A Proposition on Memes and Meta-memes in Computing for Higher-order Learning
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.
R. J. Meuth et al., "A Proposition on Memes and Meta-memes in Computing for Higher-order Learning," Memetic Computing, Springer-Verlag, Jun 2009.
The definitive version is available at http://dx.doi.org/10.1007/s12293-009-0011-1
Electrical and Computer Engineering
Keywords and Phrases
Computational Intelligence Architectures; Machine Learning; Memetic Computing; Meta-Learning
Article - Journal
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