PhD thesis,
Topics in Soft Computing
Department of Numerical Analysis and Computer Science, Royal Institute of Technology, Stockholm, (January 2002)
Abstract
This thesis discusses visual programming languages,
representation of uncertainty in geographical data and
a combination of genetic programming and
optimisation.
A new visual programming language is described, based
on a novel version of the dataflow paradigm. In this
version, cyclic graphs are replaced with nested graphs,
which also have other uses. Furthermore, the programs
become more structured, readable and scalable. This
language is then formally defined using a novel
extension of plex grammars.
Various representations of uncertainty in geographical
data are discussed, including some novel ones based on
rough sets. Various novel measures are developed, and
used in two experiments that verify the usefulness of
the representations chosen. Furthermore, a novel theory
of topological relations between uncertain data is
presented.
A novel combination of genetic programming and
optimization is presented. This has been implemented in
a system that is in actual use. The system is
described, as is the combination. An experiment has
been done to test the performance of this combination,
and in this experiment it performed better than plain
genetic programming.
