Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model
Eight single (SGA) and eight multi-population (MGA) genetic algorithms (GA) differing in the sequence of implementation of the main genetic operators’ selection, crossover and mutation, or omitting the mutation operator, have been examined for the purposes of parameter identification of a Saccharomy...
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| Format: | Article |
| Language: | English |
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Bulgarian Academy of Sciences
2024-12-01
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| Series: | International Journal Bioautomation |
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| Online Access: | http://www.biomed.bas.bg/bioautomation/2024/vol_28.4/files/28.4_05.pdf |
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| author | Maria Angelova Tania Pencheva |
| author_facet | Maria Angelova Tania Pencheva |
| author_sort | Maria Angelova |
| collection | DOAJ |
| description | Eight single (SGA) and eight multi-population (MGA) genetic algorithms (GA) differing in the sequence of implementation of the main genetic operators’ selection, crossover and mutation, or omitting the mutation operator, have been examined for the purposes of parameter identification of a Saccharomyces cerevisiae fed-batch fermentation process model. The influence of some of the main genetic algorithm parameters, namely number of individuals, maximum number of generations, generation gap, crossover and mutation rates for both SGA and MGA, and insertion and migration probability for MGA only, have been investigated in depth. Almost all applied SGA and MGA led to similar values of the optimization criterion but the SGA with operators’ sequence mutation, crossover and selection, and MGA with operators’ sequence crossover, selection and mutation, are significantly faster than others while keeping the model accuracy. Among the considered GA parameters, generation gap influences most significantly to SGA and MGA convergence time, saving of about 40% of computational time of the algorithms without affecting the model accuracy. |
| format | Article |
| id | doaj-art-a1ff46b035c743fa857e2d6d94c33be0 |
| institution | Kabale University |
| issn | 1314-1902 1314-2321 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Bulgarian Academy of Sciences |
| record_format | Article |
| series | International Journal Bioautomation |
| spelling | doaj-art-a1ff46b035c743fa857e2d6d94c33be02025-01-09T10:27:31ZengBulgarian Academy of SciencesInternational Journal Bioautomation1314-19021314-23212024-12-0128423324410.7546/ijba.2024.28.4.001038Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process ModelMaria Angelova0Tania PenchevaInstitute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, BulgariaEight single (SGA) and eight multi-population (MGA) genetic algorithms (GA) differing in the sequence of implementation of the main genetic operators’ selection, crossover and mutation, or omitting the mutation operator, have been examined for the purposes of parameter identification of a Saccharomyces cerevisiae fed-batch fermentation process model. The influence of some of the main genetic algorithm parameters, namely number of individuals, maximum number of generations, generation gap, crossover and mutation rates for both SGA and MGA, and insertion and migration probability for MGA only, have been investigated in depth. Almost all applied SGA and MGA led to similar values of the optimization criterion but the SGA with operators’ sequence mutation, crossover and selection, and MGA with operators’ sequence crossover, selection and mutation, are significantly faster than others while keeping the model accuracy. Among the considered GA parameters, generation gap influences most significantly to SGA and MGA convergence time, saving of about 40% of computational time of the algorithms without affecting the model accuracy.http://www.biomed.bas.bg/bioautomation/2024/vol_28.4/files/28.4_05.pdfsingle genetic algorithmsmulti-population genetic algorithmsparameter identificationfed-batch fermentation process modelsaccharomyces cerevisiae |
| spellingShingle | Maria Angelova Tania Pencheva Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model International Journal Bioautomation single genetic algorithms multi-population genetic algorithms parameter identification fed-batch fermentation process model saccharomyces cerevisiae |
| title | Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model |
| title_full | Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model |
| title_fullStr | Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model |
| title_full_unstemmed | Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model |
| title_short | Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model |
| title_sort | influence of genetic algorithm parameters on their performance for parameter identification of a yeast fed batch fermentation process model |
| topic | single genetic algorithms multi-population genetic algorithms parameter identification fed-batch fermentation process model saccharomyces cerevisiae |
| url | http://www.biomed.bas.bg/bioautomation/2024/vol_28.4/files/28.4_05.pdf |
| work_keys_str_mv | AT mariaangelova influenceofgeneticalgorithmparametersontheirperformanceforparameteridentificationofayeastfedbatchfermentationprocessmodel AT taniapencheva influenceofgeneticalgorithmparametersontheirperformanceforparameteridentificationofayeastfedbatchfermentationprocessmodel |