GP4LR: Uma Ferramenta para Análise de Regressão Linear com Suporte de Programação Genética
Resumo
Programação Genética e Regressão Linear têm sido utilizadas conjuntamente em diferentes aplicações, tais como em projetos de software/hardware, previsão do tempo, experimentos com fármacos, entre outras. Essa combinação introduziu uma nova classe de problemas, sendo assim necessário explorá-la para estabelecer suas principais características e demandas. Este artigo apresenta uma nova ferramenta, que combina Programação Genética e Regressão Linear, com objetivo de contribuir com a pesquisa aplicada, por modelagem estatística e predição de dados. Resultados mostram que a ferramenta proposta oferece uma grande relevância para suporte a análises estatísticas aplicadas em diversas áreas de estudo.
Palavras-chave:
Regressão Linear, Programação Genética, Pesquisa Aplicada, Ferramenta Computacional
Referências
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Arnaldo, I., Krawiec, K., O'Reilly, U. M. (2014) “Multiple regression genetic programming”. In: Proceedings of the 2014 conference on Genetic and evolutionary computation. p. 879-886.
Babovic, V., Keijzer, M. (2002) “Rainfall runoff modelling based on genetic programming”. In: Hydrology Research, 33(5). p. 331-346.
Chan, K. Y., Kwong, C. K., Fogarty, T. C. (2010) “Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers”. In: Information Sciences, 180(4), p. 506-518.
Chatterjee, S., Hadi, A. S. (2012) “Regression analysis by example”. John Wiley & Sons.
Cheng, R., Gen, M., Tsujimura, Y. (1996) “A tutorial survey of job-shop scheduling problems using genetic algorithms”. I. Representation. Computers & industrial engineering, 30(4). p. 983-997.
Cook, R. D. (1977) “Detection of Influential Observation in Linear Regression”. In: Technometrics, 19(1), p. 15–18.
Cormen, T. H. (2009) “Introduction to algorithms”. [S.l.]: MIT press.
Dabhi, V. K., Vij, S. K. (2011) “Empirical modeling using symbolic regression via postfix genetic programming”. In: 2011 International Conference on Image Information Processing (ICIIP), p. 1-6.
Davidson, J., Savic, D. A., Walters, G. A. (2003) “Symbolic and numerical regression: experiments and applications”. In: Information Sciences, Elsevier, v. 150, n. 1, p. 95–117.
Dignum, S., Poli, R. (2008) “Operator equalisation and bloat free GP”. In: Genetic Programming, Springer Berlin Heidelberg. p. 110-121.
Dua, D., Graff, C. (2019) UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml.
Esmeraldo, G., Barros, E. (2010) “A genetic programming based approach for efficiently exploring architectural communication design space of mpsocs”. In: IEEE. Programmable Logic Conference (SPL), 2010 VI Southern. [S.l.]. p. 29–34.
Esmeraldo, G. A. R. M., Oliveira, C. C. F., Sales, M. M., Souza, F. A. (2020) “Uma Abordagem para Análise de Regressão com Suporte de Programação Genética”. In: Guttenberg S. S. Ferreira; Régia T. S. Araújo. (Org.). ENSAIOS DE MATEMÁTICA: pesquisas em ensino e ciências aplicadas, Vo. 1. CRV. p. 51-72.
Espejo, P. G., Ventura, S., Herrera, F. (2010) “A survey on the application of genetic programming to classification”. In: IEEE Transactions on Systems, Man, and Cybernetics, Part C, 40(2), p. 121-144.
Keijzer, M. “Improving Symbolic Regression with Interval Arithmetic and Linear Scaling. Genetic Programming”. Heidelberg: Springer, p. 70-78, 2003.
Koza, J. R. (1992) “Genetic Programming: On the Programming of Computers by Means of Natural Selection”, MIT Press.
Koza, J. R. (1995) “Survey of genetic algorithms and genetic programming”. In: Wescon conference record. WESTERN PERIODICALS COMPANY, p. 589-594.
Linden, R. (2012) “Algoritmos Genéticos. 3.ed.”. Ciência Moderna.
Schelling, X., Robertson, S. (2020) “A development framework for decision support systems in high-performance sport”. In: International Journal of Computer Science in Sport, v. 19, n. 1, p. 1-23.
Simon, D. (2013) “Evolutionary optimization algorithms”. John Wiley & Sons.
Paterlini, S., Minerva, T. (2010) “Regression Model Selection Using Genetic Algorithms”. In: Proceedings of the 11th WSEAS International Conference on Recent Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing.
Weisberg, S. (2005) “Applied linear regression”. Vol. 528. John Wiley & Sons.
Worzel, W. P.; Yu, J., Almal, A. A., Chinnaiyan, A. M. (2009) “Applications of genetic programming in cancer research”. In: The international journal of biochemistry & cell biology, 41(2), p. 405-413.
Arnaldo, I., Krawiec, K., O'Reilly, U. M. (2014) “Multiple regression genetic programming”. In: Proceedings of the 2014 conference on Genetic and evolutionary computation. p. 879-886.
Babovic, V., Keijzer, M. (2002) “Rainfall runoff modelling based on genetic programming”. In: Hydrology Research, 33(5). p. 331-346.
Chan, K. Y., Kwong, C. K., Fogarty, T. C. (2010) “Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers”. In: Information Sciences, 180(4), p. 506-518.
Chatterjee, S., Hadi, A. S. (2012) “Regression analysis by example”. John Wiley & Sons.
Cheng, R., Gen, M., Tsujimura, Y. (1996) “A tutorial survey of job-shop scheduling problems using genetic algorithms”. I. Representation. Computers & industrial engineering, 30(4). p. 983-997.
Cook, R. D. (1977) “Detection of Influential Observation in Linear Regression”. In: Technometrics, 19(1), p. 15–18.
Cormen, T. H. (2009) “Introduction to algorithms”. [S.l.]: MIT press.
Dabhi, V. K., Vij, S. K. (2011) “Empirical modeling using symbolic regression via postfix genetic programming”. In: 2011 International Conference on Image Information Processing (ICIIP), p. 1-6.
Davidson, J., Savic, D. A., Walters, G. A. (2003) “Symbolic and numerical regression: experiments and applications”. In: Information Sciences, Elsevier, v. 150, n. 1, p. 95–117.
Dignum, S., Poli, R. (2008) “Operator equalisation and bloat free GP”. In: Genetic Programming, Springer Berlin Heidelberg. p. 110-121.
Dua, D., Graff, C. (2019) UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml.
Esmeraldo, G., Barros, E. (2010) “A genetic programming based approach for efficiently exploring architectural communication design space of mpsocs”. In: IEEE. Programmable Logic Conference (SPL), 2010 VI Southern. [S.l.]. p. 29–34.
Esmeraldo, G. A. R. M., Oliveira, C. C. F., Sales, M. M., Souza, F. A. (2020) “Uma Abordagem para Análise de Regressão com Suporte de Programação Genética”. In: Guttenberg S. S. Ferreira; Régia T. S. Araújo. (Org.). ENSAIOS DE MATEMÁTICA: pesquisas em ensino e ciências aplicadas, Vo. 1. CRV. p. 51-72.
Espejo, P. G., Ventura, S., Herrera, F. (2010) “A survey on the application of genetic programming to classification”. In: IEEE Transactions on Systems, Man, and Cybernetics, Part C, 40(2), p. 121-144.
Keijzer, M. “Improving Symbolic Regression with Interval Arithmetic and Linear Scaling. Genetic Programming”. Heidelberg: Springer, p. 70-78, 2003.
Koza, J. R. (1992) “Genetic Programming: On the Programming of Computers by Means of Natural Selection”, MIT Press.
Koza, J. R. (1995) “Survey of genetic algorithms and genetic programming”. In: Wescon conference record. WESTERN PERIODICALS COMPANY, p. 589-594.
Linden, R. (2012) “Algoritmos Genéticos. 3.ed.”. Ciência Moderna.
Schelling, X., Robertson, S. (2020) “A development framework for decision support systems in high-performance sport”. In: International Journal of Computer Science in Sport, v. 19, n. 1, p. 1-23.
Simon, D. (2013) “Evolutionary optimization algorithms”. John Wiley & Sons.
Paterlini, S., Minerva, T. (2010) “Regression Model Selection Using Genetic Algorithms”. In: Proceedings of the 11th WSEAS International Conference on Recent Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing.
Weisberg, S. (2005) “Applied linear regression”. Vol. 528. John Wiley & Sons.
Worzel, W. P.; Yu, J., Almal, A. A., Chinnaiyan, A. M. (2009) “Applications of genetic programming in cancer research”. In: The international journal of biochemistry & cell biology, 41(2), p. 405-413.
Publicado
16/05/2022
Como Citar
MENDES, Cicero Samuel R.; ESMERALDO, Guilherme Álvaro R. M..
GP4LR: Uma Ferramenta para Análise de Regressão Linear com Suporte de Programação Genética. In: CONCURSO DE TRABALHOS DE CONCLUSÃO DE CURSO EM SISTEMAS DE INFORMAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 18. , 2022, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 166-176.
DOI: https://doi.org/10.5753/sbsi_estendido.2022.222039.
