Ryan J. Meuth
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 genetic programming test-beds (the even-parity problem and the Pac-Man video game), we demonstrate the power of high-order meme-based learning, known as meta-learning. With applications ranging from cognitive science to machine learning, meta-learning 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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Index Terms
- Meta-learning genetic programming
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