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Content Provider | ACM Digital Library |
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Author | Meuth, Ryan J. |
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. |
Starting Page | 2101 |
Ending Page | 2102 |
Page Count | 2 |
File Format | |
ISBN | 9781450300735 |
DOI | 10.1145/1830761.1830882 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2010-07-07 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Even parity Genetic programming Meta-learning Late breaking abstract Memetic algorithms Pac-man |
Content Type | Text |
Resource Type | Article |
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