Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems   [GP]

by

Koza, J., R.

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Info: 1990
Keywords:genetic algorithms, genetic programming
Abstract:
Many seemingly different problems in artificial intelligence, symbolic processing, [AI] and machine learning [ML] can be viewed as requiring discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a most fit individual computer program. The new "genetic programming" paradigm described herein provides a way to search for this most fit individual computer program. In this new "genetic programming" paradigm, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (recombination) operator appropriate for genetically mating computer programs. In this paper, the process of formulating and solving problems using this new paradigm is illustrated using examples from various areas. Examples come from the areas of machine learning [ML] of a function; planning; sequence induction; function function identification (including symbolic regression, empirical discovery, [SR] "data to function" symbolic integration, "data to function" symbolic differentiation); solving equations, including differential equations, [DE] integral equations, and functional equations); concept formation; automatic programming; pattern recognition, [AP] [PR] time-optimal control; playing differential pursuer-evader games; neural network design; [NN] [ND] and finding a game-playing strategyfor a discrete game in extensive form.
Notes:
ftp://elib.stanford.edu/pub/reports/cs/tr/90/1314 also contains GIF and MAC versions
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BibTex:
@TechReport{koza-90,
  key =          "Koza",
  author =       "J. Koza",
  title =        "Genetic programming: {A} paradigm for genetically
                 breeding populations of computer programs to solve
                 problems",
  type =         "Technical Report",
  number =       "{STAN}-{CS}-90-1314",
  institution =  "Dept. of Computer Science, Stanford University",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://elib.stanford.edu/pub/reports/cs/tr/90/1314/CS-TR-90-1314.OCR.txt",
  URL =          "ftp://elib.stanford.edu/pub/reports/cs/tr/90/1314/CS-TR-90-1314.ps",
  month =        jun,
  year =         "1990",
  abstract =     "Many seemingly different problems in artificial
                 intelligence, symbolic processing, and machine learning
                 can be viewed as requiring discovery of a computer
                 program that produces some desired output for
                 particular inputs. When viewed in this way, the process
                 of solving these problems becomes equivalent to
                 searching a space of possible computer programs for a
                 most fit individual computer program. The new
                 {"}genetic programming{"} paradigm described herein
                 provides a way to search for this most fit individual
                 computer program. In this new {"}genetic programming{"}
                 paradigm, populations of computer programs are
                 genetically bred using the Darwinian principle of
                 survival of the fittest and using a genetic crossover
                 (recombination) operator appropriate for genetically
                 mating computer programs. In this paper, the process of
                 formulating and solving problems using this new
                 paradigm is illustrated using examples from various
                 areas.

                 Examples come from the areas of machine learning of a
                 function; planning; sequence induction; function
                 function identification (including symbolic regression,
                 empirical discovery, {"}data to function{"} symbolic
                 integration, {"}data to function{"} symbolic
                 differentiation); solving equations, including
                 differential equations, integral equations, and
                 functional equations); concept formation; automatic
                 programming; pattern recognition, time-optimal control;
                 playing differential pursuer-evader games; neural
                 network design; and finding a game-playing strategyfor
                 a discrete game in extensive form.",
  size =         "133 pages",
  notes =        "ftp://elib.stanford.edu/pub/reports/cs/tr/90/1314 also
                 contains GIF and MAC versions",
}