2020
DOI: 10.1115/1.4045815
|View full text |Cite
|
Sign up to set email alerts
|

Differential Evolution for the Optimization of DMSO-Free Cryoprotectants: Influence of Control Parameters

Abstract: This study presents the influence of control parameters including population (NP) size, mutation factor (F), crossover (Cr), and four types of differential evolution (DE) algorithms including random, best, local-to-best, and local-to-best with self-adaptive (SA) modification for the purpose of optimizing the compositions of dimethylsufloxide (DMSO)-free cryoprotectants. Post-thaw recovery of Jurkat cells cryopreserved with two DMSO-free cryoprotectants at a cooling rate of 1 °C/min displayed a nonlinear, four-… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
8
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 6 publications
(8 citation statements)
references
References 22 publications
0
8
0
Order By: Relevance
“…Emergent population staying constant for two generations was defined as the criterion of convergence for the DE algorithm. This criterion has been validated in our recent study (Pi et al, 2019a) to ensure ≥95% accuracy of predicting the global optimum.…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…Emergent population staying constant for two generations was defined as the criterion of convergence for the DE algorithm. This criterion has been validated in our recent study (Pi et al, 2019a) to ensure ≥95% accuracy of predicting the global optimum.…”
Section: Discussionmentioning
confidence: 99%
“…Compared to our previous DE algorithm studies (Pollock et al, 2017;Pi et al, 2019a), this study optimized four variable components instead of the former three variables, which resulted in six times as many possible vectors in the parameter space than before. However, the population size of each generation (i.e., 10) was smaller than what had been commonly used before (i.e., 13-27).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…al. 19 for strategy 1. The algorithm was completed, and convergence was achieved when post-thaw motility between multiple generations began to plateau.…”
Section: Algorithm Designmentioning
confidence: 99%
“…Recently machine learning was used to optimize a cell culture medium for T cells 16 and to design an integral membrane channel rhodopsin for efficient eukaryotic expression and plasma membrane localization 17 . Similarly, a differential evolution algorithm was used to optimize cryopreservation conditions for Jurkat cells and mesenchymal stem cells 18 , 19 . In these experimental designs protocols or medium are optimized via information obtained through physical experiments guided by the algorithm.…”
Section: Introductionmentioning
confidence: 99%