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Evolution as Computation pp 95–124Cite as

Genetic Programming: Biologically Inspired Computation That Creatively Solves Non-trivial Problems

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Part of the book series: Natural Computing Series ((NCS))

Abstract

This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. Starting with a primordial ooze of thousands of randomly created computer programs, genetic programming progressively breeds a population of computer programs over a series of generations using the Darwinian principle of natural selection, recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of developmental biology. The technique is illustrated by its application to a non-trivial problem involving the automatic synthesis (design) of a lowpass filter circuit. The evolved results are competitive with human-produced solutions to the problem. In fact, four of the automatically created circuits exhibit human-level creativity and inventiveness, as evidenced by the fact that they correspond to four inventions that were patented between 1917 and 1936.

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Authors
  1. John R. Koza
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  2. Forrest H. Bennett III
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  3. David Andre
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  4. Martin A. Keane
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Editor information

Editors and Affiliations

  1. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08544, USA

    Laura F. Landweber

  2. Computer Science and Computation and Neural Systems, Caltech, Pasadena, CA, 91125, USA

    Erik Winfree

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© 2002 Springer-Verlag Berlin Heidelberg

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Koza, J.R., Bennett, F.H., Andre, D., Keane, M.A. (2002). Genetic Programming: Biologically Inspired Computation That Creatively Solves Non-trivial Problems. In: Landweber, L.F., Winfree, E. (eds) Evolution as Computation. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55606-7_5

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  • DOI: https://doi.org/10.1007/978-3-642-55606-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-63081-1

  • Online ISBN: 978-3-642-55606-7

  • eBook Packages: Springer Book Archive

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