作者: Mohsen Hesami , Roohangiz Naderi , Masoud Tohidfar
DOI: 10.1038/S41598-019-54257-0
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摘要: The aim of the current study was modeling and optimizing medium compositions for shoot proliferation chrysanthemum, as a case study, through radial basis function- non-dominated sorting genetic algorithm-II (RBF-NSGAII). RBF one artificial neural networks (ANNs) used four outputs including rate (PR), number (SN), length (SL), basal callus weight (BCW) based on variables 6-benzylaminopurine (BAP), indole-3-butyric acid (IBA), phloroglucinol (PG), sucrose. Afterward, models were linked to optimization algorithm. Also, sensitivity analysis applied evaluating importance each input. R2 correlation values 0.88, 0.91, 0.97, 0.76 between observed predicted data obtained PR, SN, SL, BCW, respectively. According RBF-NSGAII, optimal PR (98.85%), SN (13.32), SL (4.83 cm), BCW (0.08 g) can be from containing 2.16 µM BAP, 0.14 µM IBA, 0.29 mM PG, 87.63 mM results indicated that more sensitive followed by sucrose, IBA. Finally, performance optimized tested, showed difference validation RBF-NSGAII negligible. Generally, considered an efficient computational strategy in vitro organogenesis.