Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three individual data-driven models, including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR), were developed for callogenesis rate (CR), embryogenesis rate (ER), and somatic embryo number (SEN). Consequently, the best obtained results were used in the fusion process by a bagging method. For medium reformulation, effects of eight ionic macronutrients on CR, ER, and SEN and effects of four vitamins on SEN were evaluated using data fusion (DF)–non-dominated sorting genetic algorithm-II (NSGA-II) and DF-genetic algorithm (GA), respectively. Results showed that DF models with the highest R² had superb performance in comparison with all other individual models. According to DF-NSGAII, the highest ER and SEN can be obtained from the medium containing 14.27 mM NH₄⁺, 38.92 mM NO₃⁻, 22.79 mM K⁺, 5.08 mM Cl⁻, 3.34 mM Ca²⁺, 1.67 mM Mg²⁺, 2.17 mM SO₄²⁻, and 1.44 mM H₂PO₄⁻. Based on the DF-GA model, the maximum SEN can be obtained from a medium containing 0.61 μM thiamine, 5.93 μM nicotinic acid, 0.25 μM biotin, and 0.26 μM riboflavin. The efficiency of the established-optimized medium was experimentally compared to Murashige and Skoog medium (MS) for embryogenesis of five chrysanthemum cultivars, and results indicated the efficiency of optimized medium over MS medium.

Key points

• MLP, SVR, and ANFIS were fused by a bagging method to develop a data fusion model.

• NSGA-II and GA were linked to the data fusion model for establishing and optimizing a new embryogenesis medium.

• The new culture medium (HNT) had better efficiency than MS medium.

结合优化算法的数据驱动模型可能是预测和优化体外培养过程的有益方法。这项研究旨在建模和优化一种新的菊花胚发生培养基。针对call发生率（CR），胚发生率（ER）和回声率，开发了三个独立的数据驱动模型，包括多层感知器（MLP），自适应神经模糊推理系统（ANFIS）和支持向量回归（SVR）。体细胞胚数（SEN）。因此，通过套袋法将获得的最佳结果用于融合过程。对于培养基配方，使用数据融合（DF）–非支配排序遗传算法-II（NSGA-II）和DF-遗传算法评估了8种离子型常量营养素对CR，ER和SEN的影响以及4种维生素对SEN的影响（GA）。与所有其他单个模型相比，R ²具有出色的性能。据DF-NSGAII，最高ER和SEN可以从含有14.27毫摩尔NH介质来获得₄ ⁺，38.92毫NO ₃ ^-，22.79毫米的K ⁺，5.08毫氯^-，3.34毫米的Ca ²⁺，1.67毫镁^{2 +}，2.17毫SO ₄ ^2-，和1.44毫米高₂ PO ₄ ^-。基于DF-GA模型，可以从包含0.61μM硫胺素，5.93μM烟酸，0.25μM生物素和0.26μM核黄素的培养基中获得最大SEN。建立的优化培养基的效率与Murashige和Skoog培养基（MS）在五个菊花品种的胚发生中进行了实验比较，结果表明优化培养基的效率超过了MS培养基。

关键点

•通过装袋法将MLP，SVR和ANFIS融合在一起，以开发数据融合模型。

•将NSGA-II和GA与数据融合模型联系起来，以建立和优化新的胚胎发生培养基。

•新培养基（HNT）的效率优于MS培养基。