Memetic Computing

, Volume 1, Issue 2, pp 85–100 | Cite as

A proposition on memes and meta-memes in computing for higher-order learning

  • Ryan MeuthEmail author
  • Meng-Hiot Lim
  • Yew-Soon Ong
  • Donald C. WunschII
Regular Research Paper


In computational intelligence, the term ‘memetic algorithm’ has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a ‘meme’ has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as ‘memetic algorithm’ is too specific, and ultimately a misnomer, as much as a ‘meme’ is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two conceptual case studies, we illustrate the power of high-order meme-based learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.


Machine learning Memetic computing Meta-learning Computational intelligence architectures 


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Ryan Meuth
    • 1
    Email author
  • Meng-Hiot Lim
    • 2
  • Yew-Soon Ong
    • 3
  • Donald C. WunschII
    • 1
  1. 1.Applied Computational Intelligence Laboratory, Department of Electrical and Computer EngineeringMissouri University of Science and TechnologyRollaUSA
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore

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