- author = "Lawrence Charles John Beadle",
- title = "Semantic and Structural Analysis of Genetic Programming",
- school = "School of Computing, University of Kent",
- year = "2009",
- address = "Canterbury, UK",
- month = jul,
- keywords = "genetic algorithms, genetic programming, determinacy analysis, Craig interpolants",
-
URL = "
http://www.beadle.me/Me/LBeadle_PhD_Thesis.pdf",
-
URL = "
http://ethos.bl.uk/OrderDetails.do?did=24&uin=uk.bl.ethos.509628",
-
URL = "https://kar.kent.ac.uk/id/eprint/30599",
-
URL = "http://www.cs.kent.ac.uk/pubs/2009/3056/",
-
URL = "http://www.cs.kent.ac.uk/pubs/2009/3056/content.pdf",
- size = "194 pages",
-
abstract = "Genetic programming (GP) is a subset of evolutionary
computation where candidate solutions are evaluated
through execution or interpreted execution. The
candidate solutions generated by GP are in the form of
computer programs, which are evolved to achieve a
stated objective. Darwinian evolutionary theory
inspires the processes that make up GP which include
crossover, mutation and selection. During a GP run,
crossover, mutation and selection are performed
iteratively until a program that satisfies the stated
objectives is produced or a certain number of time
steps have elapsed.
The objectives of this thesis are to empirically analyse three different aspects of these evolved programs. These three aspects are diversity, efficient representation and the changing structure of programs during evolution. In addition to these analyses, novel algorithms are presented in order to test theories, improve the overall performance of GP and reduce program size.
This thesis makes three contributions to the field of GP. Firstly, a detailed analysis is performed of the process of initialisation (generating random programs to start evolution) using four novel algorithms to empirically evaluate specific traits of starting populations of programs. It is shown how two factors simultaneously effect how strong the performance of starting population will be after a GP run. Secondly, semantically based operators are applied during evolution to encourage behavioural diversity and reduce the size of programs by removing inefficient segments of code during evolution. It is demonstrated how these specialist operators can be effective individually and when combined in a series of experiments. Finally, the role of the structure of programs is considered during evolution under different evolutionary parameters considering different problem domains. This analysis reveals some interesting effects of evolution on program structure as well as offering evidence to support the success of the specialist operators.",
-
notes = "EpochX GP software
Supervisor: Colin Johnson
uk.bl.ethos.509628",
