Evolutionary learning of fuzzy models

Duc Pham*, Marco Castellani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


This paper presents an evolutionary algorithm for generating knowledge bases for fuzzy logic systems. The algorithm dynamically adjusts the focus of the genetic search by dividing the population into three sub-groups, each concerned with a different level of knowledge base optimisation. The algorithm was tested on the identification of two highly non-linear simulated plants. Such a task represents a challenging test for any learning technique and involves two opposite requirements, the exploration of a large high-dimensional search space and the achievement of the best modelling accuracy. The algorithm achieved learning results that compared favourably with those for alternative knowledge base generation methods.

Original languageEnglish
Pages (from-to)583-592
Number of pages10
JournalEngineering Applications of Artificial Intelligence
Issue number6
Publication statusPublished - Sept 2006


  • Evolutionary algorithms
  • Fuzzy logic
  • Systems modeling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering


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