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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References
Bai, Y., Wang, D.: Fundamentals of fuzzy logic control - fuzzy sets, fuzzy rules and defuzzifications. In: Bai, Y., Zhuang, H., Wang, D. (eds.) Advanced Fuzzy Logic Technologies in Industrial Applications. Advances in Industrial Control. Springer, London (2006). https://doi.org/10.1007/978-1-84628-469-4_2
Barcellos, J.C.H.: Algoritmos genéticos adaptativos: um estudo comparativo. Dissertação (Mestrado em Engenharia) - Escola Politécnica da Universidade de São Paulo (2000)
Burdelis, M.A.P.: Ajuste de Taxas de Mutação e de Cruzamento de Algoritmos Genéticos Utilizando-se Inferências Nebulosas. 2009. Dissertação (Mestre em Engenharia) - Departamento de Engenharia de Computação e Sistemas Digitais (PCS), [S. l.] (2009)
Carvalho, W.L.O.: Estudo de parâmetros ótimos em algoritmos genéticos elitistas. Dissertação (Mestrado em Matemática Aplicada e Estatística) - Universidade Federal do Rio Grande do Norte, Natal (2017)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, New York, NY (2003)
Ferrari, A.C.K., Leandro, G.V., Olieveira, G.: Evolução Diferencial com Parâmetros Ajustáveis por Lógica Fuzzy. Anais do XX Congresso Brasileiro de Automática, [S. l.], pp. 796–803 (2014)
Gerla, G.: Fuzzy Logic: Mathematical Tools for Approximate Reasoning, Trends in Logic, Kluwer Ac. Press (2000). https://doi.org/10.1007/978-94-015-9660-2
Giguere, P., Goldberg, D.E.: Population sizing for optimum sampling with genetic algorithms: a case study of the onemax problem. Genetic Program. 98, 496–503 (1998)
Linden, R. Algoritmos genéticos. 3rd edn. Editora Ciência Moderna, Rio de Janeiro (2012)
Livingston, E.H.: Who was student and why do we care so much about his t-test?1. J. Surgical Res. 118(1), 58–65 (2004)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press (1999)
de Oliveira, N.J.R.R.: Avaliação de Taxas de Cruzamento e Mutação em um Algoritmo Genético Baseado em Ordem Aplicado ao Problema do Caixeiro Viajante. 2018. Monografia (Bacharel) - Curso de Bacharelado em Sistemas de Informação, [S. l.] (2018)
Pappa, G.L., Freitas, A.A.: Evolutionary algorithms. In: Automating the Design of Data Mining Algorithms. NCS. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-02541-9_3
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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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