Selected Completed Honours Thesis: Evolutionary Computation
Novelty-based Fitness Measures in Genetic Programming:
Abstract: The utility of the class of non-qualitative fitness measures known as
measures is considered with respect to Genetic Programming (GP). Previously used
benchmarks from GP and from other work on novelty-based search heuristics are
used. The resulting data suggest that novelty-based measures may be useful when
solution robustness is essential, but that overall, they may not be as powerful in the
context of GP as previous work suggests they are in other areas of machine learning.
The introduction of factors found in other machine learning models for which novelty-
based measures preformed well previously did not improve the performance of GP
when novelty-based measures were used, or produced inconclusive results, depending
on the benchmark problem used.