A genetic algorithm for rule extraction in fuzzy adaptive learning control networks

Glender Brás*, Alisson Marques Silva, Elizabeth F. Wanner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper presents a novel approach, dubbed Falcon-GA, for rule extraction in a Fuzzy Adaptive Learning Control Network (FALCON) using a Genetic Algorithm (GA). The FALCON-GA combines multiple techniques to establish the relationships and connections among fuzzy rules, including the use of a GA for rule extraction and a Gradient-based method for fine-tuning the membership function parameters. The learning algorithm of FALCON-GA incorporates three key components: the ART (Adaptive Resonance Theory) clustering algorithm for initial membership function identification, the Genetic Algorithm for rule extraction, and the Gradient method for adjusting membership function parameters. Moreover, FALCON-GA offers flexibility by allowing the incorporation of different rule types within the FALCON architecture, making it flexible and expansible. The proposed model has been evaluated in various forecasting problems reported in the literature and compared to alternative models. Computational experiments demonstrate the effectiveness of FALCON-GA in forecasting tasks and reveal significant performance improvements compared to the original FALCON. These results indicate that Genetic Algorithms efficiently extract rules for Fuzzy Adaptive Learning Control Networks.

Original languageEnglish
Article number11
Number of pages31
JournalGenetic Programming and Evolvable Machines
Issue number1
Early online date30 Mar 2024
Publication statusPublished - Jun 2024


  • Forecasting
  • Fuzzy systems
  • Genetic algorithm
  • Rule extraction


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