Understanding the Evolutionary Process of Grammatical Evolution Neural Networks for Feature Selection in Genetic Epidemiology

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Alison A. Motsinger and David M. Reif and 
                 Scott M. Dudek and Marylyn D. Ritchie",
  title =        "Understanding the Evolutionary Process of Grammatical
                 Evolution Neural Networks for Feature Selection in
                 Genetic Epidemiology",
  booktitle =    "IEEE Symposium on Computational Intelligence and
                 Bioinformatics and Computational Biology, CIBCB '06",
  year =         "2006",
  editor =       "Dan Ashlock",
  pages =        "1--8",
  address =      "Toronto, Canada",
  month =        sep # " 28-29",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, chromosome
                 size, common diseases, complex diseases, evolutionary
                 learning process, evolutionary process, feature
                 selection, gene-gene interactions, genetic
                 architecture, genetic epidemiology, genetic factors,
                 grammatical evolution neural networks, human genetics,
                 random search neural network strategy, diseases,
                 evolutionary computation, genetics, learning
                 (artificial intelligence), neural nets",
  DOI =          "doi:10.1109/CIBCB.2006.330945",
  size =         "8 pages",
  abstract =     "The identification of genetic factors/features that
                 predict complex diseases is an important goal of human
                 genetics. The commonality of gene-gene interactions in
                 the underlying genetic architecture of common diseases
                 presents a daunting analytical challenge. Previously,
                 we introduced a grammatical evolution neural network
                 (GENN) approach that has high power to detect such
                 interactions in the absence of any marginal main
                 effects. While the success of this method is
                 encouraging, it elicits questions regarding the
                 evolutionary process of the algorithm itself and the
                 feasibility of scaling the method to account for the
                 immense dimensionality of datasets with enormous
                 numbers of features. When the features of interest show
                 no main effects, how is GENN able to build correct
                 models? How and when should evolutionary parameters be
                 adjusted according to the scale of a particular
                 dataset? In the current study, we monitor the
                 performance of GENN during its evolutionary process
                 using different population sizes and numbers of
                 generations. We also compare the evolutionary
                 characteristics of GENN to that of a random search
                 neural network strategy to better understand the
                 benefits provided by the evolutionary learning process
                 - including advantages with respect to chromosome size
                 and the representation of functional versus
                 non-functional features within the models generated by
                 the two approaches. Finally, we apply lessons from the
                 characterisation of GENN to analyses of datasets
                 containing increasing numbers of features to
                 demonstrate the scalability of the method",
  bibdate =      "2009-04-29",
  bibsource =    "DBLP,

Genetic Programming entries for Alison A Motsinger David M Reif Scott M Dudek Marylyn D Ritchie