Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming

  • Eva Alfaro-Cid
  • Alberto Cuesta-Cañada
  • Ken Sharman
  • Anna I. Esparcia-Alcázar
Part of the Studies in Computational Intelligence book series (SCI, volume 100)


In this chapter we present the application of a genetic programming (GP) algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database that includes extensive information (not only economic) from the companies. In order to handle the different data types we have used Strongly Typed GP and variable reduction. Also, bloat control has been implemented to obtain comprehensible classification models. For comparison purposes we have solved the same problem using a support vector machine (SVM). GP has achieved very satisfactory results, improving those obtained with the SVM.


Support Vector Machine Genetic Program Error Type Global Error Variable Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Alfaro-Cid E, Sharman K, Esparcia-Alcázar A (2007) A genetic programming approach for bankruptcy prediction using a highly unbalanced database. In: et al MG (ed) Proceedings of the First European Workshop on Evolutionary Computation in Finance and Economics (EvoFIN'07), Springer-Verlag, Valencia, Spain, Lecture Notes in Computer Science, vol 4448, pp 169-178Google Scholar
  2. [2]
    Altman E I (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23(4):589-609CrossRefGoogle Scholar
  3. [3]
    Brabazon A, Keenan PB (2004) A hybrid genetic model for the prediction of corporate failure. Computational Management Science 1:293-310zbMATHCrossRefGoogle Scholar
  4. [4]
    Brabazon A, O'Neill M (2006) Biologically inspired algorithms for finantial modelling. Springer-Verlag, Berlin, GermanyGoogle Scholar
  5. [5]
    Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Software available at
  6. [6]
    Dimitras AI, Zanakis SH, Zopounidis C (1996) A survey of business failures with an emphasis on predictions, methods and industrial applications. European Journal of Operational Research 90:487-513zbMATHCrossRefGoogle Scholar
  7. [7]
    Eggermont J, Eiben AE, van Hemert JI (1999) A comparison of genetic programming variants for data classification. In: Hand DJ, Kok JN, Berthold MR (eds) Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis (IDA'99), Springer-Verlag, Amsterdam, The Netherlands, Lecture Notes in Computer Science, vol 1642, pp 281-290Google Scholar
  8. [8]
    Fernández de Vega F, Rubio del Solar M, Fernández Martínez A (2005) Implementaci ón de algoritmos evolutivos para un entorno de distribuci ón epid émica. In: Arenas MG, Herrera F, Lozano M, Merelo JJ, Romero G, SánchezAM (eds) Actas del IV Congreso Espa ñol de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB'05), Granada, Spain, pp 57-62Google Scholar
  9. [9]
    Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intelligent Data Analysis 6(5):429-449zbMATHGoogle Scholar
  10. [10]
    Kim MJ, Han I (2003) The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms. Experts Systems with Applications 25:637-646CrossRefGoogle Scholar
  11. [11]
    Kishore JK, Patnaik LM, Mani V, Agrawal VK (2001) Genetic programming based pattern classification with feature space partitioning. Information Sciences 131:65-86zbMATHCrossRefGoogle Scholar
  12. [12]
    Koza JR (1992) Genetic Programming: On the programming of computers by means of natural selection. MIT Press, Cambridge, MAzbMATHGoogle Scholar
  13. [13]
    Lensberg T, Eilifsen A, McKee TE (2006) Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research 169:677-697zbMATHCrossRefMathSciNetGoogle Scholar
  14. [14]
    Leshno M, Spector Y (1996) Neural network prediction analysis: The bankruptcy case. Neurocomputing 10:125-147CrossRefGoogle Scholar
  15. [15]
    Luke S (2000) Issues in scaling genetic programming: Breeding strategies, tree generation, and code bloat. PhD thesis, University of Maryland, Maryland, USAGoogle Scholar
  16. [16]
    Luke S, Panait L (2002) Lexicographic parsimony pressure. In: et al WBL (ed) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'02), New York, USA, pp 829-836Google Scholar
  17. [17]
    Luke S, Panait L (2006) A comparison of bloat control methods for genetic programming. Evolutionary Computation 14(3):309-344CrossRefGoogle Scholar
  18. [18]
    McKee TE, Lensberg T (2002) Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research 138:436-451zbMATHCrossRefGoogle Scholar
  19. [19]
    Montana DJ (1995) Strongly typed genetic programming. Evolutionary Computation 3(2):199-230CrossRefGoogle Scholar
  20. [20]
    Ohlson J (1980) Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18(1):109-131CrossRefMathSciNetGoogle Scholar
  21. [21]
    Poli R (2003) A simple but theoretically-motivated method to control bloat in genetic programming. In: Ryan C, Soule T, Keijzer M, Tsang EPK, Poli R, CostaE (eds) Proceedings of the Sixth European Coference on Genetic Programming EuroGP'03, Springer-Verlag, Essex, UK, Lecture Notes in Computer Science, vol 2610, pp 204-217Google Scholar
  22. [22]
    Salcedo-Sanz S, Fernández-Villacañas JL, Segovia-Vargas MJ, Bousoño-Calzón C (2005) Genetic programming for the prediction of insolvency in non-life insurance companies. Computers and Operations Research 32:749-765zbMATHCrossRefGoogle Scholar
  23. [23]
    Shin KS, Lee YL (2002) A genetic algorithm application in bankruptcy prediction modeling. Experts Systems with Applications 23:321-328CrossRefGoogle Scholar
  24. [24]
    Tsakonas A, Dounias G, Doumpos M, Zopounidis C (2006) Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming. Experts Systems with Applications 30:449-461CrossRefGoogle Scholar
  25. [25]
    Vapnik V (1998) Statistical Learning Theory. Wiley-Interscience, New YorkzbMATHGoogle Scholar
  26. [26]
    Varetto F (1998) Genetic algorithm applications in the field of insolvency risk. Journal of banking and Finance 22:1421-1439CrossRefGoogle Scholar
  27. [27]
    Vieira AS, Ribeiro B, Mukkamala S, Neves JC, Sung AH (2004) On the performance of learning machines for bankruptcy detection. In: Hand DJ, Kok JN, Berthold MR (eds) Proceedings of the IEEE Conference on Computational Cybernetics, Vienna, Austria, pp 323-327Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Eva Alfaro-Cid
    • 1
  • Alberto Cuesta-Cañada
    • 1
  • Ken Sharman
    • 1
  • Anna I. Esparcia-Alcázar
    • 1
  1. 1.Instituto Tecnológico de InformáticaUniversidad Politécnica de ValenciaValenciaSpain

Personalised recommendations