Generation gap influence on Genetic algorithms performance

View other case studies

Case study 1: Parameter identification of an model of E. coli fed-batch cultivation process
Data contain results from parameter identification procedures using Genetic algorithm (GA) with 51 different values of parameter generation gap (ggap), i.e. 51 ‘objects’ in terms of ICA  (O1, …, O51):
GA outcomes – objective function value and computation time (C1 and C2),
current value of ggap (C3), and
model parameters estimates (C4–C6),
where Ci are ‘criteria’ in terms of ICA.

Comments are closed.