Feature-based classifiers for design optimization

We present a design optimization method for systems with high-dimensional parameter spaces using inductive decision trees. The essential idea is to map designs into a relatively low-dimensional feature space, and to derive a classifier to search for high-performing design alternatives within this space. Unlike learning classifier systems that were pioneered by Holland and Goldberg, classifiers defined by inductive decision trees were not originally developed for design optimization. In this paper, we explore modifications to such classifiers to make them more effective in the optimization problem. We expand the notions of feature space, generalize the tree construction heuristic beyond the original information-theoretic definitions increase the reliance on domain expertise, and facilitate the transfer edge between related systems. There is a relatively small but rapidly growing body of work in the use of inductive trees for engineering design; the method presented herein is complementary to this research effort.