Using Ant Colony Optimization and Genetic Algorithms for the Linguistic Summarization of Creep Data

  • Carlos A. Donis-DíazEmail author
  • Rafael Bello
  • Janusz Kacprzyk
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 322)


Some models using metaheuristics based in an “improvement of solutions” procedure, specifically Genetic Algorithms (GA), have been proposed previously to the linguistic summarization of numerical data (LDS). In the present work is proposed a new model for LDS based in Ant Colony Optimization (ACO), a metaheuristic that use a “construction of solution” procedure. Both models are compared in LDS over creep data. Results show how the ACO based model overcomes the measures of goodness of the final summary but fails to improve the results of the GA based model in relation to the diversity of the summary. Features of both models are considered to explain the results.


Linguistic Data Summarization Ant Colony Optimization Genetic Algorithms Fuzzy Logic Creep rupture stress 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Carlos A. Donis-Díaz
    • 1
    Email author
  • Rafael Bello
    • 1
  • Janusz Kacprzyk
    • 2
  1. 1.Informatic Studies CenterUniversidad Central Marta Abreu de Las VillasSanta ClaraCuba
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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