scholarly journals RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models

2021 ◽  
Vol 14 (0) ◽  
pp. 30-35
Author(s):  
Kazuhiro Maeda ◽  
Fred C. Boogerd ◽  
Hiroyuki Kurata
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Kinetic Models ◽  
Real Coded Genetic Algorithm

scholarly journals Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Technique ◽  
Convergence Time ◽  
Generation Gap ◽  
Model Order ◽  
Real Coded Genetic Algorithm ◽  
The Mean ◽  
Conventional Genetic Algorithm

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

scholarly journals RCGAToolbox: A real-coded genetic algorithm software for parameter estimation of kinetic models

2021 ◽  
Author(s):  
Kazuhiro Maeda ◽  
Fred C. Boogerd ◽  
Hiroyuki Kurata
Keyword(s):  
Parameter Estimation ◽  
User Interfaces ◽  
Kinetic Models ◽  
Biochemical Networks ◽  
Generation Gap ◽  
Supplementary Information ◽  
Real Coded Genetic Algorithm ◽  
Supplementary Material ◽  
Real Coded Genetic Algorithms ◽  
User Friendly

AbstractSummaryKinetic modeling is essential in understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in the development of kinetic models. We present RCGAToolbox, software for real-coded genetic algorithms (RCGAs), which accelerates the parameter estimation of kinetic models. RCGAToolbox provides two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and real-coded ensemble crossover star with just generation gap (REXstar/JGG), using the stochastic ranking method. The RCGAToolbox also provides user-friendly graphical user interfaces.Availability and implementationRCGAToolbox is available from https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, with application examples. The user guide is provided in the Supplementary Material. RCGAToolbox runs on MATLAB in Windows, Linux, and [email protected] informationSupplementary Material is available at Bioinformatics online.

Parameter optimization and sensitivity analysis for large kinetic models using a real-coded genetic algorithm

Gene ◽  
2013 ◽  
Vol 518 (1) ◽  
pp. 84-90 ◽  
Author(s):  
Yukako Tohsato ◽  
Kunihiko Ikuta ◽  
Akitaka Shionoya ◽  
Yusaku Mazaki ◽  
Masahiro Ito
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Parameter Optimization ◽  
Kinetic Models ◽  
Real Coded Genetic Algorithm

A hybrid genetic algorithm for efficient parameter estimation of large kinetic models

2004 ◽  
Vol 28 (12) ◽  
pp. 2569-2581 ◽  
Author(s):  
Santhoji Katare ◽  
Aditya Bhan ◽  
James M. Caruthers ◽  
W. Nicholas Delgass ◽  
Venkat Venkatasubramanian
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Kinetic Models ◽  
Hybrid Genetic Algorithm

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

Sign in / Sign up

Export Citation Format

Share Document