Quality Assessment of Multi-population Genetic Algorithms Performance
نویسندگان
چکیده
The quality of performance of multi-population genetic algorithms (MpGA) has been assessed for the purposes of parameter identification of S. cerevisiae fed-batch cultivation. Intuitionistic fuzzy logic has been implemented aiming to derive intuitionistic fuzzy estimations of obtained model parameters. Three kinds of MpGA, differ from each other in the sequence of execution of main genetic operators, namely selection, crossover and mutation, have been assessed before and after the application of the recently developed procedure for purposeful model parameters genesis. Results obtained after the implementation of intuitionistic fuzzy logic for MpGA performances assessment have been compared and MpGA with a sequence selection and crossover after the procedure for purposeful model parameters genesis application has been distinguished as the fastest and quite reliable one.
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