- 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 problems.",
- notes = "Supervisor: Dr. Conor Ryan",
