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ON LEARNING kDNF^s_n BOOLEAN FORMULAS
Long Beach, Cailfornia July 12-July 14
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/EH.2001.937967The Third NASA/DoD Workshop on Evolva ...
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Arturo Hernandez-Aguirre, Bill P. Buckles, Carlos A. Coello Coello, "ON LEARNING kDNF^s_n BOOLEAN FORMULAS," Evolvable Hardware, NASA/DoD Conference on, pp. 0240, The Third NASA/DoD Workshop on Evolvable Hardware, 2001.
 
 
BibTex
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@article{ 10.1109/EH.2001.937967,
author = {Arturo Hernandez-Aguirre and Bill P. Buckles and Carlos A. Coello Coello},
title = {ON LEARNING kDNF^s_n BOOLEAN FORMULAS},
journal ={Evolvable Hardware, NASA/DoD Conference on},
volume = {0},
year = {2001},
isbn = {0-7695-1180-5},
pages = {0240},
doi = {http://doi.ieeecomputersociety.org/10.1109/EH.2001.937967},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Evolvable Hardware, NASA/DoD Conference on
TI - ON LEARNING kDNF^s_n BOOLEAN FORMULAS
SN - 0-7695-1180-5
SP
EP
A1 - Arturo Hernandez-Aguirre,
A1 - Bill P. Buckles,
A1 - Carlos A. Coello Coello,
PY - 2001
VL - 0
JA - Evolvable Hardware, NASA/DoD Conference on
ER -
 
Arturo Hernandez-Aguirre, Tulane University
Bill P. Buckles, Tulane University
Carlos A. Coello Coello, CINVESTAV-IPN
Abstract: The number of samples needed to learn an instance of the representation class kDNF^s_n of Boolean formulas is predicted using some tolerance parameters by the PAC framework. When the learning machine is a simple genetic algorithm, the initial population is an issue. Using PAC-learning we derive the population size that has at least one individual at a given Hamming distance from the optimum. Then we show that the GA evolves solutions from initial populations rather far (Hamming distance) from the optimum.
Citation:
Arturo Hernandez-Aguirre, Bill P. Buckles, Carlos A. Coello Coello, "ON LEARNING kDNF^s_n BOOLEAN FORMULAS," eh, pp.0240, The Third NASA/DoD Workshop on Evolvable Hardware, 2001
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