@techreport(langdon:1995:survey,
  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 =	{October},
  note =	{},
%ignored by BibTex but available for automatic searching
% email =	{},
  keywords =    {genetic algorithms, genetic programming, 
Automatic Programming, Machine Learning}, 
  url =         {ftp://cs.ucl.ac.uk/genetic/papers/surveyRN76.ps},
  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.
},
  notes = 	{},
  size = 	{45 pages}
)