Strongly Typed Evolutionary Programming

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

@PhdThesis{ClaireJKennedy:thesis,
  author =       "Claire Julia Kennedy",
  title =        "Strongly Typed Evolutionary Programming",
  school =       "Computer Science, University of Bristol",
  year =         "1999",
  address =      "UK",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, STGP",
  URL =          "http://www.cs.bris.ac.uk/Publications/Papers/1000461.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/claire_j_kennedy/kennedy_phdthesis.pdf",
  size =         "183 pages",
  abstract =     "As the potential of applying machine learning
                 techniques to perplexing problems is realised,
                 increasingly complex problems are being tackled,
                 requiring intricate explanations to be induced. Escher
                 is a functional logic language whose higher-order
                 constructs allow arbitrarily complex observations to be
                 captured and highly expressive generalisations to be
                 conveyed.

                 The work presented in this thesis alleviates the
                 challenging problem of identifying an underlying
                 structure normally required to search the resulting
                 hypothesis space efficiently. This is achieved through
                 STEPS, an evolutionary based system that allows the
                 vast space of highly expressive Escher programs to be
                 explored. STEPS provides a natural upgrade of the
                 evolution of concept descriptions to the higher-order
                 level.

                 In particular STEPS uses the individual-as-terms
                 approach to knowledge representation where all the
                 information provided by an example is localised as a
                 single closed term so that examples of arbitrary
                 complexity can be treated in a uniform manner. STEPS
                 also supports Lambda abstractions as arguments to
                 higher-order functions thus enabling the invention of
                 new functions not contained in the original alphabet.
                 Finally, STEPS provides a number of specialised genetic
                 operators for the design of specific concept learning
                 strategies.

                 STEPS has been successfully applied to a number of
                 complex real world problems, including the
                 international PTE2 challenge. This problem involves the
                 prediction of the Carcinogenic activity of a test set
                 of 30 chemical compounds. The results produced by STEPS
                 rank joint second if the hypothesis must be
                 interpretable and joint first if interpretability is
                 sacrificed for increased accuracy.",
}

Genetic Programming entries for Claire J Kennedy

Citations