High Throughput Mathematical Modeling And Multi-Objective Evolutionary Algorithms For Plant Tissue Culture Media Formulation: Case Study Of Pear Rootstocks

Simplified prediction of the interactions of plant tissue culture media components is of critical importance to efficient development and optimization of new media. We applied two algorithms, gene expression programming (GEP) and M5' model tree, to predict the effects of media components on in vitro proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), vitrification (Vitri) and quality index (QI) in pear rootstocks (Pyrodwarf and OHF 69). In order to optimize the selected prediction models, as well as achieving a precise multi-optimization method, multi-objective evolutionary optimization algorithms using genetic algorithm (GA) and particle swarm optimization (PSO) techniques were compared to the mono-objective GA optimization technique. A Gamma test (GT) was used to find the most important determinant input for optimizing each output factor. GEP had a higher prediction accuracy than M5' model tree. GT results showed that BA (Gamma = 4.0178), Mesos (Gamma = 0.5482), Mesos (Gamma = 184.0100), Micros (Gamma = 136.6100) and Mesos (Gamma = 1.1146), for PR, SL, STN, Vitri and QI respectively, were the most important factors in culturing OHF 69, while for Pyrodwarf culture, BA (Gamma = 10.2920), Micros (Gamma = 0.7874), NH4NO3 (Gamma = 166.410), KNO3 (Gamma = 168.4400), and Mesos (Gamma = 1.4860) were the most important influences on PR, SL, STN, Vitri and QI respectively. The PSO optimized GEP models produced the best outputs for both rootstocks.