- author = "John R. Woodward and Colin G. Johnson and Alexander E. I. Brownlee",
- title = "Connecting Automatic Parameter Tuning, Genetic Programming as a Hyper-heuristic, and Genetic Improvement Programming",
- booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation",
- year = "2016",
- isbn13 = "978-1-4503-4323-7",
- pages = "1357--1358",
- month = "20-24 " # jul,
- organisation = "SIGEVO",
- address = "Denver, Colorado, USA",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Genetic Improvement",
-
URL = "
http://hdl.handle.net/1893/23394",
-
URL = "
https://dspace.stir.ac.uk/bitstream/1893/23394/1/ecada03%20%284%29.pdf",
-
DOI = "
doi:10.1145/2908961.2931728",
-
abstract = "Automatically designing algorithms has long been a
dream of computer scientists. Early attempts which
generate computer programs from scratch, have failed to
meet this goal. However, in recent years there have
been a number of different technologies with an
alternative goal of taking existing programs and
attempting to improving them.
These methods form a range of methodologies, from the limited ability to change (for example only the parameters) to the complete ability to change the whole program. These include; automatic parameter tuning (APT), using GP as a hyper-heuristic (GPHH), and GI, which we will now briefly review. Part of research is building links between existing work, and the aim of this paper is to bring together these currently separate approaches.",
