Discrete Versus Continuous Parametrization of Bank Credit Rating Systems Optimization Using Differential Evolution

Genetic and Evolutionary Computation Conference (GECCO-2010)

8 Pages Posted: 19 Apr 2010 Last revised: 13 May 2010

See all articles by K. Ming Leung

K. Ming Leung

NYU-Poly

Xi Zhang

New York University (NYU) - NYU Polytechnic School of Engineering

Date Written: April 19, 2010

Abstract

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.

Keywords: Bank credit rating, differential evolution, optimization, constraints, integer programming

Suggested Citation

Leung, K. Ming and Zhang, Xi, Discrete Versus Continuous Parametrization of Bank Credit Rating Systems Optimization Using Differential Evolution (April 19, 2010). Genetic and Evolutionary Computation Conference (GECCO-2010), Available at SSRN: https://ssrn.com/abstract=1592532 or http://dx.doi.org/10.2139/ssrn.1592532

K. Ming Leung (Contact Author)

NYU-Poly ( email )

Brooklyn, NY 11201
United States

Xi Zhang

New York University (NYU) - NYU Polytechnic School of Engineering ( email )

Brooklyn, NY 11201
United States

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