- author = "Daan {van Laar}",
- title = "Fitness Landscape Analysis applied to functional Genetic Improvement",
- school = "Utrecht University",
- year = "2021",
- address = "The Netherlands",
- month = "15 " # nov,
- keywords = "genetic algorithms, genetic programming, genetic improvement, PyGGI, Fitness Landscape Analysis, Evolutionary Computing, Program Transformation, Computing Science",
-
URL = "
https://studenttheses.uu.nl/handle/20.500.12932/224",
-
URL = "
https://studenttheses.uu.nl/bitstream/handle/20.500.12932/224/Thesis_vFinal.pdf",
- size = "43 pages",
-
abstract = "Genetic Improvement is the concept of a computer
improving human-written code. This improves either the
functional or the non-functional properties of the
program. Genetic Improvement uses mutations to find
improved versions of the original program. This makes
the search space for Genetic Improvement very large.
Furthermore for functional improvement, the fitness
landscape forms large plateaus. In this masters thesis,
we will attempt to analyse the search space of Genetic
Improvement using Fitness Landscape Analysis techniques
to achieve a better understanding of the search
space.
To achieve this, we have edited the PyGGI framework to perform a random walk, and to analyse how large plateaus are. The PyGGI framework has been edited in such a way that it suits our needs and has such a performance that the experiments can be concluded in a reasonable amount of time. We perform the Genetic Improvement process on programs selected from the Bears benchmark, which contains many programs with bugs and test suites.
The results of this masters thesis conclude that while the plateaus are near-infinitely big, a random walk over the plateau often finds the global optimum. The only cases where the global optimum could not be found are the experiments which could not be improved with the used set of mutations. These results are in line with similar results in researches in this area.",
-
notes = "Fernanda Madeiral, Simon Urli, Marcelo Maia, and
Martin Monperrus. BEARS: An Extensible Java Bug
Benchmark for Automatic Program Repair Studies.
First supervisor: Dirk Thierens",
