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The focus of this thesis is on understanding the genetic programming
population, the ways it can be measured and the role it plays
on guiding the search process. Specifically, the diversity of
the population is analysed to uncover key features and
relationships that make search successful. In the process
of developing a better understanding of genetic programming,
a clearer description of the dynamics of the algorithm emerges
to facilitate and motivate future enhancements.
The existing theoretical models of evolutionary algorithms are
limited in use and applicability due to their complexity.
Therefore, the majority of theoretical work has
been derived from experimentation.
The approach taken in this thesis is also based on the careful
design, collection and analysis of experimental results.