An Analysis of Integration of Hill Climbing in Crossover and Mutation operation for EEG Signal Classification

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

  author =       "Arpit Bhardwaj and Aruna Tiwari and 
                 M. Vishaal Varma and M. Ramesh Krishna",
  title =        "An Analysis of Integration of Hill Climbing in
                 Crossover and Mutation operation for EEG Signal
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "209--216",
  keywords =     "genetic algorithms, genetic programming, Biological
                 and Biomedical Applications",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754710",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "A common problem in the diagnosis of epilepsy is the
                 volatile and unpredictable nature of the epileptic
                 seizures. Hence, it is essential to develop Automatic
                 seizure detection methods. Genetic programming (GP) has
                 a potential for accurately predicting a seizure in an
                 EEG signal. However, the destructive nature of
                 crossover operator in GP decreases the accuracy of
                 predicting the onset of a seizure. Designing
                 constructive crossover and mutation operators (CCM) and
                 integrating local hill climbing search technique with
                 the GP have been put forward as solutions. In this
                 paper, we proposed a hybrid crossover and mutation
                 operator, which uses both the standard GP and CCM-GP,
                 to choose high performing individuals in the least
                 possible time. To demonstrate our approach, we tested
                 it on a benchmark EEG signal dataset. We also compared
                 and analysed the proposed hybrid crossover and mutation
                 operation with the other state of art GP methods in
                 terms of accuracy and training time. Our method has
                 shown remarkable classification results. These results
                 affirm the potential use of our method for accurately
                 predicting epileptic seizures in an EEG signal and hint
                 on the possibility of building a real time automatic
                 seizure detection system.",
  notes =        "Also known as \cite{2754710} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Arpit Bhardwaj Aruna Tiwari M Vishaal Varma M Ramesh Krishna