Virtual Reality Visual Data Mining via Neural Networks obtained from Multi-objective Evolutionary Optimization: Application to Geophysical Prospecting

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{valdes:2006:IJCNN,
  author =       "Julio J. Valdes and Alan J. Barton",
  title =        "Virtual Reality Visual Data Mining via Neural Networks
                 obtained from Multi-objective Evolutionary
                 Optimization: Application to Geophysical Prospecting",
  booktitle =    "International Joint Conference on Neural Networks,
                 IJCNN'06",
  year =         "2006",
  pages =        "4862--4869",
  address =      "Sheraton Vancouver Wall Centre Hotel, Vancouver, BC,
                 Canada",
  month =        "16-21 " # jul,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  DOI =          "doi:10.1109/IJCNN.2006.247165",
  abstract =     "A method for the construction of Virtual Reality
                 spaces for visual data mining using multi-objective
                 optimisation with genetic algorithms on non-linear
                 discriminant (NDA) neural networks is presented. Two
                 neural network layers (output and last hidden) are used
                 for the construction of simultaneous solutions for: a
                 supervised classification of data patterns and an
                 unsupervised similarity structure preservation between
                 the original data matrix and its image in the new
                 space. A set of spaces are constructed from selected
                 solutions along the Pareto front. This strategy
                 represents a conceptual improvement over spaces
                 computed by single-objective optimisation. In addition,
                 genetic programming (in particular gene expression
                 programming) is used for finding analytic
                 representations of the complex mappings generating the
                 spaces (a composition of NDA and orthogonal principal
                 components). The presented approach is domain
                 independent and is illustrated via application to the
                 geophysical prospecting of caves.",
}

Genetic Programming entries for Julio J Valdes Alan J Barton

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