Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Aliehyaei:2014:SKIMA,
  author =       "R. Aliehyaei and S. Khan",
  booktitle =    "8th International Conference on Software, Knowledge,
                 Information Management and Applications (SKIMA)",
  title =        "Ant Colony Optimization, Genetic Programming and a
                 hybrid approach for credit scoring: A comparative
                 study",
  year =         "2014",
  abstract =     "Credit scoring is a commonly used method for
                 evaluating the risk involved in granting credits. Both
                 Genetic Programming (GP) and Ant Colony Optimisation
                 (ACO) have been investigated in the past as possible
                 tools for credit scoring. This paper reports an
                 investigation into the relative performances of GP, ACO
                 and a new hybrid GP-ACO approach, which relies on the
                 ACO technique to produce the initial populations for
                 the GP technique. Performance of the hybrid approach
                 has been compared with both the GP and ACO approaches
                 using two well-known benchmark data sets. Experimental
                 results demonstrate the dependence of GP and ACO
                 classification accuracies on the input data set. For
                 any given data set, the hybrid approach performs better
                 than the worse of the other two methods. Results also
                 show that use of ACO in the hybrid approach has only a
                 limited impact in improving GP performance.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SKIMA.2014.7083391",
  month =        dec,
  notes =        "Also known as \cite{7083391}",
}

Genetic Programming entries for R Aliehyaei S Khan

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