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Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

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  • Davalos, Sergio
  • Gritta, Richard D.
  • Adrangi, Bahram

Abstract

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model’s performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

Suggested Citation

  • Davalos, Sergio & Gritta, Richard D. & Adrangi, Bahram, 2007. "Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 46(2).
    • Handle: RePEc:ags:ndjtrf:206886
    DOI: 10.22004/ag.econ.206886
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