@article{Davalos:208166,
      recid = {208166},
      author = {Davalos, Sergio and Gritta, Richard D. and Adrangi, Bahram  and Goodfriend, Jason},
      title = {The Use of a Genetic Algorithm in Forecasting Air Carrier  Financial Stress and Insolvency},
      address = {2005-03},
      number = {1426-2016-118474},
      pages = {8},
      year = {2005},
      abstract = {While statistical and artificial intelligence methods such  as Artificial Neural
Networks (ANN) have been used  successfully to classify organizations in terms of
solvency  or insolvency, they are limited in degree of generalization  either by
requiring linearly separable variables, lack of  knowledge of how a conclusion is
reached, or lack of a  consistent approach for dealing with local optimal  solution
whether maximum or minimum. This research explores  the use of a method that
has the ability of the ANN method  to deal with linearly inseparable variables and
incomplete,  noisy data; and resolves the problem of falling into a  local optimum in
searching the problems space. The paper  applies a genetic algorithm to a sample of
U.S. airlines  and utilizes financial data from carrier income statements  and balance
sheets and ratios calculated from this data to  assess air carrier solvency.},
      url = {http://ageconsearch.umn.edu/record/208166},
      doi = {https://doi.org/10.22004/ag.econ.208166},
}