Automatically Discovering Solutions that Flexibly Combine Iterative and non-Iterative Computations

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

@PhdThesis{handley:thesis,
  author =       "Simon G. Handley",
  title =        "Automatically Discovering Solutions that Flexibly
                 Combine Iterative and non-Iterative Computations",
  school =       "Department of Computer Science, Stanford University",
  year =         "1997",
  address =      "USA",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, ADF,
                 computational biology, coiled coils, SCZ, E.Coli
                 promoters, intron v exon, pinochie poker",
  URL =          "http://searchworks.stanford.edu/view/3911278",
  URL =          "http://search.proquest.com/docview/304454352",
  size =         "513 pages",
  abstract =     "This thesis investigates computational techniques that
                 automatically discover solutions to problems. In
                 particular, this thesis focuses on the discovery of
                 solutions that flexibly combine iterative and
                 non-iterative computations. Consider, for example, the
                 problem of assigning poker hands to classes (such as
                 full-house or two-pairs). This classification is a
                 mapping $f\sb{\rm PKR}$: Hand $\mapsto$ Class where
                 Hand = $\lbrack C\sb1,C\sb2,\...,C\sb{n}\rbrack,$ the
                 $C\sb{i}$ are cards, n is typically five and $\rm
                 Class\in\{royal-flush,\...,high-card\}.$ A solution to
                 this problem will likely do computations such as 'count
                 up the number of Aces', 'how many cards occur 3 or more
                 times?' and 'is the frequency of occurrence of rank x
                 equal to 2?'. We investigate four techniques: three
                 adaptations of existing techniques and one new
                 technique that partially addresses concerns with the
                 other three techniques. These techniques automatically
                 discover solutions that combine iterative and
                 non-iterative computations with varying degrees of
                 flexibility. All four techniques are based on genetic
                 programming, an evolutionary algorithm. The appropriate
                 criteria for analysing these techniques are discussed.
                 One important design criterion is the degree to which
                 representational flexibility is traded-off for run-time
                 predictability. This trade-off is observed in many
                 solution discovery techniques: solutions that are drawn
                 from a search space with a high degree of
                 representational freedom often have execution times
                 that are difficult to predict. The techniques are
                 demonstrated on the following problems: computing
                 parity, classifying poker hands, generating hypotheses
                 about coiled-coil regions, recognizing splice sites,
                 parsing genes, recognizing E. coli promoters, secondary
                 structure prediction, and predicting the degree of
                 exposure to solvent of amino acid residues. By
                 examining the problems that worked, and those that did
                 not, we gained an understanding of (a) why these
                 techniques work, (b) the types of problems on which
                 they work, and (c) the types of problems on which they
                 don't work.",
  notes =        "John R. Koza, primary
                 advisor.

                 http://genealogy.math.ndsu.nodak.edu/id.php?id=71265

                 Special Collections - University Archives Request at
                 service desk 3781 1998 H must be paged/requested for
                 in-library use only

                 UMI Microform 9837102

                 OCLC Number: 77834341

                 Copyright 1997 by Simon Graham Handley",
}

Genetic Programming entries for Simon G Handley

Citations