Recombination, Selection, and the Genetic Construction of Computer Programs

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

@PhdThesis{Tackett:1994:thesis,
  author =       "Walter Alden Tackett",
  title =        "Recombination, Selection, and the Genetic Construction
                 of Computer Programs",
  school =       "University of Southern California, Department of
                 Electrical Engineering Systems",
  year =         "1994",
  address =      "USA",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/WAT_PHD_DissFull_USC94_Recombination_etc_Genetic_Construction_of_Computer_Programs.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/watphd.tar.Z",
  URL =          "http://digitallibrary.usc.edu/cdm/ref/collection/p15799coll20/id/187980",
  size =         "167 pages",
  abstract =     "Computational intelligence seeks as a basic goal to
                 create artificial systems which mimic aspects of
                 biological adaptation, behavior, perception, and
                 reasoning. Toward that goal, genetic program induction
                 - 'Genetic Programming' - has succeeded in automating
                 an activity traditionally considered to be the realm of
                 creative human endeavor. It has been applied
                 successfully to the creation of computer programs which
                 solve a diverse set of model problems. This naturally
                 leads to questions such as:

                 * Why does it work? * How does it fundamentally differ
                 from existing methods?

                 * What can it do that existing methods cannot?

                 The research described here seeks to answer those
                 questions through investigations on several fronts.
                 Analysis is performed which shows that Genetic
                 Programming has a great deal in common with heuristic
                 search, long studied in the field of Artificial
                 Intelligence. It introduces a novel aspect to that
                 method in the form of the recombination operator which
                 generates successors by combining parts of favorable
                 strategies. On another track, we show that Genetic
                 Programming is a powerful tool which is suitable for
                 real-world problems. This done first by applying it to
                 an extremely difficult induction problem and measuring
                 performance against other state-of-the-art methods. We
                 continue by formulating a model induction problem which
                 not only captures the pathologies of the real world,
                 but also parameterizes them so that variation in
                 performance can be measured as a function of
                 confounding factors. At the same time, we study how the
                 properties of search can be varied through the effects
                 of the selection operator. Combining the lessons of the
                 search analysis with known properties of biological
                 systems leads to the formulation of a new recombination
                 operator which is shown to improve induction
                 performance. In support of the analysis of selection
                 and recombination, we define problems in which
                 structure is precisely controlled. These allow fine
                 discrimination of search performance which help to
                 validate analytic predictions. Finally, we address a
                 truly unique aspect of Genetic Programming, namely the
                 exploitation of symbolic procedural knowledge in order
                 to provide 'explanations' from genetic programs.",
  notes =        "Also available as Available as Technical Report CENG
                 94-13, Dept. of Electrical Engineering Systems,
                 University of Southern California, April 1994.

                 Aug 2016 Use tar zxvf watphd.tar.Z to extract README
                 and three .ps from compressed file",
}

Genetic Programming entries for Walter Alden Tackett

Citations