Created by W.Langdon from gp-bibliography.bib Revision:1.8028
The problem addressed in this thesis is locating performance improvements within programs. By randomly modifying a location within a program and measuring the change in performance and functionality we determine the probability of finding a performance improvement at that location under further modification. Locating performance improvements can be performed during GP as GP relies on mutation.
A probabilistic overlay of bias values for modification emerges as GP progresses and the software evolves. Measuring different aspects of program change can fine-tune the GP algorithm To focus on code which is particularly relevant to the measured aspect. Measuring execution cost reduction can indicate where an improvement is likely to exist and increase the chances of finding an improvement during GP.",
Supervisor Stephen Barrett",
Genetic Programming entries for Brendan Cody-Kenny