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Artigo de periodico
Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems (2021)
Carvalho, Vinicius Renan de ; Özcan, Ender; Sichman, Jaime S.
Source: Applied Science. Unidade: EP
Subjects: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARVALHO, Vinicius Renan de e ÖZCAN, Ender e SICHMAN, Jaime S. Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems. Applied Science, v. 11, n. 19, p. 9153, 2021Tradução . . Disponível em: https://doi.org/10.3390/app11199153. Acesso em: 27 nov. 2025.
APA
Carvalho, V. R. de, Özcan, E., & Sichman, J. S. (2021). Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems. Applied Science, 11( 19), 9153. doi:10.3390/app11199153
NLM
Carvalho VR de, Özcan E, Sichman JS. Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems [Internet]. Applied Science. 2021 ; 11( 19): 9153.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3390/app11199153
Vancouver
Carvalho VR de, Özcan E, Sichman JS. Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems [Internet]. Applied Science. 2021 ; 11( 19): 9153.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3390/app11199153
Artigo de periodico
Artificial neural network based crossover for evolutionary algorithms (2020)
Tinós, Renato
Source: Applied Soft Computing. Unidade: FFCLRP
Subjects: ALGORITMOS, REDES NEURAIS, OPERADORES, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TINÓS, Renato. Artificial neural network based crossover for evolutionary algorithms. Applied Soft Computing, v. 95, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2020.106512. Acesso em: 27 nov. 2025.
APA
Tinós, R. (2020). Artificial neural network based crossover for evolutionary algorithms. Applied Soft Computing, 95. doi:10.1016/j.asoc.2020.106512
NLM
Tinós R. Artificial neural network based crossover for evolutionary algorithms [Internet]. Applied Soft Computing. 2020 ; 95[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.asoc.2020.106512
Vancouver
Tinós R. Artificial neural network based crossover for evolutionary algorithms [Internet]. Applied Soft Computing. 2020 ; 95[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.asoc.2020.106512
Artigo de periodico
Multi-objective evolutionary algorithm in tables for placement of SCADA and PMU considering the concept of Pareto Frontier (2019)
Vigliassi, Marcos Paulo; Massignan, Júlio A. D; Delbem, Alexandre Cláudio Botazzo ; London Junior, João Bosco Augusto
Source: International Journal of Electrical Power & Energy Systems. Unidades: ICMC, EESC
Subjects: SISTEMAS ELÉTRICOS DE POTÊNCIA, OTIMIZAÇÃO COMBINATÓRIA, ALGORITMOS, OTIMIZAÇÃO COMBINATÓRIA, OBSERVABILIDADE, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VIGLIASSI, Marcos Paulo et al. Multi-objective evolutionary algorithm in tables for placement of SCADA and PMU considering the concept of Pareto Frontier. International Journal of Electrical Power & Energy Systems, v. 106, p. 373-382, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.ijepes.2018.10.026. Acesso em: 27 nov. 2025.
APA
Vigliassi, M. P., Massignan, J. A. D., Delbem, A. C. B., & London Junior, J. B. A. (2019). Multi-objective evolutionary algorithm in tables for placement of SCADA and PMU considering the concept of Pareto Frontier. International Journal of Electrical Power & Energy Systems, 106, 373-382. doi:10.1016/j.ijepes.2018.10.026
NLM
Vigliassi MP, Massignan JAD, Delbem ACB, London Junior JBA. Multi-objective evolutionary algorithm in tables for placement of SCADA and PMU considering the concept of Pareto Frontier [Internet]. International Journal of Electrical Power & Energy Systems. 2019 ; 106 373-382.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.ijepes.2018.10.026
Vancouver
Vigliassi MP, Massignan JAD, Delbem ACB, London Junior JBA. Multi-objective evolutionary algorithm in tables for placement of SCADA and PMU considering the concept of Pareto Frontier [Internet]. International Journal of Electrical Power & Energy Systems. 2019 ; 106 373-382.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.ijepes.2018.10.026
Artigo de periodico
Improving k-means through distributed scalable metaheuristics (2017)
Oliveira, G. V; Coutinho, F. P; Campello, Ricardo José Gabrielli Barreto; Naldi, M. C
Source: Neurocomputing. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS, MINERAÇÃO DE DADOS, HEURÍSTICA, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, G. V et al. Improving k-means through distributed scalable metaheuristics. Neurocomputing, v. 246, p. 45-57, 2017Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2016.07.074. Acesso em: 27 nov. 2025.
APA
Oliveira, G. V., Coutinho, F. P., Campello, R. J. G. B., & Naldi, M. C. (2017). Improving k-means through distributed scalable metaheuristics. Neurocomputing, 246, 45-57. doi:10.1016/j.neucom.2016.07.074
NLM
Oliveira GV, Coutinho FP, Campello RJGB, Naldi MC. Improving k-means through distributed scalable metaheuristics [Internet]. Neurocomputing. 2017 ; 246 45-57.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.neucom.2016.07.074
Vancouver
Oliveira GV, Coutinho FP, Campello RJGB, Naldi MC. Improving k-means through distributed scalable metaheuristics [Internet]. Neurocomputing. 2017 ; 246 45-57.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.neucom.2016.07.074
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

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