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Joint set-up of parameters in genetic algorithms and the artificial bee colony algorithm: an approach for cultivation process modelling

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Abstract

In this paper, a Joint set-up procedure for tuning metaheuristic algorithms’ parameters is proposed. The approach is applied to a genetic algorithm (GA) and tested further on the artificial bee colony (ABC) algorithm. The joint influence of parameters (the crossover and mutation probabilities for GA and the number of population and limit for ABC) on the performance of the algorithms is investigated. As a case study, a model parameter identification of an E. coli fed-batch cultivation process is considered. E. coli is one of the most commonly used bacteria for producing medical substances in the pharmaceutical industry. The development of an effective model of a fed-batch cultivation process is very important. The processes in a bioreactor are usually described by a system of parametric nonlinear differential equations. The model parameter identification is a difficult optimization problem, which cannot be solved by applying traditional numerical methods. Feasibilities of GA and ABC for a model parameter identification of a nonlinear fed-batch cultivation process based on real experimental data are presented. The application of the proposed Joint set-up approach leads to a significant improvement in the performance of GA and ABC. As a result, a reasonable enhancement of the E. coli cultivation model accuracy is achieved. The main advantage of the tuning procedure, which searches an optimal set of values of GA and ABC control parameters, focusing on promising intervals of variation of the parameter values and refining their ranges, is that the computational efforts are reduced by more than 60% for the ABC algorithm and more than 90% for GA.

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References

  • Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018a) Metaheuristic algorithms: a comprehensive review. In: Sangaiah AK, Sheng M, Zhang Z (eds) Computational intelligence for multimedia big data on the cloud with engineering applications, intelligent data-centric systems. Academic Press, Cambridge, pp 185–231. https://doi.org/10.1016/B978-0-12-813314-9.00010-4

    Chapter  Google Scholar 

  • Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018) Metaheuristic algorithms: a comprehensive review. In: Computational intelligence for multimedia big data on the cloud with engineering applications. Academic Press, pp 185–231

  • Adamczyk PA, Reed JL (2017) Escherichia coli as a model organism for systems metabolic engineering. Curr Opin Syst Biol 6:80–88. https://doi.org/10.1016/j.coisb.2017.11.001

    Article  Google Scholar 

  • Akay B, Karaboga D (2009) Parameter tuning for the artificial bee colony algorithm. In: Nguyen NT, Kowalczyk R, Chen SM (eds) Computational collective intelligence. Semantic web, social networks and multiagent systems. ICCCI 2009. Lecture notes in computer science, vol 5796. https://doi.org/10.1007/978-3-642-04441-0_53

  • Akay B, Karaboga D (2015) A survey on the applications of artificial bee colony in signal, image, and video processing. SIViP 9:967–990. https://doi.org/10.1007/s11760-015-0758-4

    Article  Google Scholar 

  • Ali MZ, Awad NH, Suganthan PN, Shatnawi AM, Reynolds RG (2018) An improved class of real-coded genetic algorithms for numerical optimization. Neurocomputing 275:155–166

    Article  Google Scholar 

  • Alshamiri AK, Singh A, Surampudi BR (2016) Artificial bee colony algorithm for clustering: an extreme learning approach. Soft Comput 20:3163–3176. https://doi.org/10.1007/s00500-015-1686-5

    Article  Google Scholar 

  • Álvarez JD, Risco-Martín JL, Colmenar JM (2018) Evolutionary design of the memory subsystem. Appl Soft Comput 62:1088–1101

    Article  Google Scholar 

  • Angelova M, Pencheva T (2011) Tuning genetic algorithm parameters to improve convergence time. Int J Chem Eng 2011:7

    Article  Google Scholar 

  • Angelova M, Pencheva T (2013) Genetic operators significance assessment in simple genetic algorithm. In: International conference on large-scale scientific computing. Springer, pp 223–231. https://link.springer.com/chapter/10.1007/978-3-662-43880-0_24

  • Angelova M, Roeva O, Pencheva T (2019) Artificial bee colony algorithm for parameter identification of fermentation process model. In: Ntalianis K, Vachtsevanos G, Borne P, Croitoru A (eds) Applied physics, system science and computers III. APSAC 2018. Lecture Notes in Electrical Engineering, vol 574, pp 317–323

  • Bartz-Beielstein T, Lasarczyk CWG, Preuss M (2005) Sequential parameter optimization. IEEE Congress Evolut Comput 1:773–780

    MATH  Google Scholar 

  • Binod P, Sindhu R, Madhavan A, Abraham A, Mathew AK, Beevi US, Sukumaran RK, Singh SP, Pandey A (2017) Recent developments in l-glutaminase production and applications—an overview. Bioresour Technol 245:1766–1774. https://doi.org/10.1016/j.biortech.2017.05.059

    Article  Google Scholar 

  • Birattari M, Stützle T, Paquete L, Varrentrapp K (2002) A racing algorithm for configuring metaheuristics. Genet Evol Comput Conf 2:11–18

    Article  Google Scholar 

  • Blount ZD (2015) The unexhausted potential of E. coli. eLife. https://doi.org/10.7554/elife.05826

    Article  Google Scholar 

  • Campos L, Nogueira J, Coelho FA, Santos BF (2018) Parameter estimation of biosurfactant production from agro-industrial waste using genetic algorithm. In: Friedl A, Kleměs JJ, Radl S, Varbanov PS, Wallek T (eds) 28th European symposium on computer aided process engineering, volume 43 of computer aided chemical engineering. Elsevier, pp. 483–488. https://doi.org/10.1016/B978-0-444-64235-6.50086-3

  • Castillo O, Amador-Angulo L (2018) A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design. Inf Sci 460:476–496

    Article  Google Scholar 

  • Chipperfield A, Fleming PJ, Pohlheim H, Fonseca CM (1994) Genetic algorithm toolbox. For use with MATLAB. User’s Guide. Version 1.2. Department of Automatic Control and System Engineering, University of Sheffield, UK

  • Diwold K, Aderhold A, Scheidler A, Middendorf M (2011) Performance evaluation of artificial bee colony optimization and new selection schemes. Memetic Comput 3(3):149–162. https://doi.org/10.1007/s12293-011-0065-8

    Article  MATH  Google Scholar 

  • Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, Cambridge

    Book  Google Scholar 

  • Dutta S, Biswal M, Acharya S, Mishra R (2018) Fuzzy stochastic price scenario based portfolio selection and its application to BSE using genetic algorithm. Appl Soft Comput 62:867–891

    Article  Google Scholar 

  • Ederer M, Steinsiek S, Stagge S, Rolfe M, Ter Beek A, Knies D, Teixeira de Mattos J, Sauter T, Green J, Poole R et al (2014) A mathematical model of metabolism and regulation provides a systems-level view of how Escherichia coli responds to oxygen. Front Microbiol 5:124. https://doi.org/10.3389/fmicb.2014.00124

    Article  Google Scholar 

  • Edgington MP, Tindall MJ (2015) Understanding the link between single cell and population scale responses of Escherichia coli in differing ligand gradients. Comput Struct Biotechnol J 13:528–538

    Article  Google Scholar 

  • Eiben ÁE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evolut Comput 3(2):124–141

    Article  Google Scholar 

  • Faraji R, Naji HR (2014) An efficient crossover architecture for hardware parallel implementation of genetic algorithm. Neurocomputing 128:316–327

    Article  Google Scholar 

  • Ferreiro M, García-Rodríguez JA, Vázquez C, Silva EC, Correia A (2019) Parallel two-phase methods for global optimization on GPU. Math Comput Simul 156:67–90. https://doi.org/10.1016/j.matcom.2018.06.005

    Article  MathSciNet  Google Scholar 

  • Gartland KM, Gartland JS (2018) Opportunities in biotechnology. J Biotechnol 282:38–45. https://doi.org/10.1016/j.jbiotec.2018.06.303

    Article  Google Scholar 

  • Goldberg E (2006) Genetic algorithms in search, optimization and machine learning. Addison Wesley Longman, London

    Google Scholar 

  • Gong X, Plets D, Tanghe E, Pessemier TD, Martens L, Joseph W (2018) An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments. Appl Soft Comput 65:243–259. https://doi.org/10.1016/j.asoc.2018.01.016

    Article  Google Scholar 

  • Gu W, Yu Y, Hu W (2017) Artificial bee colony algorithm-based parameter estimation of fractional-order chaotic system with time delay. IEEE/CAA J Autom Sin 4(1):107–113

    Article  MathSciNet  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems, 2nd edn. MIT Press, Cambridge

    Book  Google Scholar 

  • Huang C, Lin H, Yang X (2012) Industrial production of recombinant therapeutics in Escherichia coli and its recent advancements. J Ind Microbiol Biotechnol 39:383–399

    Article  Google Scholar 

  • Jesenik M, Mernik M, Cřepinšek M, Ravber M, Trlep M (2018) Searching for soil models’ parameters using metaheuristics. Appl Soft Comput 69:131–148. https://doi.org/10.1016/j.asoc.2018.04.045

    Article  Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. Erciyes University, Engineering Faculty, Computer Engineering Department

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697

    Article  Google Scholar 

  • D. Karaboga, B. Akay, C. Ozturk (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: Modeling decisions for artificial intelligence. LNCS, vol 4617. Springer, pp 318–329

  • Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57. https://doi.org/10.1007/s10462-012-9328-0

    Article  Google Scholar 

  • Kumar A, Kumar D, Jarial SK (2016) A comparative analysis of selection schemes in the artificial bee colony algorithm. Computacion y Sistemas 20:55–66. https://doi.org/10.13053/CyS-20-1-2228

    Article  Google Scholar 

  • Kumar D, Kumar D, Jarial SK (2017) A review on artificial bee colony algorithms and their applications to data clustering. Cybern Inf Technol 17(3):3–28

    MathSciNet  Google Scholar 

  • Kunde C, Kienle A (2018) Global optimization of multistage binary separation networks. Chem Eng Process Process Inten 131:164–177. https://doi.org/10.1016/j.cep.2018.06.024

    Article  Google Scholar 

  • Lee C (2018) A review of applications of genetic algorithms in operations management. Eng Appl Artif Intell 76:1–12. https://doi.org/10.1016/j.engappai.2018.08.011

    Article  Google Scholar 

  • Lee NK, Li X, Wang D (2018) A comprehensive survey on genetic algorithms for DNA motif prediction. Inf Sci 466:25–43

    Article  MathSciNet  Google Scholar 

  • Li X, Yang G (2016) Artificial bee colony algorithm with memory. Appl Soft Comput 41:362–372. https://doi.org/10.1016/j.asoc.2015.12.046

    Article  Google Scholar 

  • Li Y, Zhou C, Zheng X (2014) The application of artificial bee colony algorithm in protein structure prediction. In: Pan L, Păun G, Pérez-Jiménez MJ, Song T (eds) Bio-inspired computing-theories and applications. Communications in computer and information science, vol 472, pp 255–258

  • Liao T, Doğan A, Stützle T (2013) Artificial bee colonies for continuous optimization: experimental analysis and improvements. Swarm Intell 7(4):327–356

    Article  Google Scholar 

  • Lin W-Y, Lee W-Y, Hong T-P (2003) Adapting crossover and mutation rates in genetic algorithms. J Inf Sci Eng 19(5):889–903

    Google Scholar 

  • Lin W-C, Xu J, Bai D, Chung I-H, Liu S-C, Wu C-C (2018) Artificial bee colony algorithms for the order scheduling with release dates. Soft Comput 23:8677–8688. https://doi.org/10.1007/s00500-018-3466-5

    Article  Google Scholar 

  • Liu H, Xu B, Lu D, Zhang G (2018) A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Appl Soft Comput 68:360–376. https://doi.org/10.1016/j.asoc.2018.04.015

    Article  Google Scholar 

  • Lobo CF, Lima Z (2007) Michalewicz, parameter setting in evolutionary algorithms, vol 54. Springer, New York

    Book  Google Scholar 

  • Madhavan A, Sindhu R, Binod P, Sukumaran RK, Pandey A (2017) Strategies for design of improved biocatalysts for industrial applications. Bioresour Technol 245:1304–1313. https://doi.org/10.1016/j.biortech.2017.05.031

    Article  Google Scholar 

  • Mernik M, Liu SH, Karaboga D, Črepinšek M (2015) On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation. Inf Sci 291:115–127

    Article  MathSciNet  Google Scholar 

  • Morrison C, Lähteenmäki R (2016) Public biotech in 2016—the numbers. Nat Biotechnol 35(2017):623–629. https://doi.org/10.1038/nbt.3917

    Article  Google Scholar 

  • Nannen V, Eiben AE (2007) Relevance estimation and value calibration of evolutionary algorithm parameters. Int Jt Conf Artif Intell 7:975–980

    Google Scholar 

  • Okdem S, Karaboga D, Ozturk C (2011) An application of wireless sensor network routing based on artificial bee colony algorithm. In: IEEE congress on evolutionary computation (CEC), pp 326–330

  • Olivas F, Valdez F, Castillo O, Gonzalez CI, Martinez G, Melin P (2017) Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems. Appl Soft Comput 53:74–87

    Article  Google Scholar 

  • Olivas F, Valdez F, Melin P, Sombra A, Castillo O (2019) Interval type-2 fuzzy logic for dynamic parameter adaptation in a modified gravitational search algorithm. Inf Sci 476:159–175

    Article  Google Scholar 

  • Pan H, Zhong W, Wang Z, Wang G (2018) Optimization of industrial boiler combustion control system based on genetic algorithm. Comput Electric Eng 70:987–997. https://doi.org/10.1016/j.compeleceng.2018.03.003

    Article  Google Scholar 

  • Peker M (2018) A fully customizable hardware implementation for general purpose genetic algorithms. Appl Soft Comput 62:1066–1076

    Article  Google Scholar 

  • Pierre DM, Zakaria N (2017) Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows. Appl Soft Comput 52:863–876

    Article  Google Scholar 

  • Poolman T (2017) Escherichia coli. In: Quah SR (ed) International encyclopedia of public health, 2nd edn. Academic Press, Oxford, pp 585–593

    Chapter  Google Scholar 

  • Roeva O (2012) A comparison of simulated annealing and genetic algorithm approaches for cultivation model identification. Walter de Gruyter, pp 193–201. https://doi.org/10.1007/978-3-319-12631-9_7

  • Roeva O (2014) Genetic algorithm and firefly algorithm hybrid schemes for cultivation processes modelling. In: Nguyen NT, Kowalczyk R, Fred A, Joaquim F (eds) Transactions on computational collective intelligence, vol XVII. Springer, Berlin, pp 196–211. https://doi.org/10.1007/978-3-662-44994-3_10

    Chapter  Google Scholar 

  • Roeva O (2018) Application of artificial bee colony algorithm for model parameter identification. In: Zelinka I, Vasant P, Duy V, Dao T (eds) Innovative computing, optimization and its applications. Studies in computational intelligence, vol 741, pp 285–303

  • Roeva O, Fidanova S (2012) A comparison of genetic algorithms and ant colony optimization for modelling of E. coli cultivation process. In: Roeva O (ed) Real-world application of genetic algorithms. InTech, pp 261–282. https://doi.org/10.5772/2674

  • Roeva O, Vassilev P (2016) Intercriteria analysis of generation gap influence on genetic algorithms performance. In: Novel developments in uncertainty representation and processing. Springer, pp 301–313. https://link.springer.com/chapter/10.1007/978-3-319-26211-6_26

  • Roeva O, Zoteva D (2018) Knowledge discovery from data: interCriteria analysis of mutation rate influence. Notes Intuitionistic Fuzzy Sets 24(1):120–130. https://doi.org/10.7546/nifs.2018.24.1.120-130

    Article  Google Scholar 

  • Roeva O, Fidanova S, Atanassova V (2013) Hybrid ACO-GA for parameter identification of an E. coli cultivation process model. In: International conference on large-scale scientific computing. Springer, pp 313–320. https://link.springer.com/chapter/10.1007/978-3-662-43880-0_35

  • Roeva O, Pencheva T, Tzonkov S, Hitzmann B (2015a) Functional state modelling of cultivation processes: dissolved oxygen limitation state. Int J Bioautom 19(1):S93–S112

    Google Scholar 

  • Roeva O, Fidanova S, Paprzycki M (2015) Population size influence on the genetic and ant algorithms performance in case of cultivation process modeling. In: Recent advances in computational optimization. Springer, pp 107–120. https://link.springer.com/chapter/10.1007/978-3-319-12631-9_7

  • Roeva O, Vassilev P, Fidanova S, Paprzycki M (2016) Intercriteria analysis of genetic algorithms performance. In: Recent advances in computational optimization. Springer, pp 235–260. https://link.springer.com/chapter/10.1007/978-3-319-40132-4_14

  • Roeva O, Pencheva T, Angelova M, Vassilev P (2016) Intercriteria analysis by pairs and triples of genetic algorithms application for models identification. In: Recent advances in computational optimization. Springer, pp 193–218. https://link.springer.com/chapter/10.1007/978-3-319-40132-4_12

  • Ryu KH, Kim B, Lee JH (2019) A model-based optimization of microalgal cultivation strategies for lipid production under photoautotrophic condition. Comput Chem Eng 121:57–66. https://doi.org/10.1016/j.compchemeng.2018.10.004

    Article  Google Scholar 

  • Salza P, Ferrucci F (2019) Speed up genetic algorithms in the cloud using software containers. Future Gen Comput Syst 92:276–289. https://doi.org/10.1016/j.future.2018.09.066

    Article  Google Scholar 

  • Shao G, Shangguan Y, Tao J, Zheng J, Liu T, Wen Y (2018) An improved genetic algorithm for structural optimization of AU–AG bimetallic nanoparticles. Appl Soft Comput 73:39–49. https://doi.org/10.1016/j.asoc.2018.08.019

    Article  Google Scholar 

  • Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746. https://doi.org/10.1016/j.asoc.2018.07.033

    Article  Google Scholar 

  • Singh A, Deep K (2019) Exploration–exploitation balance in artificial bee colony algorithm: a critical analysis. Soft Comput 23:9525–9536. https://doi.org/10.1007/s00500-018-3515-0

    Article  Google Scholar 

  • Sinha SK, Kumar M, Guria C, Kumar A, Banerjee C (2017) Biokinetic model-based multi-objective optimization of Dunaliella tertiolecta cultivation using elitist non-dominated sorting genetic algorithm with inheritance. Bioresour Technol 242:206–217. https://doi.org/10.1016/j.biortech.2017.03.146

    Article  Google Scholar 

  • Sirohi R, Singh A, Tarafdar A, Shahi N (2018) Application of genetic algorithm in modelling and optimization of cellulase production. Bioresour Technol 270:751–754. https://doi.org/10.1016/j.biortech.2018.09.105

    Article  Google Scholar 

  • Sörensen K, Sevaux M, Glover F (2016) A history of metaheuristics. In: OR2016: annual international conference of the german operations research society, Hamburg, Germany. https://arxiv.org/abs/1704.00853

  • Szczepanski R, Tarczewski T, Grzesiak LM (2019) Adaptive state feedback speed controller for PMSM based on artificial bee colony algorithm. Appl Soft Comput 83:105644. https://doi.org/10.1016/j.asoc.2019.105644

    Article  Google Scholar 

  • Tatsis VA, Parsopoulos KE (2019) Dynamic parameter adaptation in metaheuristics using gradient approximation and line search. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2018.09.034

    Article  Google Scholar 

  • Tehzeeb-ul-Hassan T, Alquthami SE, Butt MF, Tahir K (2020) Mehmood, short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm. Energy Rep 6:984–992. https://doi.org/10.1016/j.egyr.2020.04.003

    Article  Google Scholar 

  • Thomas D, Kovoor BC (2018) A genetic algorithm approach to autonomous smart vehicle parking system. Proc Comput Sci 125:68–76

    Article  Google Scholar 

  • Toimil D, Gómes A (2017) Review of metaheuristics applied to heat exchanger network design. Int Trans Oper Res 24(1–2):7–26

    Article  MathSciNet  Google Scholar 

  • Tumuluru JS, McCulloch R (2016) Application of hybrid genetic algorithm routine in optimizing food and bioengineering processes. Foods. https://doi.org/10.3390/foods5040076

    Article  Google Scholar 

  • Tylecote A (2019) Biotechnology as a new techno-economic paradigm that will help drive the world economy and mitigate climate change. Res Policy. https://doi.org/10.1016/j.respol.2018.10.001

    Article  Google Scholar 

  • Valdez F, Melin P, Castillo O (2014) A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Syst Appl 41(14):6459–6466

    Article  Google Scholar 

  • Veček N, Mernik M, Filipič B, Črepinšek M (2016) Parameter tuning with chess rating system (CRS-tuning) for meta-heuristic algorithms. Inf Sci 372:446–469

    Article  Google Scholar 

  • Veček N, Liu SH, Črepinšek M, Mernik M (2017) On the Importance of the artificial bee colony control parameter ‘limit’. Inf Technol Control 46(4):566–604

    Google Scholar 

  • Wang J, Duan L, Yang Y (2018) An improvement crossover operation method in genetic algorithm and spatial optimization of heliostat field. Energy 155:15–28

    Article  Google Scholar 

  • Wong WK, Ming CI (2019) A review on metaheuristic algorithms: recent trends, benchmarking and applications. In: 7th International conference on smart computing and communications (ICSCC). IEEE, pp 1–5

  • Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Stud Comput Intell 284:65–74

    MATH  Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via levy flights. In: Proceedings of world congress on nature and biologically inspired computing. IEEE, pp 210–214

  • Younas F, Kamrani M, Bashir J (2018) Schubert, efficient genetic algorithms for optimal assignment of tasks to teams of agents. Neurocomputing 314:409–428

    Article  Google Scholar 

  • Yuan B, Gallagher M (2004) Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. In: Parallel problem solving from nature, pp 172–181

  • Zacchigna S, Giacca M (2018) The global role of biotechnology for non communicable disorders. J Biotechnol 283:115–119. https://doi.org/10.1016/j.jbiotec.2018.07.043

    Article  Google Scholar 

  • Zimmer C (2008) Microcosm: E. coli and the new science of life. Pantheon Books, New York

    Google Scholar 

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Acknowledgements

This research has been partially supported by the National Scientific Fund of Bulgaria under the Grant КП-06-H32/3 “Interactive System for Education in Modelling and Control of Bioprocesses (InSEMCoBio)”. This research work did not receive funding.

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  1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Olympia Roeva & Dafina Zoteva

  2. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Oscar Castillo

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Roeva, O., Zoteva, D. & Castillo, O. Joint set-up of parameters in genetic algorithms and the artificial bee colony algorithm: an approach for cultivation process modelling. Soft Comput 25, 2015–2038 (2021). https://doi.org/10.1007/s00500-020-05272-1

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