City Planning with a Multiobjective Genetic Algorithm and a Pareto Set Scanner


A genetic algorithm was used to search for optimal future land use and transportation plans for a pair of high-growth cities. Hundreds of thousands of plans were considered. Constraints were imposed to insure adequate housing for future residents. Objectives included the minimization of traffic congestion, the minimization of costs, and the minimization of change from the status quo. The genetic algorithm provided planners and decision-makers with a rich set of optimal plans known as the Pareto set. An interactive computer tool was developed to assist decision-makers in scanning the information in the Pareto set as they "shop" for a plan.