Advancements in Genetic Programming for Data Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{Jabeen:thesis,
  author =       "Hajira Jabeen",
  title =        "Advancements in Genetic Programming for Data
                 Classification",
  school =       "National University of Computer and Emerging Sciences
                 Islamabad",
  year =         "2010",
  address =      "Pakistan",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://prr.hec.gov.pk/Thesis/717S.pdf",
  URL =          "http://eprints.hec.gov.pk/6983/",
  size =         "130 pages",
  abstract =     "This thesis aims to advance the state of the art in
                 data classification using Genetic programming(GP).GP is
                 an evolutionary algorithm that has several outstanding
                 features making it ideal for complex problems like data
                 classification. However, it suffers from a few
                 limitations that reduce its significance. This thesis
                 targets at proposing optimal solutions to these GP
                 limitations.The problems covered in this thesis are: 1.
                 Increase in GP tree complexity during evolution that
                 results in long training time.

                 2. Lack of convergence to a single (optimal) solution.
                 3. Lack of methodology to handle mixed data-type
                 without type transformation.

                 4. Search of a better method for multi-class
                 classification. Through this work, we have proposed a
                 method which achieves significant reduction in bloat
                 for classification task. Moreover, we have presented a
                 Particle Swarm Optimisation based hybrid approach to
                 increase performance of GP evolved classifiers.The
                 approach offers better performance in less
                 computational effort. Another approach introduces a new
                 two layered paradigm for mixed type data classification
                 with an added feature that uses data in its original
                 form instead of any transformation or
                 pre-processing.The last but not the least contribution
                 is an efficient binary encoding method for multi-class
                 classification problems. The method involves smaller
                 number of GP evolutions, reducing the computation and
                 suffers from fewer conflicts yielding better
                 results.

                 All of the proposed methods have been tested and our
                 experiments conclude the efficiency of proposed
                 approaches.",
  notes =        "ID Code: 6983",
}

Genetic Programming entries for Hajira Jabeen

Citations