Integration of Genetic Algorithm and Agent Based Model to Visualize Near Realistic Sustainable Urban Growth: A Comparative Study

To overcome the limitations of traditional land use modelling techniques, this paper introduces genetic algorithm (GA) to find optimum solution for the factors affecting the change in land use within the search space through developing and Agent Based Model(ABM). Optimized values of agents were then introduced to a goal-oriented environment where interactions are programmed to be self-constrained ABM. GA-ABM clearly outperformed the traditional CA approach in terms of Kappa indices of agreement, therefore, reducing the simulation uncertainty, improving the overall model accuracy. Modelled output for the year 2025 insists planning authorities for considering rapid expansion of paved spaces as a challenge to achieve sustainable growth, to fulfil sustainable development goals by the year 2030.