Intelligent Techniques for Data Modeling Problems

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

@PhdThesis{bautu:thesis,
  author =       "Elena Bautu",
  title =        "Intelligent Techniques for Data Modeling Problems",
  school =       "Al. I. Cuza University",
  year =         "2010",
  address =      "Iasi, Romania",
  month =        jun,
  note =         "Romanian subtitle is Programare genetica pentru
                 probleme de optimizare in Inteligenta artificiala",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, inverse problems, financial
                 forecasting, data analysis, hypernetwork,
                 hybridization",
  URL =          "https://sites.google.com/site/ebautu/home/publications/thesis/thesis_elena_bautu.pdf",
  URL =          "https://sites.google.com/site/ebautu/home/publications/thesis",
  size =         "220 pages",
  abstract =     "Supervised learning deals with the problem of
                 discovering models from data as relationships between
                 input and output attributes. Two types of models are
                 distinguished: regression models for continuous output
                 and classification models (classiffiers) for discrete
                 output. This thesis addresses both regression and
                 classiffication problems, with an emphasis on new
                 applications and on proposing new evolutionary
                 techniques.

                 First, we address the regression domain. Symbolic
                 regression by means of evolutionary techniques is
                 recommended when there is little or no a priori
                 information on the modelled process. It relies on a set
                 of input-output observations to infer mathematical
                 models, posing no constraints on the structure, the
                 coefficients or the size of the model. We introduce
                 inverse problems modeled by Fredholm integral equations
                 of the first kind and the inverse problem of log
                 synthesis to be modelled by symbolic regression by
                 means of gene expression programming. A new genetic
                 programming scheme is formulated for the problem of
                 automatically designing quantum circuits. An adaptive
                 version of the gene expression programming algorithm is
                 presented, which automatically tunes the complexity of
                 the model by a gene (de)activation mechanism. For
                 modelling time series produced by dynamic processes, we
                 propose an evolutionary approach that uses a novel
                 representation (and suitable genetic operators) to
                 partition the time series based on change points.
                 Empirical results prove the approach to be
                 promising.

                 Research on building classifiers for a given problem is
                 also extensive, since there exists no best classifier
                 at all tasks. The problem of predicting the direction
                 of change of stock price on the market can be
                 formulated as the search for a classifier that links
                 past evolution to an increase or decrease. We explore
                 new techniques for classification, in the context of
                 predicting the direction of change of stock price,
                 formulated as a binary classification",
}

Genetic Programming entries for Elena Bautu

Citations