InterCriteria Analysis Approach for Comparison of Simple and Multi-population Genetic Algorithms Performance

  • Maria AngelovaEmail author
  • Tania Pencheva
Part of the Studies in Computational Intelligence book series (SCI, volume 795)


Intercriteria analysis approach, based on the apparatuses of index matrices and intuitionistic fuzzy sets, has been here applied for comparison of simple and multi-population genetic algorithms performance. Six kinds of simple genetic algorithms and six kinds of multi-population genetic algorithms, differing in the execution order of the main genetic operators selection, crossover and mutation are in the focus of current investigation. Intercriteria analysis approach is implemented to assess the performance of mentioned above genetic algorithms for the purposes of parameter identification of Saccharomyces cerevisiae fed-batch fermentation process. For the completeness, the performance of altogether twelve algorithms have been also assessed. Degrees of agreement and disagreement between the algorithms outcomes, namely convergence time and model accuracy, from one hand, and model parameters estimations, from the other hand, have been established. The obtained results after the application of intercriteria analysis have been compared and outlined relations have been thoroughly discussed.


InterCriteria Analysis (ICrA) Multi-population GA Intuitionistic Fuzzy Sets (IFS) Main Genetic Operators Algorithm Convergence Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work is partially supported by the National Science Fund of Bulgaria under Grant DM-07/1 “Development of New Modified and Hybrid Metaheuristic Algorithms” and Grant DN-02/10 “New Instruments for Knowledge Discovery from Data, and Their Modelling”.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Biophysics and Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria

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