The searches are applied to test problems with known optima to gauge them for solution speed and nearness to optimality. In this paper we propose a genetic algorithm for discriminant analysis. The genetic fitness function uses duality principles of mathematical programming to solve the linear discriminant problem. The genetic method performed well in empirical testing and also provides the reasoning power behind the mathematical programming models. Factors adding to the complexity of the problem include asymmetrically informed agents, incentive problems, and inability to specify functional relationships between variables.
Research literature identifying determinants of long-run performance of new issues is limited.
This study uses a data driven, nonparametric, neural network based approach to predict the long-run operating performance of new ventures. The classification accuracy of the neural network model is compared with that of a logit model. Methodological issues such as sample design and estimation of optimal cutoff probabilities for classification are addressed. The results suggest that the neural networks generally outperform logit models. This is accomplished by developing an intelligent Knowledge- Based Decision Support System for a contractor prequalification environment.