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Many regional environmental problems are the consequence of anthropogenic activities involving land cover changes. Temporal land cover data with social aspects are critical in tracing relationships of cause and effect on variables of interest with the effects of context on behaviour or with the process of human environment interaction and are also useful for governance of urbanising cities. Many cities are now undergoing redevelopment for economic purposes with new roads, infrastructure improvements, etc. This phenomenona is very rapid in India with urban population growing at around 2.3 percent per annum. This dramatic increase in urbanisation has raised the necessity to understand the dynamics of urban growth process for planning of natural resources. Cellular automata, an artificial intelligence technique based on pixels, states, neighbourhood and transition rules, is being implemented to model the urban growth process due to its ability to fit such complex spatial nature using simple and effective rules. The possibility of using genetic algorithms for automatic calibration of the model through proper design of their parameters, including objective function, initial population, selection, crossover and mutation has also been explored. The techniques are tested for Bangalore city, India by modeling the urban growth using remote sensing data of various resolutions.