Sampling Bias in Estimation of Distribution Algorithms for Genetic Programming Using Prototype Trees

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/pricai/KimMP10,
  title =        "Sampling Bias in Estimation of Distribution Algorithms
                 for Genetic Programming Using Prototype Trees",
  author =       "Kangil Kim and R. I. (Bob) McKay and 
                 Dharani Punithan",
  booktitle =    "{PRICAI} 2010: Trends in Artificial Intelligence, 11th
                 Pacific Rim International Conference on Artificial
                 Intelligence, Daegu, Korea, August 30-September 2,
                 2010. Proceedings",
  publisher =    "Springer",
  year =         "2010",
  volume =       "6230",
  editor =       "Byoung-Tak Zhang and Mehmet A. Orgun",
  isbn13 =       "978-3-642-15245-0",
  pages =        "100--111",
  series =       "Lecture Notes in Computer Science",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dx.doi.org/10.1007/978-3-642-15246-7",
  DOI =          "doi:10.1007/978-3-642-15246-7_12",
  abstract =     "Probabilistic models are widely used in evolutionary
                 and related algorithms. In Genetic Programming (GP),
                 the Probabilistic Prototype Tree (PPT) is often used as
                 a model representation. Drift due to sampling bias is a
                 widely recognised problem, and may be serious,
                 particularly in dependent probability models. While
                 this has been closely studied in independent
                 probability models, and more recently in probabilistic
                 dependency models, it has received little attention in
                 systems with strict dependence between probabilistic
                 variables such as arise in PPT representation. Here, we
                 investigate this issue, and present results suggesting
                 that the drift effect in such models may be
                 particularly severe; so severe as to cast doubt on
                 their scalability. We present a preliminary analysis
                 through a factor representation of the joint
                 probability distribution. We suggest future directions
                 for research aiming to overcome this problem",
  bibdate =      "2010-08-27",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/pricai/pricai2010.html#KimMP10",
}

Genetic Programming entries for Kangil Kim R I (Bob) McKay Dharani Punithan

Citations