Genetic Programming -- Computers using ``Natural Selection'' to generate programs   [GP] [NS]

by

Langdon, W., B. and Qureshi, A.

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Info: 1995
Keywords:genetic algorithms, genetic programming, Automatic Programming, Machine Learning
Abstract:
Computers that ``program themselves''; science fact or fiction? Genetic Programming [GP] 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). [GA] [GAG] Genetic Algorithms, [GA] pioneered by Holland, Goldberg and others, are evolutionary search techniques [ES] inspired by natural selection [NS] (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 [GP] applies GAs to a ``population'' of programs - typically encoded as tree-structures. Trial programs are evaluated against a ``fitness function'' [FF] 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. [GP] As a basis it reviews Genetic Algorithms [GA] and automatic program generation. Next it introduces Genetic Programming, [GP] 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.
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BibTex:
@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 =        oct,
  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.",
  size =         "45 pages",
}