Rhizobacteria and plant symbiosis in heavy metal uptake and its implications for soil bioremediation

A methodology based on artificial neural networks (ANNs) and differential evolution (DE), specifically a neuro-evolutionary approach, was applied to model germination rates, dry biomass and root/stem length and proving the robustness of the experimental data. The errors associated with all four variables are small and the correlation coefficients higher than 0.98, which indicate that the selected models can efficiently predict the experimental data.