Exploring the solution space of semi- structured spatial problems using genetic algorithms

The resolution of semi-structured spatial problems often requires consensus building and compromise among stakeholders as they attempt to optimize their own set of criteria. The union of these sets form a criteria space that constrains the set of viable solutions that may be adopted by decision-makers. Knowledge about the criteria space, the solution space, and the relation between the two is normally incomplete and this lack of understanding places real limits on the ability of decision-makers to solve complex spatial problems. This research explores new approaches that are designed to establish a link between criteria space and solution space and to provide a mechanism that competing stakeholders can use to identify areas of conflict and compromise.