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A learning machine: part I

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R. M. FriedbergAuthors Info & Claims

IBM Journal of Research and Development, Volume 2, Issue 1

Pages 2 - 13

https://doi.org/10.1147/rd.21.0002

Published: 01 January 1958 Publication History

Abstract

Machines would be more useful if they could learn to perform tasks for which they were not given precise methods. Difficulties that attend giving a machine this ability are discussed. It is proposed that the program of a stored-program computer be gradually improved by a learning procedure which tries many programs and chooses, from the instructions that may occupy a given location, the one most often associated with a successful result. An experimental test of this principle is described in detail. Preliminary results, which show limited success, are reported and interpreted. Further results and conclusions will appear in the second part of the paper.

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Carreon NLu SLysecky R(2021)Probabilistic Estimation of Threat Intrusion in Embedded Systems for Runtime DetectionACM Transactions on Embedded Computing Systems10.1145/343259020:2(1-27)Online publication date: 4-Jan-2021
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Solgi RLoáiciga H(2021)Bee-inspired metaheuristics for global optimization: a performance comparisonArtificial Intelligence Review10.1007/s10462-021-10015-154:7(4967-4996)Online publication date: 1-Oct-2021
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cover image IBM Journal of Research and Development

IBM Journal of Research and Development Volume 2, Issue 1

January 1958

82 pages

ISSN:0018-8646

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IBM Corp.

United States

Publication History

Published: 01 January 1958

Received: 14 November 1957

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Langdon W(2023)Jaws 30Genetic Programming and Evolvable Machines10.1007/s10710-023-09467-x24:2Online publication date: 22-Nov-2023
https://dl.acm.org/doi/10.1007/s10710-023-09467-x
Carreon NLu SLysecky R(2021)Probabilistic Estimation of Threat Intrusion in Embedded Systems for Runtime DetectionACM Transactions on Embedded Computing Systems10.1145/343259020:2(1-27)Online publication date: 4-Jan-2021
https://dl.acm.org/doi/10.1145/3432590
Solgi RLoáiciga H(2021)Bee-inspired metaheuristics for global optimization: a performance comparisonArtificial Intelligence Review10.1007/s10462-021-10015-154:7(4967-4996)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s10462-021-10015-1
O’Neill MSpector L(2020)Automatic programming: The open issue?Genetic Programming and Evolvable Machines10.1007/s10710-019-09364-221:1-2(251-262)Online publication date: 1-Jun-2020
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Rostami MSadrossadat EGhorbani BKazemi S(2018)New empirical formulations for indirect estimation of peak-confined compressive strength and strain of circular RC columns using LGP methodEngineering with Computers10.1007/s00366-018-0577-734:4(865-880)Online publication date: 1-Oct-2018
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Forsyth R(2017)The genesis of genetic programmingACM SIGEVOlution10.1145/3066862.30668639:3(3-11)Online publication date: 17-Mar-2017
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Meng ZPan J(2016)A Competitive QUasi-Affine TRansformation Evolutionary (C-QUATRE) Algorithm for global optimization2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2016.7844474(001644-001649)Online publication date: 9-Oct-2016
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