Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design

Thomas Ellman, John Keane, Mark Schwabacher

Models of physical systems can differ according to computational cost, accuracy and precision, among other things. Depending on the problem solving task at hand, different models will be appropriate. Several investigators have recently developed methods of automatically selecting among multiple models of physical systems. Our research is novel in that we are developing model selection techniques specifically suited to computer-aided design. Our approach is based on the idea that artifact performance models for computer-aided design should be chosen in light of the design decisions they are required to support. We have developed a technique called "Gradient Magnitude Model Selection" (GMMS), which embodies this principle. GMMS operates in the context of a hillclimbing search process. It selects the simplest model that meets the needs of the hillclimbing algorithm in which it operates. We are using the domain of sailing yacht design as a testbed for this research. We have implemented GMMS and used it in hillclimbing search to decide between a computationally expensive potential-flow program and an algebraic approximation to analyze the performance of sailing yachts. Experimental tests show that GMMS makes the design process faster than it would be if the most expensive model were used for all design evaluations. GMMS achieves this performance improvement with little or no sacrifice in the quality of the resulting design.


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