Optimal Spatial Searching Based on Advanced Genetic Algorithm and GIS

This study successfully solve a spatial-searching problem by using GA (genetic algorithm) and GIS, the method can be used as a planning tool to help urban planner to improve development efficiency for site selecting. A new genetic strategy basing on information entropy is proposed to analyze the importance of diversity, in this strategy, the algorithm will adjust operator parameters adaptively in accordance with the individual entropy and populations, and the optimal individual will be reserved, avoiding premature convergence. The methods can keep exuberance and diversity of population and promote algorithmic global searching ability by combining the internal information and inheritance operating organically. The proposed method has been tested by searching the best position in the city of Beijing, a densely populated for region, the population data is prepared in GIS as a main input to EAGA program. The results of the application indicated the proposed method is more efficacious than the other methods for solving complex problems by using large spatial data sets.