Optimization for large scale process based on evolutionary algorithms: Genetic algorithms

The role of cations in regulating reaction pathways driven by Bacillus circulans β-galactosidase

“…The genetic algorithm starts with an initial population of individual data, which is generated randomly in many directions at the same time so that the optimization can proceed in any direction to reach a global minimum point. This approach avoids the results reflecting only a local optimum, making it an appropriate tool for non-linear optimization problems [23][24][25]. MATLAB R2017b was used to prepare the codes and run the GA. After the GA initialization, the set of differential equations was solved by a variable order method (ode15s in MATLAB) and the objective function was then evaluated.…”

A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil

“…T t nt=T 0 y i (nt) (8) where y i (nt) is the experimental data of composition i at the time of nt,ŷ i (nt) is the predictive value of composition i at the time of nt. T 0 and T t are starting time and stopping time of experiments.…”

“…Genetic algorithms do not tackle problems directly but in a chromosome space, i.e., candidate solutions of an optimization problem are converted to chromosome firstly. Due to no requirement of prior information about search space and owning excellent global search ability, GAs have been applied widely to address complicated real-world problems with non-differentiable, non-convex and non-linear [5][6][7][8][9][10].…”

Fluid catalytic cracking optimisation using factorial design and genetic algorithm techniques

“…GA initiates with a population of represented random solutions in some series of structures. After this first stage, a series of operators is applied repeatedly up to convergence is achieved (Victorino et al, 2007). The optimisation procedure based on such approach can be considered as a global optimisation method that does not depend upon the initial values to achieve the convergence.…”