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An Analysis of Evolutionary Algorithms for Multiobjective Optimization of Structure and Learning of Fuzzy Cognitive Maps Based on Multidimensional Medical Data

  • Alexander Yastrebov
  • Łukasz Kubuś
  • Katarzyna PoczetaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11934)

Abstract

The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. The aim of this research study is an automatic construction of a collection of FCM models based on various criteria depending on the structure of the model and forecasting capabilities. The simulation analysis was performed with the use of the developed multiobjective Individually Directional Evolutionary Algorithm. Experiments show that the collection of fuzzy cognitive maps, in which each element is built on the basis of particular patient data, allows us to receive higher forecasting accuracy compared to the standard approach. Moreover, by appropriate aggregation of these collections we can also obtain satisfactory accuracy of forecasts for the new patient.

Keywords

Fuzzy cognitive maps Multiobjective optimization Evolutionary algorithms Multidimensional medical data 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Kielce University of TechnologyKielcePoland

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