according to disability of the software for manipulating large number of variables in genetic algorithm tool in the software, a program implemented in python language in order to carry out genetic algorithm for coefficients in the model to simulate human land use of karaj city as a rapid urbanization area in iran. results revealed from the program had 67% similarity with genetic algorithm tool. by using the results of the simulations have done. finally, the results of the original status have been compared with the results of simulation based on genetic algorithm. the results show that proposed model provides a new way for the simulation of land use changes and demonstrated that there is no significant difference between results of original model and genetic algorithm simulation. although it seems that genetic algorithm approach will lead to more optimal results but this is not guaranteed it has better outputs compared to original status. land use planning; land cover changes; genetic algorithm; simulation