Statistical generalization: theory and applications

Abstract

In this paper, we discuss a new approach to generalize heuristic methods (HMs) to new test cases of an application, and conditions under which such generalization is possible. Generalization is difficult when performance values of HMs are characterized by multiple statistical distributions across subsets of test cases of an application. We define a new measure called probability of win and propose three methods to evaluate it: interval analysis, maximum likelihood estimate, and Bayesian analysis. We show experimental results on new HMs found for blind equalization and branch-and-bound search.