Abstract
In this paper, a variation of a genetic algorithm for optimization problems is presented, focusing on the adjustment of the mutation rate parameter by fuzzifying the diversity of the population and the value of the individual’s adaptation. Here, it is important to remember that this parameter directly interferes with the convergence and quality of the solution found by the genetic algorithm. To evaluate the performance of the proposed solution, experiments were conducted on the OneMax problem, analyzing aspects such as: convergence, quality of the solution, the diversity of the population, and the number of individuals evaluated. Obtained results and their impacts are presented in this paper.
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Ferro, J.V.R., Brito, J.R.d.S., Lopes, R.V.V., Costa, E.d.B. (2022). Mutation Rate Analysis Using a Self-Adaptive Genetic Algorithm on the OneMax Problem. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13653. Springer, Cham. https://doi.org/10.1007/978-3-031-21686-2_14
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DOI: https://doi.org/10.1007/978-3-031-21686-2_14
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