Design optimization using dynamic evaluation

We describe a search strategy that may be useful for a class of design problems by developing an example from cancer radiation treatment planning. This application problem involves typical features of design problems such as constraints, optimality, a large search space with continuously varying parameters as well as discrete (non-numeric) parameters. There is no known method of comparing elements of the solution space based on a static evaluation function. We have therefore developed a dynamic evaluation function, which attempts to heuristically compare all solutions with one another, as a way of interpreting the evaluation results. This allows us to use an analog of hill-climbing with a simple SELECT-GENERATE-TEST loop where expert rules are used as "move generators' ' and a similarity metric is used to control or direct the application of the rules for plan modification. Preliminary tests of these ideas indicate that a practical working system can be built. 1 Problem definition Design problems have received a lot of attention recently in AI research [Mostow, 1985]. Typically, design tasks present difficult problems with big search spaces and solutions defined in terms of continuously varying parameters. They usually involve constraints and optimality criteria. Analytic solutions generally do not exist and experiential "rules of thumb " are not sufficient. This is because it is often necessary to reason about complex properties of objects, such as their geometry, and incrementally approach the best solution by drawing conclusions from the explored variants. The few existing systems such as: AIR/CYL [Brown and Chandrasekaran, 1986], PRIDE [Mittal and Araya, 1986], VT [Marcus e