Discrete Versus Continuous Parametrization of Bank Credit Rating Systems Optimization Using Differential Evolution
K. Ming Leung
New York University (NYU) - Polytechnic Institute of NYU
April 19, 2010
Genetic and Evolutionary Computation Conference (GECCO-2010)
Bank credit rating system is a clustering problem that aims to achieve the optimal classification of the clients’ probability of defaults (PDs) into discrete buckets under a number of constraints. This global optimization problem can be parametrized either using continuous or discrete decision variables, and treated using basically the same differential evolution (DE) method that takes into account of real-world constraints imposed by the recent Basel Accord on Banking Supervision. This enables us to make interesting comparisons between continuous versus discrete parametrization of the same problem in terms of the efficiency, robustness and the rate of convergence. It turns out to be beneficial to use discrete parameters for all of these reasons. In addition we have also explored the use of the elitist as well as the classic strategies within the DE approach. The former choice turns out to perform better in terms of efficiency, robustness, and faster convergence, except when the number of required buckets is large.
Number of Pages in PDF File: 8
Keywords: Bank credit rating, differential evolution, optimization, constraints, integer programmingworking papers series
Date posted: April 19, 2010 ; Last revised: May 13, 2010
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo1 in 0.891 seconds