Source: Applied Soft Computing. Unidade: ICMC
Subjects: JOGOS DE COMPUTADOR, OTIMIZAÇÃO RESTRITA, ALGORITMOS GENÉTICOS
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VIANA, Breno Mauricio de Freitas et al. Feasible-infeasible two-population genetic algorithm to evolve dungeon levels with dependencies in barrier mechanics. Applied Soft Computing, v. 119, p. 1-16, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2022.108586. Acesso em: 27 ago. 2024.APA
Viana, B. M. de F., Pereira, L. T., Toledo, C. F. M., Santos, S. R. dos, & Maia, S. M. D. M. (2022). Feasible-infeasible two-population genetic algorithm to evolve dungeon levels with dependencies in barrier mechanics. Applied Soft Computing, 119, 1-16. doi:10.1016/j.asoc.2022.108586NLM
Viana BM de F, Pereira LT, Toledo CFM, Santos SR dos, Maia SMDM. Feasible-infeasible two-population genetic algorithm to evolve dungeon levels with dependencies in barrier mechanics [Internet]. Applied Soft Computing. 2022 ; 119 1-16.[citado 2024 ago. 27 ] Available from: https://doi.org/10.1016/j.asoc.2022.108586Vancouver
Viana BM de F, Pereira LT, Toledo CFM, Santos SR dos, Maia SMDM. Feasible-infeasible two-population genetic algorithm to evolve dungeon levels with dependencies in barrier mechanics [Internet]. Applied Soft Computing. 2022 ; 119 1-16.[citado 2024 ago. 27 ] Available from: https://doi.org/10.1016/j.asoc.2022.108586