Evolutionary Optimisation and Prediction in Subjective Problem Domains

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Dan Costelloe",
  title =        "Evolutionary Optimisation and Prediction in Subjective
                 Problem Domains",
  school =       "University of Limerick",
  year =         "2009",
  address =      "Limerick, Ireland",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "https://digitary.ul.ie/verifier/servlet/DocumentVerifierApp/template/VerifyDAT.vm?datid=k7aahpcxm1",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Costelloe_thesis.pdf",
  size =         "158 pages",
  abstract =     "Artificial Evolution is a powerful tool for generating
                 realistic solutions to a large range of computationally
                 difficult problems. It has been applied with great
                 success to many optimisation problems in engineering
                 and science, yet its application is not restricted to
                 problems specific to these fields. The power of
                 evolution can also be coupled with human supervision to
                 tackle problems whose solutions must be (wholly or
                 partly) subjectively evaluated. This thesis describes
                 the design, implementation and use of
                 Evolutionary-based system used for the evolution of
                 such entities whose 'goodness' is commonly only
                 subjectively defined.

                 Additionally, this research investigates and tests
                 formal models of subjective notions for a specific
                 problem: the Interactive Evolution of music. It is
                 demonstrated by this research how various evolutionary
                 techniques can be used to generate and evolve pleasing
                 musical sequences. It is also shown how similar
                 techniques are used to build models of the subjective
                 notions used by human users, when evaluating the
                 goodness of musical pieces. The research presented here
                 also makes it possible to understand what environmental
                 conditions lead to the construction of artificial
                 models that have good predictive power.

                 Finally, an investigation of the generalisation
                 performance of a specific Evolutionary technique,
                 Genetic Programming, is presented in the context of
                 more recently developed improvement techniques. It is
                 demonstrated that any improvement must take
                 generalisation performance into account in order to be
                 considered a worthy addition to the field. It is also
                 shown how a combination of recent improvement
                 techniques make significant performance improvements on
                 both artificial and real-world symbolic regression
  notes =        "Supervisor: Dr. Conor Ryan",

Genetic Programming entries for Dan Costelloe