Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
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 b...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Stanford, Calif.
1990
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| Schriftenreihe: | Stanford University / Computer Science Department: Report STAN CS
1314 |
| Schlagworte: | |
| Zusammenfassung: | 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 [sic] 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 strategy for a discrete game in extensive form. |
| Beschreibung: | 126 S. graph. Darst. |
Internformat
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| 245 | 1 | 0 | |a Genetic programming |b a paradigm for genetically breeding populations of computer programs to solve problems |
| 264 | 1 | |a Stanford, Calif. |c 1990 | |
| 300 | |a 126 S. |b graph. Darst. | ||
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| 337 | |b n |2 rdamedia | ||
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| 490 | 1 | |a Stanford University / Computer Science Department: Report STAN CS |v 1314 | |
| 520 | 3 | |a 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 | |
| 520 | 3 | |a 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 | |
| 520 | 3 | |a Examples come from the areas of machine learning of a function; planning; sequence induction; function function [sic] 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 strategy for a discrete game in extensive form. | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Computer programs | |
| 650 | 4 | |a Machine learning | |
| 810 | 2 | |a Computer Science Department: Report STAN CS |t Stanford University |v 1314 |w (DE-604)BV008928280 |9 1314 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-005905611 | ||
Datensatz im Suchindex
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| author | Koza, John R. |
| author_GND | (DE-588)1193602319 |
| author_facet | Koza, John R. |
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| callnumber-subject | QA - Mathematics |
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| id | DE-604.BV008950007 |
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| indexdate | 2024-07-09T17:27:18Z |
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| language | English |
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| physical | 126 S. graph. Darst. |
| publishDate | 1990 |
| publishDateSearch | 1990 |
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| series2 | Stanford University / Computer Science Department: Report STAN CS |
| spelling | Koza, John R. Verfasser (DE-588)1193602319 aut Genetic programming a paradigm for genetically breeding populations of computer programs to solve problems Stanford, Calif. 1990 126 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Stanford University / Computer Science Department: Report STAN CS 1314 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 [sic] 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 strategy for a discrete game in extensive form. Künstliche Intelligenz Artificial intelligence Computer programs Machine learning Computer Science Department: Report STAN CS Stanford University 1314 (DE-604)BV008928280 1314 |
| spellingShingle | Koza, John R. Genetic programming a paradigm for genetically breeding populations of computer programs to solve problems Künstliche Intelligenz Artificial intelligence Computer programs Machine learning |
| title | Genetic programming a paradigm for genetically breeding populations of computer programs to solve problems |
| title_auth | Genetic programming a paradigm for genetically breeding populations of computer programs to solve problems |
| title_exact_search | Genetic programming a paradigm for genetically breeding populations of computer programs to solve problems |
| title_full | Genetic programming a paradigm for genetically breeding populations of computer programs to solve problems |
| title_fullStr | Genetic programming a paradigm for genetically breeding populations of computer programs to solve problems |
| title_full_unstemmed | Genetic programming a paradigm for genetically breeding populations of computer programs to solve problems |
| title_short | Genetic programming |
| title_sort | genetic programming a paradigm for genetically breeding populations of computer programs to solve problems |
| title_sub | a paradigm for genetically breeding populations of computer programs to solve problems |
| topic | Künstliche Intelligenz Artificial intelligence Computer programs Machine learning |
| topic_facet | Künstliche Intelligenz Artificial intelligence Computer programs Machine learning |
| volume_link | (DE-604)BV008928280 |
| work_keys_str_mv | AT kozajohnr geneticprogrammingaparadigmforgeneticallybreedingpopulationsofcomputerprogramstosolveproblems |