| Resumen |
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 non-binary representation of individuals, elitism selection, uniform crossover, and high mutation rate. The obtained parameters setting allow the plagiarism detection model to achieve better results than the state-of-the-art approaches. |
