Background Predicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices. Results Generally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability. Conclusions GEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.

中文翻译：

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结合基因表达编程和遗传算法作为梨砧木组织培养基配方的强大混合建模方法。

背景 预测植物组织培养基成分对外植体增殖的影响是重要的，尤其是在商业规模中以优化有效培养基。以前的研究集中在通过传统的多层感知器神经网络 (MLPNN) 和多元线性回归 (MLR) 方法预测媒体成分对外植体生长的影响。因此，有机会找到更有效的算法，例如径向基函数神经网络 (RBFNN) 和基因表达编程 (GEP)。在这里，首次将一种新的算法，即GEP，以前没有应用于植物组织培养研究中，与RBFNN 和MLR 进行了比较。梨砧木（Pyrodwarf 和 OHF）被用作预测培养基中矿物质和某些激素对增殖指数影响的案例研究。结果 一般来说，RBFNN 和 GEP 表现出比 MLR 更高的性能精度。此外，使用遗传算法 (GA) 优化了作为最准确模型的 GEP 模型。改进主要是由于 RBFNN 和 GEP 强大的估计能力以及它们对实验噪声或不可能性的优越容忍度。结论 GEP 作为实现最高增殖质量和数量的最稳健、最准确的勘探程序，还具有易于使用的优点。