A proposition on memes and metamemes in computing for higherorder learning
 Ryan Meuth,
 MengHiot Lim,
 YewSoon Ong,
 Donald C. Wunsch II
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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 highorder memebased learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide muchneeded stimulation to the field of computational intelligence by providing a framework for higher order learning.
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 Title
 A proposition on memes and metamemes in computing for higherorder learning
 Journal

Memetic Computing
Volume 1, Issue 2 , pp 85100
 Cover Date
 20090601
 DOI
 10.1007/s1229300900111
 Print ISSN
 18659284
 Online ISSN
 18659292
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Machine learning
 Memetic computing
 Metalearning
 Computational intelligence architectures
 Authors

 Ryan Meuth ^{(1)}
 MengHiot Lim ^{(2)}
 YewSoon Ong ^{(3)}
 Donald C. Wunsch II ^{(1)}
 Author Affiliations

 1. Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA
 2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
 3. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore