Intelligent Crossover and Mutation Technique to Control Bloat for Breast Cancer Diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Arzoo Khan and Medhavi Chouhan",
  booktitle =    "2015 International Conference on Computational
                 Intelligence and Communication Networks (CICN)",
  title =        "Intelligent Crossover and Mutation Technique to
                 Control Bloat for Breast Cancer Diagnosis",
  year =         "2015",
  pages =        "387--391",
  abstract =     "Breast cancer affects several people at present time.
                 Diagnosis which determines whether the cancer is benign
                 or malignant requires a lot of effort from doctors and
                 physician. Early diagnosis may save many lives.
                 Accurate classification plays an important role in
                 medical diagnosis. Genetic programming is a machine
                 learning algorithm which now days excelling in
                 classification field. But Genetic programming generally
                 face the problem of code bloating in which an increase
                 in average tree size is found without a corresponding
                 increase in fitness. In this paper we are proposing a
                 new technique for solving the problem of bloat and for
                 increasing classification accuracy. The technique is
                 known as intelligent crossover and mutation technique.
                 This technique is a combination of hill climbing and
                 conventional method which will be applied on both
                 crossover and mutation operator. To demonstrate this,
                 we had taken WBC dataset from UCI repository which has
                 2 classes and 9 features and we have compared
                 classification accuracy of our method with standard
                 crossover and FEDS crossover. Our classification
                 accuracy was 97.5percent for 50-50 training and testing
                 methodology 95percent for 60-40, 99percent for 70-30,
                 99.5percent for 80-20 and 99.6percent for 10 fold cross
                 validation technique. This shows our method can be used
                 for medical diagnosis as it provides good results.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CICN.2015.82",
  month =        dec,
  notes =        "Also known as \cite{7546120}",

Genetic Programming entries for Arzoo Khan Medhavi Chouhan