Density propagation based adaptive multi-density clustering algorithm

This research was supported by the Science & Technology Development Foundation of Jilin Province (Grants Nos. 20160101259JC, 20180201045GX), the National Natural Science Foundation of China (Grants No. 61772227) and the Natural Science Foundation of Xinjiang Province (Grants No. 2015211C127). This resarch is also supported by the Engineering and Physical Sciences Research Council (EPSRC) funded project on New Industrial Systems: Manufacturing Immortality (EP/R020957/1).Peer reviewedPublisher PD

Modeling and Optimizing in vitro Sterilization of Chrysanthemum via Multilayer Perceptron-Non-dominated Sorting Genetic Algorithm-II (MLP-NSGAII)

In vitro sterilization is a primary step of plant tissue culture which the ultimate results of in vitro culture are directly depended on the efficiency of the sterilization. Artificial intelligence models in a combination of optimization algorithms could be beneficial computational approaches for modeling and optimizing in vitro culture. The aim of this study was modeling and optimizing in vitro sterilization of chrysanthemum, as a case study, through Multilayer Perceptron- Non-dominated Sorting Genetic Algorithm-II (MLP-NSGAII). MLP was used for modeling two outputs including contamination frequency (CF), and explant viability (EV) based on seven variables including HgCl2, Ca(ClO)2, Nano-silver, H2O2, NaOCl, AgNO3, and immersion times. Subsequently, models were linked to NSGAII for optimizing the process, and the importance of each input was evaluated by sensitivity analysis. Results showed all of the R2 of training and testing data were over 94%. According to MLP-NSGAII, optimal CF (0%), and EV (99.98%) can be obtained from 1.62% NaOCl at 13.96 min immersion time. The results of sensitivity analysis showed that CF and EV were more sensitive to immersion time and less sensitive to AgNO3. Subsequently, the performance of predicted and optimized sterilants × immersion times combination were tested, and results indicated that the differences between the MLP predicted and validation data were negligible. Generally, MLP-NSGAII as a powerful methodology may pave the way for establishing new computational strategies in plant tissue culture