## InterCriteria Analysis of Multi-population Genetic Algorithms Performance

### Maria Angelova, Tania Pencheva

DOI: http://dx.doi.org/10.15439/2017F171

Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 77–82 (2017)

Abstract. InterCriteria Analysis approach is here applied for the assessment of promising genetic algorithms optimization techniques. Altogether six multi-population genetic algorithms are here considered, differing in the execution order of main genetic operators selection, crossover and mutation. InterCriteria Analysis approach, based on the apparatuses of index matrices and intuitionistic fuzzy sets, is implemented to assess the performance of multi-population genetic algorithms for the parameter identification of Saccharomyces cerevisiae fed-batch fermentation process. Degrees of ``agreement'' and ``disagreement'' between the algorithms outcomes convergence time and model accuracy, from one hand, and model parameters estimations, from the other hand, have been established. Outlined relations are going to lead to an additional exploring of the model, expected to be extraordinary valuable especially in the case of modelling of living systems, such as fermentation processes.

### References

- M. Angelova and T. Pencheva, “Genetic operators’ significance assess- ment in multi-population genetic algorithms”, Int. J. of Metaheuristics, vol. 3(2), 2014, pp. 162-173, http://dx.doi.org/10.1504/IJMHEUR.2014.063146.
- M. Angelova, O. Roeva and T. Pencheva, “Intercriteria analysis of crossover and mutation rates relations in simple genetic algorithm”, Ann. Comp. Sci. Info. Syst., vol. 5, 2015, pp. 419-424, http://dx.doi.org/10.15439/2015F178.
- M. Angelova, S. Tzonkov and T. Pencheva,“Modified multi-population genetic algorithm for yeast fed-batch cultivation parameter identification”, Int. J. Bioautomation, vol. 13(4), 2009, pp. 163-172.
- K. Atanassov, On intuitionistic fuzzy sets theory, Springer, Berlin, 2012, http://dx.doi.org/10.1007/978-3-642-29127-2.
- K. Atanassov, “Generalized index matrices”, C. R. Acad. Bulg. Sci., vol. 40(11), 1987, pp. 15-18.
- K. Atanassov, “On index matrices, part 1: standard cases”, Adv. Stud. Cont. Math., vol. 20(2), 2010, pp. 291-302.
- K. Atanassov, “On index matrices, part 2: intuitionistic fuzzy case”, Proc. Jangjeon Math. Soc., vol. 13(2), 2010, pp. 121-126.
- K. Atanassov, D. Mavrov and V. Atanassova, “Intercriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets”, Iss. Int. Fuz. Sets and Gen. Nets, vol. 11, 2014, pp. 1-8.
- A. Ghaheri, S. Shoar, M. Naderan and S. S. Hoseini, “The applications of genetic algorithms in medicine”, Oman Med. J., vol. 30(6), 2015, pp. 406-416, http://dx.doi.org/10.5001/omj.2015.82.
- D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison Wesley Longman, London, 2006.
- J. Holland, Adaptation in natural and artificial systems: an introductory analysis with application to biology, control and artificial intelligence, University of Michigan Press, 1975.
- T. Ilkova and M. Petrov, “Intercriteria analysis for identification of Escherichia coli fed-batch mathematical model”, J. Int. Sci. Publ.: Mat., Meth. & Techn., vol. 9, 2015, pp. 598-608.
- T. Pencheva and M. Angelova, “Intercriteria analysis of simple genetic algorithms performance”, Advanced Computing in Industrial Mathematics, Vol. 681 of Studies in Computational Intelligence, 2017, 147-159, http://dx.doi.org/10.1007/978-3-319-49544-6 13.
- T. Pencheva and M. Angelova, “Modified multi-population genetic algorithms for parameter identification of yeast fed-batch cultivation”, Bulg. Chem. Comm., vol. 48(4), 2016, pp. 713-719.
- T. Pencheva, O. Roeva and I. Hristozov, Functional state approach to fermentation processes modelling, Prof. M. Drinov Acad. Publ. House, Sofia, 2006.
- O. Roeva (Ed.), Real-world application of genetic algorithms, InTech, 2012, http://dx.doi.org/10.5772/2674.
- O. Roeva, S. Fidanova, P. Vassilev and P. Gepner, “Intercriteria analysis of a model parameters identification using genetic algorithm”, Ann. Comp. Sci. Inf. Syst., vol. 5, 2015, pp. 501-506, http://dx.doi.org/10.15439/2015F223.