InterCriteria Analysis of Generation Gap Influence on Genetic Algorithms Performance

  • Olympia Roeva
  • Peter VassilevEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 401)


In this investigation InterCriteria Analysis (ICA) is applied to examine the influences of one of the genetic algorithms parameters—the generation gap (ggap). The investigation is carried out during the model parameter identification of E. coli MC4110 cultivation process. The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between ggap and GAs outcomes (computational time and decision accuracy), on one hand, and cultivation process model parameters on the other hand. The obtained results after ICA application are analyzed in terms of convergence time and model accuracy and some conclusions about derived interactions are reported.


Intercriteria analysis Genetic algorithms Generation gap Parameter identification E. coli 



The work is supported by the Bulgarian National Scientific Fund under the grant DFNI-I-02-5 “InterCriteria Analysis—A New Approach to Decision Making”.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

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