A Memetic Fuzzy ARTMAP by a Grammatical Evolution Approach

  • Shing Chiang TanEmail author
  • Chee Peng Lim
  • Junzo Watada
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)


This paper presents a memetic fuzzy ARTMAP (mFAM) model constructed using a grammatical evolution approach. mFAM performs adaptation through a global search with particle swarm optimization (PSO) as well as a local search with the FAM training algorithm. The search and adaptation processes of mFAM are governed by a set of grammatical rules. In the memetic framework, mFAM is constructed and it evolves with a combination of PSO and FAM learning in an arbitrary sequence. A benchmark study is carried out to evaluate and compare the classification performance between mFAM and other state-of-art methods. The results show the effectiveness of mFAM in providing more accurate prediction outcomes.


Grammatical evolution Fuzzy ARTMAP Particle swarm optimization Data classification 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Shing Chiang Tan
    • 1
    Email author
  • Chee Peng Lim
    • 2
  • Junzo Watada
    • 3
  1. 1.Multimedia UniversityCyberjayaMalaysia
  2. 2.Deakin UniversityWaurn PondsAustralia
  3. 3.Waseda UniversityTokyoJapan

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