Genetic Algorithms Based Parameter Identification of Yeast Fed-Batch Cultivation

Angelova, Maria; Tzonkov, Stoyan; Pencheva, Tania

doi:10.1007/978-3-642-18466-6_26

Cited by 20 publications

(15 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Case study 2, parameter mutr is in a high correlation with J and model parameter Y S/X . The values of xovr and mutr reflect on T because of the more complex model used in Case study 1 [1], [12], [15]. In opposite, the more simple model structure in Case study 2 allows the relations between mutr and J and one of the most sensitive model parameter Y S/X [17] to be outlined.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…The selected values of xovr and mutr are chosen based on the following prerequisites: i) concerning the recommended by the literature values and trying to comprise different values in the ranges for both Case studies [12], [13], [15]; ii) concerning the previous authors' experience of modelling of FP using GA [1], [16], [17], [18], [19]. All other GA operators and parameters are tuned as presented in [1], [19].…”

Section: Simple Genetic Algorithms For Parameter Identificationmentioning

confidence: 99%

“…The choice of appropriate model parameter identification procedure is the most important problem for FP adequate modeling. Among others biologically inspired optimization techniques, genetic algorithms (GA) [12] has been proved as a global search method [10] for solving different engineering and optimization problems [18], among that for parameter identification of FP [1], [16], [17], [19]. GA efficiency strongly depends on the tuning of different operators, functions, and parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

InterCriteria Analysis of Crossover and Mutation Rates Relations in Simple Genetic Algorithm

Angelova

Roeva

Pencheva

2015

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

Abstract-In this investigation recently developed InterCriteria Analysis (ICA) is applied to examine the influences of two main genetic algorithms parameters -crossover and mutation rates during the model parameter identification of S. cerevisiae and E. coli fermentation processes. The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between investigated genetic algorithms parameters, from one hand, and fermentation process model parameters, from the other hand. The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about derived interactions are reported.

show abstract

Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Simple Genetic Algorithms For Parameter Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

InterCriteria Analysis of Crossover and Mutation Rates Relations in Simple Genetic Algorithm

Angelova

Roeva

Pencheva

2015

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…GAs have been successfully applied in a variety of areas to solve many engineering and optimization problems [6][7][8]. Properties such as noise tolerance and ease of interfacing and hybridization make GA a suitable method for the identification of parameters in fermentation models [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…For the purpose of this investigation, SGA and MpGA with standard sequence of genetic operators, namely selection, crossover, and mutation, are denoted, respectively, as SGA-SCM and MpGA-SCM. Many improved variations of the SGA and MpGA have been developed [9,13,15,16]. Among them are the modified genetic algorithm with a sequence crossover, mutation, and selection [9], here denoted as SGA-CMS, and consequent modification of MpGA based on such exchange, here denoted as MpGA-CMS.…”

Section: Introductionmentioning

confidence: 99%

Tuning Genetic Algorithm Parameters to Improve Convergence Time

Angelova

Pencheva

2011

International Journal of Chemical Engineering

Self Cite

View full text Add to dashboard Cite

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

show abstract

Metaheuristic Algorithms: Theory and Applications

Ribagin

Lyubenova

2021

Studies in Computational Intelligence

View full text Add to dashboard Cite

Genetic Algorithms Based Parameter Identification of Yeast Fed-Batch Cultivation

Cited by 20 publications

References 7 publications

InterCriteria Analysis of Crossover and Mutation Rates Relations in Simple Genetic Algorithm

InterCriteria Analysis of Crossover and Mutation Rates Relations in Simple Genetic Algorithm

Tuning Genetic Algorithm Parameters to Improve Convergence Time

Metaheuristic Algorithms: Theory and Applications

Contact Info

Product

Resources

About