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Artigo de periodico
Inducing hierarchical multi-label classification rules with genetic algorithms (2019)
Cerri, Ricardo; Basgalupp, Márcio P; Barros, Rodrigo C; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Applied Soft Computing. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS GENÉTICOS, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CERRI, Ricardo et al. Inducing hierarchical multi-label classification rules with genetic algorithms. Applied Soft Computing, v. 77, p. 584-604, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2019.01.017. Acesso em: 27 nov. 2025.
APA
Cerri, R., Basgalupp, M. P., Barros, R. C., & Carvalho, A. C. P. de L. F. de. (2019). Inducing hierarchical multi-label classification rules with genetic algorithms. Applied Soft Computing, 77, 584-604. doi:10.1016/j.asoc.2019.01.017
NLM
Cerri R, Basgalupp MP, Barros RC, Carvalho ACP de LF de. Inducing hierarchical multi-label classification rules with genetic algorithms [Internet]. Applied Soft Computing. 2019 ; 77 584-604.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.asoc.2019.01.017
Vancouver
Cerri R, Basgalupp MP, Barros RC, Carvalho ACP de LF de. Inducing hierarchical multi-label classification rules with genetic algorithms [Internet]. Applied Soft Computing. 2019 ; 77 584-604.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.asoc.2019.01.017
Artigo de periodico
Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification (2015)
Barros, Rodrigo C; Basgalupp, Márcio P; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Genetic Programming and Evolvable Machines. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, COMPUTAÇÃO EVOLUTIVA, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BARROS, Rodrigo C e BASGALUPP, Márcio P e CARVALHO, André Carlos Ponce de Leon Ferreira de. Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification. Genetic Programming and Evolvable Machines, v. 16, n. 3, p. Se 2015, 2015Tradução . . Disponível em: https://doi.org/10.1007/s10710-014-9235-z. Acesso em: 27 nov. 2025.
APA
Barros, R. C., Basgalupp, M. P., & Carvalho, A. C. P. de L. F. de. (2015). Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification. Genetic Programming and Evolvable Machines, 16( 3), Se 2015. doi:10.1007/s10710-014-9235-z
NLM
Barros RC, Basgalupp MP, Carvalho ACP de LF de. Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification [Internet]. Genetic Programming and Evolvable Machines. 2015 ; 16( 3): Se 2015.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/s10710-014-9235-z
Vancouver
Barros RC, Basgalupp MP, Carvalho ACP de LF de. Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification [Internet]. Genetic Programming and Evolvable Machines. 2015 ; 16( 3): Se 2015.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/s10710-014-9235-z
Artigo de periodico
Convergence detection for optimization algorithms: approximate-KKT stopping criterion when Lagrange multipliers are not available (2015)
Haeser, Gabriel ; Melo, Vinicius Veloso de
Source: Operations Research Letters. Unidade: IME
Subjects: ALGORITMOS, ALGORITMOS GENÉTICOS, OTIMIZAÇÃO MATEMÁTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HAESER, Gabriel e MELO, Vinicius Veloso de. Convergence detection for optimization algorithms: approximate-KKT stopping criterion when Lagrange multipliers are not available. Operations Research Letters, v. 43, n. 5, p. 484–488, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.orl.2015.06.009. Acesso em: 27 nov. 2025.
APA
Haeser, G., & Melo, V. V. de. (2015). Convergence detection for optimization algorithms: approximate-KKT stopping criterion when Lagrange multipliers are not available. Operations Research Letters, 43( 5), 484–488. doi:10.1016/j.orl.2015.06.009
NLM
Haeser G, Melo VV de. Convergence detection for optimization algorithms: approximate-KKT stopping criterion when Lagrange multipliers are not available [Internet]. Operations Research Letters. 2015 ; 43( 5): 484–488.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.orl.2015.06.009
Vancouver
Haeser G, Melo VV de. Convergence detection for optimization algorithms: approximate-KKT stopping criterion when Lagrange multipliers are not available [Internet]. Operations Research Letters. 2015 ; 43( 5): 484–488.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.orl.2015.06.009
Artigo de periodico
Automatic design of decision-tree algorithms with evolutionary algorithms (2013)
Barros, Rodrigo C; Basgalupp, Márcio P; Carvalho, André Carlos Ponce de Leon Ferreira de ; Freitas, Alex A
Source: Evolutionary Computation. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS GENÉTICOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BARROS, Rodrigo C et al. Automatic design of decision-tree algorithms with evolutionary algorithms. Evolutionary Computation, v. 21, n. 4, p. 659-684, 2013Tradução . . Acesso em: 27 nov. 2025.
APA
Barros, R. C., Basgalupp, M. P., Carvalho, A. C. P. de L. F. de, & Freitas, A. A. (2013). Automatic design of decision-tree algorithms with evolutionary algorithms. Evolutionary Computation, 21( 4), 659-684.
NLM
Barros RC, Basgalupp MP, Carvalho ACP de LF de, Freitas AA. Automatic design of decision-tree algorithms with evolutionary algorithms. Evolutionary Computation. 2013 ; 21( 4): 659-684.[citado 2025 nov. 27 ]
Vancouver
Barros RC, Basgalupp MP, Carvalho ACP de LF de, Freitas AA. Automatic design of decision-tree algorithms with evolutionary algorithms. Evolutionary Computation. 2013 ; 21( 4): 659-684.[citado 2025 nov. 27 ]

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