Developing and evaluating incremental evolution using high quality performance measures for genetic programming : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosphy in Computer Science at Massey University, Albany, Auckland, New Zealand
This thesis is divided into two parts. The first part considers and develops some
of the statistics used in genetic programming (GP) while the second uses those
statistics to study and develop a form of incremental evolution and an early
termination heuristic for GP.
The first part looks in detail at success proportion, Koza's minimum computational
effort, and a measure we rename "success effort". We describe and
develop methods to produce confidence intervals for these measures as well as
confidence intervals for the difference and ratio of these measures.
The second part studies Jackson's fitness-based incremental evolution. If the
number of fitness evaluations are considered (rather than the number of generations)
then we find some potential benefit through reduction in the effort required
to find a solution. We then automate the incremental evolution method and show
a statistically significant improvement compared to GP with automatically
defined functions (ADFs).
The success effort measure is shown to have the critical advantage over Koza's
measure as it has the ability to include a decreasing cost of failure. We capitalise
on this advantage by demonstrating an early termination heuristic that again
offers a statistically significant advantage.