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Risk Management Using Fuzzy Logic and Genetic Algorithms

In: The Ordered Weighted Averaging Operators

Author

Listed:
  • Teresa C. Rubinson

    (TCR, Inc.)

  • Georgette Geotsi

    (Sacred Heart University)

Abstract

Risk management is a complex and subjective task. It involves identifying risk factors, setting risk thresholds, and determining appropriate actions to reduce risk [Bernstein, 1995]. Fuzzy logic techniques are especially well suited to subjective problems of this type. Traditionally, fuzzy techniques have relied on user supplied data to define fuzzy functional parameters. However, in real life people are notoriously inaccurate and unreliable in reporting their preferences, especially as the complexity and uncertainty of the problem increases. In this paper, we describe a methodology which uses a genetic algorithm to automate and validate derivation of fuzzy functional parameters.

Suggested Citation

  • Teresa C. Rubinson & Georgette Geotsi, 1997. "Risk Management Using Fuzzy Logic and Genetic Algorithms," Springer Books, in: Ronald R. Yager & Janusz Kacprzyk (ed.), The Ordered Weighted Averaging Operators, pages 155-166, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-6123-1_13
    DOI: 10.1007/978-1-4615-6123-1_13
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