- 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 = "
http://www.genetic-programming.com/jkpdf/tr1314.pdf",
-
URL = "
http://www-db.stanford.edu/TR/CS-TR-90-1314.html",
-
URL = "ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/90/1314/CS-TR-90-1314.pdf",
- 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 = "
http://i.stanford.edu/pub/cstr/reports/cs/tr/90/1314/ also contains GIF and MAC versions",
