To Accomplish Amelioration Of Classifier Using Gene-Mutation Tactics In Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Bakshi:2012:ijetae,

• author = "Ankit Bakshi and Pallavi Pandit and Santosh Easo",
• title = "To Accomplish Amelioration Of Classifier Using Gene-Mutation Tactics In Genetic Programming",
• journal = "International Journal of Emerging Technology and Advanced Engineering",
• year = "2012",
• volume = "2",
• number = "12",
• pages = "319--322",
• month = dec,
• keywords = "genetic algorithms, genetic programming, elitism, double tournament, plain crossover",
• ISSN = "2250--2459",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.414.3468",
• rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.3468",
• URL = "http://www.ijetae.com/Volume2Issue12.html",
• URL = "http://www.ijetae.com/files/Volume2Issue12/IJETAE_1212_59.pdf",
• size = "4 pages",
• 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.",
• notes = "Article 59.",

}

Genetic Programming entries for Ankit Bakshi Pallavi Pandit Santosh Easo

Citations