Comparing the properties of meta-heuristic optimization techniques with various parameters on a fuzzy rule-based classifier

A. Tormási, L. Kóczy

Research output: Article


In this paper, the results of meta-heuristic optimization techniques with various parameter settings are presented. A formerly published Fuzzy-Based Recognizer (FUBAR): A fuzzy rule-based classification algorithm was used to analyze and evaluate the behavior of the used meta-heuristic optimization algorithms for rule-base optimization. Besides the reached accuracy, the execution time, the CPU load of the algorithms, and the effects of the shapes of the fuzzy membership functions in the initial rule-base are also investigated.

Original languageEnglish
Pages (from-to)157-169
Number of pages13
JournalStudies in Fuzziness and Soft Computing
Publication statusPublished - 2016


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mathematics

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