- author = "Ryan J. Meuth",
- title = "Meta-learning genetic programming",
- booktitle = "GECCO 2010 Late breaking abstracts",
- year = "2010",
- editor = "Daniel Tauritz",
- isbn13 = "978-1-4503-0073-5",
- keywords = "genetic algorithms, genetic programming",
- pages = "2101--2102",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Portland, Oregon, USA",
-
DOI = "
doi:10.1145/1830761.1830882",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- 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. Using 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.",
-
notes = "Also known as \cite{1830882} Distributed on CD-ROM at
GECCO-2010.
ACM Order Number 910102.",
