Special Issue on The Mexican Conference on Pattern Recognition; Guest Editors: José Fco. Martínez-Trinidad (National Institute of Astrophysics, Optics and Electronics, Mexico), Jesús Ariel Carrasco-Ochoa (National Institute of Astrophysics, Optics and Electronics, Mexico), Víctor Ayala-Ramírez (University of Guanajuato, Mexico) and José Arturo Olvera-López (Autonomous University of Puebla (BUAP), Mexico)No Access

Plagiarism Detection with Genetic-Based Parameter Tuning

Miguel A. Sanchez-Perez

Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Mexico City, Mexico

E-mail Address: [email protected]

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Alexander Gelbukh

Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Mexico City, Mexico

www.gelbukh.com

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Grigori Sidorov

Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Mexico City, Mexico

E-mail Address: [email protected]

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, and

Helena Gómez-Adorno

Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Mexico City, Mexico

E-mail Address: [email protected]

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https://doi.org/10.1142/S0218001418600066Cited by:2 (Source: Crossref)

Abstract

A crucial step in plagiarism detection is text alignment. This task consists in finding similar text fragments between two given documents. We introduce an optimization methodology based on genetic algorithms to improve the performance of a plagiarism detection model by optimizing its input parameters. The implementation of the genetic algorithm is based on nonbinary representation of individuals, elitism selection, uniform crossover, and high mutation rate. The obtained parameter settings allow the plagiarism detection model to achieve better results than the state-of-the-art approaches.

Keywords:

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