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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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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