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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.