Doctoral Dissertations

Author

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 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.

Advisor(s)

Wunsch, Donald C.

Committee Member(s)

Beetner, Daryl G.
Venayagamoorthy, Ganesh K.
Tauritz, Daniel R.
Moss, Randy Hays, 1953-

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2009

Pagination

xiv, 160 pages

Note about bibliography

Includes bibliographical references (pages 152-159).

Rights

© 2009 Ryan James Meuth, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Computational intelligence
Evolutionary computation
Genetic algorithms
Genetic programming (Computer science)
Traveling-salesman problem

Thesis Number

T 9591

Print OCLC #

631853912

Electronic OCLC #

495850862

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