As of August 2024, International Journal "Notes on Intuitionistic Fuzzy Sets" is being indexed in Scopus.
Please check our Instructions to Authors and send your manuscripts to nifs.journal@gmail.com. Next issue: September/October 2024.
Open Call for Papers: International Workshop on Intuitionistic Fuzzy Sets • 13 December 2024 • Banska Bystrica, Slovakia/ online (hybrid mode).
Deadline for submissions: 16 November 2024.

Issue:Knowledge discovery from data: InterCriteria Analysis of mutation rate influence

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to navigation Jump to search
shortcut
http://ifigenia.org/wiki/issue:nifs/24/1/120-130
Title of paper: Knowledge discovery from data: InterCriteria Analysis of mutation rate influence
Author(s):
Olympia Roeva
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
olympia@biomed.bas.bg
Dafina Zoteva
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
dafy.zoteva@gmail.com
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 24 (2018), Number 1, pages 120–130
DOI: https://doi.org/10.7546/nifs.2018.24.1.120-130
Download:  PDF (252 Kb  Kb, File info)
Abstract: In this paper the InterCriteria Analysis (ICrA) approach is applied to find more knowledge from series of identification procedures using 34 differently tuned genetic algorithms (GAs). The influence of the mutation rate pm on the algorithm performance is investigated. An E. coli fed-batch fermentation process model is used as a test problem. Based on the results from parameter identification, namely objective function values, the GAs, with the correspondent pm-value, producing the best results are determined. Frther, ICrA is applied using information from all model parameter estimates, computational time and objective function value. The ICrA confirms the conclusions based only on objective function values and helps to choose what mutation rate pm is more appropriate to use in the considered case study.
Keywords: InterCriteria Analysis, Intuitionistic fuzzy sets, Genetic algorithms, Mutation rate, E. coli.
AMS Classification: 03E72
References:
  1. Angelova, M., Roeva, O., & Pencheva, T. (2015) InterCriteria analysis of a cultivation process model based on the genetic algorithm population size influence. Notes on Intuitionistic Fuzzy Sets, 21(4), 90–103.
  2. Atanassov, K. (2012) On Intuitionistic Fuzzy Sets Theory, Springer, Berlin.
  3. Atanassov, K. (2010) On Index Matrices, Part 1: Standard Cases. Advanced Studies in Contemporary Mathematics, 20(2), 291–302.
  4. Atanassov, K., Mavrov, D., & Atanassova, V. (2014) Intercriteria decision making: A new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues in IFSs and GNs, 11, 1–8.
  5. Atanassov, K., Atanassova, V., & Gluhchev, G. (2015) InterCriteria Analysis: Ideas and problems. Notes on Intuitionistic Fuzzy Sets, 21(1), 81–88.
  6. Bastin, G., & Dochain, D. (1991) On-line Estimation and Adaptive Control of Bioreactors, Elsevier Scientific Publications.
  7. Capko, D., Erdeljan, A., Vukmirovic, S., & Lendak, I. (2011) A hybrid Genetic Algorithms for partitioning of data model in distribution management system. Information Technology and Control, 40(4), 316–321.
  8. Fidanova, S., Roeva, O., Paprzycki, M., & Gepner, P. (2016) InterCriteria Analysis of ACO Start Startegies. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, 547–550. 129
  9. Goldberg, D. E. (2006) Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley Longman, London.
  10. Ilkova, T., & Petrov, M. (2016) Intercriteria analysis for evaluation of the pollution of the Struma River in the Bulgarian section. Notes on Intuitionistic Fuzzy Sets, 22(3), 120–130.
  11. Krawczak, M., Bureva, V., Sotirova, E., & Szmidt, E. (2016) Application of the InterCriteria decision making method to universities ranking. Advances in Intelligent Systems and Computing, 401, 365–372.
  12. Laboudi, Z., & Chikhi, S. (2012) Comparison of genetic algorithms and quantum genetic algorithms. Int. Arab J Inf. Tech., 9(3), 243–249.
  13. Matsuoka, Y., & Shimizu, K. (2012) Importance of Understanding the Main Metabolic Regulation in Response to the Specific Pathway Mutation for Metabolic Engineering of Escherichia coli. Comput Struct Biotechnol J., 3(4), e201210018, http://dx.doi.org/10.5936/csbj.201210018.
  14. Patil, V.P., & Pawar, D.D. (2015) The optimal crossover or mutation rates in genetic algorithm: A review. International Journal of Applied Engineering and Technology, 5(3), 38–41.
  15. Parvez, W., & Dhar, S. (2013) Study of Optimal Mutation Factor for Genetic Algorithm. International Journal of Application or Innovation in Engineering and Management, 2(6), 429–432.
  16. Prasad, G., Singh, D., Mishra, A., & Shah, V.H. (2017) Genetic Algorithm Performance Assessment by Varying Population Size and Mutation Rate in Case of String Reconstruction. Journal of Basic and Applied Engineering Research, 4(2), 157–161.
  17. Roeva, O., & Vassilev, P. (2016) InterCriteria Analysis of Generation Gap Influence on Genetic Algorithms Performance. Advances in Intelligent Systems and Computing, 401, 301– 313.
  18. Roeva, O., Pencheva,T., Hitzmann, B., & Tzonkov, St. (2004) A Genetic Algorithms Based Approach for Identification of Escherichia coli Fed-batch Fermentation. International Journal Bioautomation, 1, 30–41.
  19. Sotirov, S., Sotirova, E., Melin, P., Castillo, O., & Atanassov, K. (2016) Modular Neural Network Preprocessing Procedure with Intuitionistic Fuzzy InterCriteria Analysis Method. In: Flexible Query Answering Systems 2015, Springer International Publishing, 175–186.
  20. Stratiev, D., Sotirov, S., Shishkova, I., Nedelchev, A., Sharafutdinov, I., Veli, A., Mitkova, M., Yordanov, D., Sotirova, E., Atanassova, V., Atanassov, K., Stratiev, D., Rudnev, N., & Ribagin, S. (2016) Investigation of relationships between bulk properties and fraction properties of crude oils by application of the Intercriteria Analysis. Petroleum Science and Technology, 34(13), 1113–1120.
Citations:

The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.

Retrieved from "https://ifigenia.org/index.php?title=Issue:Knowledge_discovery_from_data:_InterCriteria_Analysis_of_mutation_rate_influence&oldid=11796"