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

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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.

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Authors and Affiliations

  1. Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA

    Ryan Meuth & Donald C. Wunsch II

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Meng-Hiot Lim

  3. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Yew-Soon Ong

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  1. Ryan Meuth
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  2. Meng-Hiot Lim
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  4. Donald C. Wunsch II
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Correspondence to Ryan Meuth.

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Meuth, R., Lim, MH., Ong, YS. et al. A proposition on memes and meta-memes in computing for higher-order learning. Memetic Comp. 1, 85–100 (2009). https://doi.org/10.1007/s12293-009-0011-1

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