Hardware accelerated genetic programming for pattern mining in strings

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

@PhdThesis{drphil-satrom-05,
  author =       "Pal Saetrom",
  title =        "Hardware accelerated genetic programming for pattern
                 mining in strings",
  school =       "Faculty of Information Technology, Mathematics and
                 Electrical Engineering Department of Computer and
                 Information Science, Norwegian University of Science
                 and Technology, NTNU",
  year =         "2005?",
  type =         "Dr.philos thesis",
  address =      "Norway",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.idi.ntnu.no/grupper/su/publ/phd/drphil-satrom-05.pdf",
  size =         "162 pages",
  abstract =     "This thesis considers the problem of mining patterns
                 in strings. Informally, this is the problem of
                 extracting information (patterns) that characterises
                 parts of, or even the complete, string. The thesis
                 describes a high performance hardware for string
                 searching, which together with genetic programming,
                 forms the basis for the thesis' pattern mining
                 algorithms.

                 This work considers two different pattern mining
                 problems and develops several different algorithms to
                 solve different variants of these problems. Common to
                 all algorithms is that they use genetic programming to
                 evolve patterns that can be evaluated by the special
                 purpose search hardware.

                 The first pattern mining problem considered is
                 unsupervised mining of prediction rules in discretised
                 time series. Such prediction rules describe relations
                 between consecutive patterns in the discretized time
                 series; that is, the prediction rules state that if the
                 first pattern occurs, the second pattern will, with
                 high probability, follow within a fixed number of
                 symbols. The goal is to automatically extract
                 prediction rules that are accurate, comprehensible, and
                 interesting.

                 The second pattern mining problem considered is
                 supervised learning of classifiers that predict whether
                 or not a given string belongs to a specific class of
                 strings. This binary classification problem is very
                 general, but this thesis focuses on two recent problems
                 from molecular biology: i) predicting the efficacy of
                 short interfering RNAs and antisense oligonucleotides;
                 and ii) predicting whether or not a given DNA sequence
                 is a non-coding RNA gene. The thesis describes a
                 genetic programming-based mining algorithm that produce
                 state-of-the-art classifiers on both problems.",
  notes =        "cf
                 http://www.diva-portal.org/ntnu/theses/abstract.xsql?dbid=713",
}

Genetic Programming entries for Pal Saetrom

Citations