To Accomplish Amelioration Of Classifier Using Gene-Mutation Tactics In Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Bakshi:2012:ijetae,
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author = "Ankit Bakshi and Pallavi Pandit and Santosh Easo",
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title = "To Accomplish Amelioration Of Classifier Using
Gene-Mutation Tactics In Genetic Programming",
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journal = "International Journal of Emerging Technology and
Advanced Engineering",
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year = "2012",
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volume = "2",
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number = "12",
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pages = "319--322",
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month = dec,
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keywords = "genetic algorithms, genetic programming, elitism,
double tournament, plain crossover",
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ISSN = "2250--2459",
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annote = "The Pennsylvania State University CiteSeerX Archives",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.414.3468",
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rights = "Metadata may be used without restrictions as long as
the oai identifier remains attached to it.",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.3468",
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URL = "http://www.ijetae.com/Volume2Issue12.html",
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URL = "http://www.ijetae.com/files/Volume2Issue12/IJETAE_1212_59.pdf",
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size = "4 pages",
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abstract = "A phenomenon for designing classifier for three or
more classes (Multiclass) problem using genetic
programming (GP) is multiclass classifier. In this
scenario we purported three methods named Double
Tournament Method, Gene-Mutation Method and a Plain
Crossover method. In Double Tournament Method, we pick
out two idiosyncratic for the crossover operation on
the basis of size and fitness. In Gene-Mutation tactic
we are propagating two child from single parent and
selecting one of them on the basis of fitness and also
bring into play elitism on the child so that the
mutation operation does not degrade the fitness of the
distinct, whereas in Plain Crossover we select the two
child for the succeeding generation on the basis of
size, depth and fitness along with elitism on each step
from the six child which is generated during crossover.
To exhibit our approach we have designed a Multiclass
Classifier using GP by taking some standard datasets.
The results attained show that by applying Plain
crossover together with Gene-Mutation refined the
performance of the classifier.",
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notes = "Article 59.",
- }
Genetic Programming entries for
Ankit Bakshi
Pallavi Pandit
Santosh Easo
Citations