A Memetic Genetic Programming with Decision Tree-based Local Search for Classification Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  title =        "A Memetic Genetic Programming with Decision Tree-based
                 Local Search for Classification Problems",
  author =       "Pu Wang and Ke Tang and Edward Tsang and Xin Yao",
  pages =        "916--923",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, area under
                 ROC curve, classification problems, classifier,
                 decision tree-based local search, fitness function,
                 memetic computing, memetic genetic programming,
                 statistical genetic decision tree, training algorithms,
                 decision trees, learning (artificial intelligence),
                 pattern classification, search problems",
  DOI =          "doi:10.1109/CEC.2011.5949716",
  abstract =     "In this work, we propose a new genetic programming
                 algorithm with local search strategies, named Memetic
                 Genetic Programming (MGP), for classification problems.
                 MGP aims to acquire a classifier with large Area Under
                 the ROC Curve (AUC), which has been proved to be a
                 better performance metric for traditionally used
                 metrics (e.g., classification accuracy). Three new
                 ideas are presented in our new algorithm. First, a new
                 representation called statistical genetic decision tree
                 (SGDT) for GP is proposed on the basis of Genetic
                 Decision Tree (GDT). Second, a new fitness function is
                 designed by using statistic information from SGDT.
                 Third, the concept of memetic computing is introduced
                 into SGDT. As a result, the MGP is equipped with a
                 local search method based on the training algorithms
                 for decision trees. The efficacy of the MGP is
                 empirically justified against a number of relevant
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",

Genetic Programming entries for Pu Wang Ke Tang Edward P K Tsang Xin Yao