Multi-objective phylogenetic algorithm: solving multi-objective decomposable deceptive problems (2011)
- Autores:
- Autor USP: DELBEM, ALEXANDRE CLÁUDIO BOTAZZO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-642-19893-9_20
- Assunto: ALGORITMOS GENÉTICOS
- Idioma: Inglês
- Imprenta:
- Editora: Springer
- Local: Heidelberg
- Data de publicação: 2011
- Fonte:
- Título do periódico: Lecture Notes in Computer Sciences
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 6576, p. 285-297, 2011
- Nome do evento: International Conference Evolutionary Multi-Criterion Optimization - EMO 2011
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
MARTINS, Jean Paulo et al. Multi-objective phylogenetic algorithm: solving multi-objective decomposable deceptive problems. Lecture Notes in Computer Sciences. Heidelberg: Springer. Disponível em: https://doi.org/10.1007/978-3-642-19893-9_20. Acesso em: 18 set. 2024. , 2011 -
APA
Martins, J. P., Soares, A. H. M., Vargas, D. V., & Delbem, A. C. B. (2011). Multi-objective phylogenetic algorithm: solving multi-objective decomposable deceptive problems. Lecture Notes in Computer Sciences. Heidelberg: Springer. doi:10.1007/978-3-642-19893-9_20 -
NLM
Martins JP, Soares AHM, Vargas DV, Delbem ACB. Multi-objective phylogenetic algorithm: solving multi-objective decomposable deceptive problems [Internet]. Lecture Notes in Computer Sciences. 2011 ; 6576 285-297.[citado 2024 set. 18 ] Available from: https://doi.org/10.1007/978-3-642-19893-9_20 -
Vancouver
Martins JP, Soares AHM, Vargas DV, Delbem ACB. Multi-objective phylogenetic algorithm: solving multi-objective decomposable deceptive problems [Internet]. Lecture Notes in Computer Sciences. 2011 ; 6576 285-297.[citado 2024 set. 18 ] Available from: https://doi.org/10.1007/978-3-642-19893-9_20 - Multi-criterion phylogenetic inference using evolutionary algorithms
- A feasibility cachaca type recognition using computer vision and pattern recognition
- On the effectiveness of genetic algorithms for the multidimensional knapsack problem
- Multimodality and the linkage-learning difficulty of additively separable functions
- On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem
- Evolvable hardware approach using evolutionary algorithms and FPGAs
- Easy efficiency-enhancement technique for the ECGA
- Evolutionary algorithm and HP model for protein structure prediction
- Neuroevolution for solving multiobjective knapsack problems
- Representations for evolutionary algorithms applied to protein structure prediction problem using HP model
Informações sobre o DOI: 10.1007/978-3-642-19893-9_20 (Fonte: oaDOI API)
Como citar
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
