Global Optimization of Some Difficult Benchmark Functions by Host-Parasite Coevolutionary Algorithm
This paper proposes a novel method of global optimization based on host-parasite co-evolution. It also develops a Fortran-77 code for the algorithm. The algorithm has been tested on 100 benchmark functions (of which the results of 32 relatively harder problems have been reported). In its search ability, the proposed method is comparable to the Differential Evolution method of global optimization. The method has been used for solving the 'completing the incomplete correlation matrix' problem encountered in financial economics. It is found that the proposed methods as well as the Differential Evolution method solves the problem, but the proposed method provides results much faster than the Differential Evolution method.
Volume (Year): 33 (2013)
Issue (Month): 1 ()
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- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
- Ingram Olkin, 1981. "Range restrictions for product-moment correlation matrices," Psychometrika, Springer, vol. 46(4), pages 469-472, December.
- Mishra, Sudhanshu, 2006. "Some new test functions for global optimization and performance of repulsive particle swarm method," MPRA Paper 2718, University Library of Munich, Germany.
- Mishra, SK, 2004. "Optimal solution of the nearest correlation matrix problem by minimization of the maximum norm," MPRA Paper 1783, University Library of Munich, Germany.
- Mishra, SK, 2007. "Minimization of Keane’s Bump Function by the Repulsive Particle Swarm and the Differential Evolution Methods," MPRA Paper 3098, University Library of Munich, Germany, revised 05 May 2007.
- Chesney, Marc & Scott, Louis, 1989. "Pricing European Currency Options: A Comparison of the Modified Black-Scholes Model and a Random Variance Model," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(03), pages 267-284, September.
- Raoul Pietersz & Patrick Groenen, 2004.
"Rank reduction of correlation matrices by majorization,"
Taylor & Francis Journals, vol. 4(6), pages 649-662.
- Pietersz, R. & Groenen, P.J.F., 2004. "Rank reduction of correlation matrices by majorization," Econometric Institute Research Papers EI 2004-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Raoul Pietersz & Patrick J. F. Groenen, 2005. "Rank Reduction of Correlation Matrices by Majorization," Finance 0502006, EconWPA.
- Igor Grubisic & Raoul Pietersz, 2005.
"Efficient Rank Reduction of Correlation Matrices,"
- Grubisic, I. & Pietersz, R., 2005. "Efficient Rank Reduction of Correlation Matrices," ERIM Report Series Research in Management ERS-2005-009-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
- Ormerod, Paul & Mounfield, Craig, 2000. "Random matrix theory and the failure of macro-economic forecasts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 280(3), pages 497-504.
- Mishra, SK, 2006.
"Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multi-modal Benchmark Functions,"
449, University Library of Munich, Germany.
- Mishra, SK, 2006. "Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multi-modal Benchmark Functions," MPRA Paper 1743, University Library of Munich, Germany.
- Mishra, SK, 2007. "Completing correlation matrices of arbitrary order by differential evolution method of global optimization: A Fortran program," MPRA Paper 2000, University Library of Munich, Germany.
- Mishra, SK, 2006. "Global Optimization by Differential Evolution and Particle Swarm Methods: Evaluation on Some Benchmark Functions," MPRA Paper 1005, University Library of Munich, Germany.
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