Meta-learning computational intelligence architectures

"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.

Name thesis:degree_name

Ph. D. in Computer Engineering

Grantor

Missouri University of Science and Technology

Year dc:date.available

2016

Author dc:creator

• Meuth, Ryan J.

• Computer Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/2209

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-3211