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Solving Irregular Econometric and Mathematical Optimization Problems with a Genetic Hybrid Algorithm

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  • Ostermark, Ralf

Abstract

In the present paper we apply a new Genetic Hybrid Algorithm (GHA) to globally minimize a representative set of ill-conditioned econometric/mathematical functions. The genetic algorithm was specifically designed for nonconvex mixed integer nonlinear programming problems and it can be successfully applied to both global and constrained optimization. In previous studies, we have demonstrated the efficiency of the GHA in solving complicated NLP, INLP and MINLP problems. The present study is a continuation of this research, now focusing on a set of highly irregular optimization problems. In this paper we discuss the genetic hybrid algorithm, the nonlinear problems to be solved and present the results of the empirical tests. Citation Copyright 1999 by Kluwer Academic Publishers.

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  • Ostermark, Ralf, 1999. "Solving Irregular Econometric and Mathematical Optimization Problems with a Genetic Hybrid Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 13(2), pages 103-115, April.
    • Handle: RePEc:kap:compec:v:13:y:1999:i:2:p:103-15
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    Cited by:

    1. Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    2. Ostermark, Ralf, 2001. "Genetic modelling of multivariate EGARCHX-processes: evidence on the international asset return signal response mechanism," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 71-93, November.
    3. Ostermark, Ralf, 2004. "A multipurpose parallel genetic hybrid algorithm for non-linear non-convex programming problems," European Journal of Operational Research, Elsevier, vol. 152(1), pages 195-214, January.
    4. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    5. Trindade, Graça & Ambrósio, Jorge, 2012. "An optimization method to estimate models with store-level data: A case study," European Journal of Operational Research, Elsevier, vol. 217(3), pages 664-672.

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