"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 dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. 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"--Abstract, page iii.
Wunsch, Donald C.
Beetner, Daryl G.
Venayagamoorthy, Ganesh K.
Tauritz, Daniel R.
Moss, Randy Hays, 1953-
Electrical and Computer Engineering
Ph. D. in Computer Engineering
Missouri University of Science and Technology
xiv, 160 pages
© 2009 Ryan James Meuth, All rights reserved.
Dissertation - Open Access
Library of Congress Subject Headings
Genetic programming (Computer science)
Print OCLC #
Electronic OCLC #
Link to Catalog Record
Meuth, Ryan J., "Meta-learning computational intelligence architectures" (2009). Doctoral Dissertations. 2209.