Linear Discriminant Functions Determined by Genetic Search

This paper studies a genetic algorithm that determines linear discriminant functions. The objective is to minimize the number of misclassifications and then to minimize the number of used attributes. The algorithm produces results that are significantly better than Fisher's method for minimizing the probability of misclassification but not significantly different than those that exactly minimize the number of misclassifications. The algorithm has the added benefit that the solutions obtained are more parsimonious than these exact algorithms since two objectives could be considered simultaneously. In our empirical studies, the algorithm routinely found the optimal solution while taking significantly less time than exact algorithms which must both find and prove optimality. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.