Abstract
The performance of hillclimbing design optimization can be improved by abstraction and decomposition of the design space. Methods for automatically finding and exploiting such abstractions and decompositions are presented in this paper. A technique called Operator Importance Analysis" finds useful abstractions. It does so by determining which of a given set of operators are the most important for a given class of design problems. Hillclimbing search runs faster when performed using this smaller set of operators. A technique called Operator Interaction Analysis" finds useful decompositions. It does so by measuring the pairwise interaction between operators. It uses such measurements to form an ordered partition of the operator set. This partition can then be used in a hierarchic" hillclimbing algorithm which runs faster than ordinary hillclimbing with an unstructured operator set. We have implemented both techniques and tested them in the domain of racing yacht hull design. Our experimental results show that these two methods can produce substantial speedups with little or no loss in quality of the resulting designs.