Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation

Saeid Jamshidi; Abbas Yadollahi; Mohammad Mehdi Arab; Mohammad Soltani; Maliheh Eftekhari; Hamed Sabzalipoor; Abdollatif Sheikhi; Jalal Shiri

doi:10.1186/s13007-019-0520-y

Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation

Plant Methods. 2019 Nov 18:15:136. doi: 10.1186/s13007-019-0520-y. eCollection 2019.

Authors

Saeid Jamshidi¹, Abbas Yadollahi¹, Mohammad Mehdi Arab^{1

2}, Mohammad Soltani³, Maliheh Eftekhari¹, Hamed Sabzalipoor⁴, Abdollatif Sheikhi⁵, Jalal Shiri⁶

Affiliations

¹ 1Department of Horticultural Science, Faculty of Agriculture, Tarbiat Modares University (TMU), Tehran, Iran.
² 2Department of Horticultural Sciences, College of Aburaihan, University of Tehran (UT), Tehran, Iran.
³ 3Department of Irrigation and Drainage Engineering, College of Aburaihan, University of Tehran, Tehran, Iran.
⁴ 4Department of Nanobiotechnology, Faculty of Biological Sciences, Tarbiat Modares University (TMU), Tehran, Iran.
⁵ 5Department of Horticultural Sciences, College of Agriculture, Vali-e-Asr University of Rafsanjan, Kerman, Iran.
⁶ 6Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.

Abstract

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.

Keywords: Gene expression programming; Genetic algorithm; Multiple linear regression; OHF; Pear rootstock; Pyrodwarf; Radial basis function neural network.