A proposition on memes and meta-memes in computing for higher-order learning |
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Authors: | Ryan Meuth Meng-Hiot Lim Yew-Soon Ong Donald C Wunsch II |
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Institution: | 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
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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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