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Multiple Objective Step Function Maximization with Genetic Algorithms and Simulated Annealing

Author

Listed:
  • Grohall, Guenther

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna)

  • Jung, Juergen

    (Indiana University)

Abstract

We develop a hybrid algorithm using Genetic Algorithms (GA) and Simulated Annealing (SA) to solve multi-objective step function maximization problems. We then apply the algorithm to a specific economic problem which is taken out of the corporate governance literature.

Suggested Citation

  • Grohall, Guenther & Jung, Juergen, 2003. "Multiple Objective Step Function Maximization with Genetic Algorithms and Simulated Annealing," Economics Series 141, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:141
    as

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    File URL: https://irihs.ihs.ac.at/id/eprint/1518
    File Function: First version, 2003
    Download Restriction: no
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    References listed on IDEAS

    as
    1. Wu, Lihua & Wang, Yuyun, 1998. "An Introduction to Simulated Annealing Algorithms for the Computation of Economic Equilibrium," Computational Economics, Springer;Society for Computational Economics, vol. 12(2), pages 151-169, October.
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    Cited by:

    1. W. C. Jackson & J. D. Norgard, 2008. "A Hybrid Genetic Algorithm with Boltzmann Convergence Properties," Journal of Optimization Theory and Applications, Springer, vol. 136(3), pages 431-443, March.

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    More about this item

    Keywords

    Numerical computation; Genetic algorithms; Simulated annealing;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance

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