Knowledge discovery from data: InterCriteria Analysis of mutation rate influence

Roeva, Olympia; Zoteva, Dafina

doi:10.7546/nifs.2018.24.1.120-130

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…GA has been applied repeatedly for the mathematical modeling of cultivation processes [9,71,72]. Based on the thorough investigation of the parameters' influence [8], the parameters' values and GA functions have been set as follows: Since CSA has been applied for the first time for the parameter identification of the mathematical model of the considered cultivation process, the initial algorithm's parameters have been chosen based on values known in the literature (Table 1 In the hybrid algorithm, a small GA population was engaged in the beginning.…”

Section: Parameters' Algorithms Tuningmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

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Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

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Section: Parameters' Algorithms Tuningmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

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show abstract

“…The ICA approach has been applied for analyzing data and decision making in different areasmedical investigations [17,18,37,38,40,41], genetic algorithms [1,2,3,21,23,26,29], metaheuristic algorithms [10,11,12,13,14,15,16,19,22,27,28,30,31], neural networks [32,33,34,35], etc.…”

Section: Introductionmentioning

confidence: 99%