Adapted Geometric Semantic Genetic programming for diabetes and breast cancer classification

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

@InProceedings{Zhu:2013:MLSP,
  author =       "Zhechen Zhu and Asoke K. Nandi and 
                 Muhammad Waqar Aslam",
  title =        "Adapted Geometric Semantic Genetic programming for
                 diabetes and breast cancer classification",
  booktitle =    "IEEE International Workshop on Machine Learning for
                 Signal Processing (MLSP 2013)",
  year =         "2013",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/MLSP.2013.6661969",
  ISSN =         "1551-2541",
  abstract =     "In this paper, we explore new Adapted Geometric
                 Semantic (AGS) operators in the case where Genetic
                 programming (GP) is used as a feature generator for
                 signal classification. Also to control the
                 computational complexity, a devolution scheme is
                 introduced to reduce the solution complexity without
                 any significant impact on their fitness. Fisher's
                 criterion is employed as fitness function in GP. The
                 proposed method is tested using diabetes and breast
                 cancer datasets. According to the experimental results,
                 GP with AGS operators and devolution mechanism provides
                 better classification performance while requiring less
                 training time as compared to standard GP.",
  notes =        "Also known as \cite{6661969}",
}

Genetic Programming entries for Zhechen Zhu Asoke K Nandi Muhammad Waqar Aslam

Citations