- author = "William B. Langdon and Adil Qureshi",
- title = "Genetic Programming -- Computers using ``Natural Selection'' to generate programs",
- institution = "University College London",
- year = "1995",
- type = "Research Note",
- number = "RN/95/76",
- address = "Gower Street, London WC1E 6BT, UK",
- month = oct,
- keywords = "genetic algorithms, genetic programming, Automatic Programming, Machine Learning",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/surveyRN76.pdf",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/surveyRN76.ps",
-
URL = "
http://www.cs.ucl.ac.uk/research/research_notes/research_note_archive/1995/",
-
abstract = "Computers that ``program themselves''; science fact or
fiction? Genetic Programming uses novel optimisation
techniques to ``evolve'' simple programs; mimicking the
way humans construct programs by progressively
re-writing them. Trial programs are repeatedly modified
in the search for ``better/fitter'' solutions. The
underlying basis is Genetic Algorithms (GAs).
Genetic Algorithms, pioneered by Holland, Goldberg and others, are evolutionary search techniques inspired by natural selection (i.e\ survival of the fittest). GAs work with a ``population'' of trial solutions to a problem, frequently encoded as strings, and repeatedly select the ``fitter'' solutions, attempting to evolve better ones. The power of GAs is being demonstrated for an increasing range of applications; financial, imaging, VLSI circuit layout, gas pipeline control and production scheduling. But one of the most intriguing uses of GAs - driven by Koza - is automatic program generation.
Genetic Programming applies GAs to a ``population'' of programs - typically encoded as tree-structures. Trial programs are evaluated against a ``fitness function'' and the best solutions selected for modification and re-evaluation. This modification-evaluation cycle is repeated until a ``correct'' program is produced. GP has demonstrated its potential by evolving simple programs for medical signal filters, classifying news stories, performing optical character recognition, and for target identification.
This paper surveys the exciting field of Genetic Programming. As a basis it reviews Genetic Algorithms and automatic program generation. Next it introduces Genetic Programming, describing its history and describing the technique via a worked example in C. Then using a taxonomy that divides GP researchs into theory/techniques and applications, it surveys recent work from both of these perspectives.
Extensive bibliographies, glossaries and a resource list are included as appendices.",
- size = "45 pages",
