Memetic Computing

, Volume 1, Issue 2, pp 85–100

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

Authors

    • Applied Computational Intelligence Laboratory, Department of Electrical and Computer EngineeringMissouri University of Science and Technology
  • Meng-Hiot Lim
    • School of Electrical and Electronic EngineeringNanyang Technological University
  • Yew-Soon Ong
    • School of Computer EngineeringNanyang Technological University
  • Donald C. WunschII
    • Applied Computational Intelligence Laboratory, Department of Electrical and Computer EngineeringMissouri University of Science and Technology
Regular Research Paper

DOI: 10.1007/s12293-009-0011-1

Cite this article as:
Meuth, R., Lim, M., Ong, Y. et al. Memetic Comp. (2009) 1: 85. doi:10.1007/s12293-009-0011-1

Abstract

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.

Keywords

Machine learning Memetic computing Meta-learning Computational intelligence architectures

Copyright information

© Springer-Verlag 2009