Organizational Learning Within A Learning Classifier System

This thesis recasts the debate between Michigan-style and Pitt-style classi er systems to a debate on appropriately sizing organizations within a learning classi er system. Motivated by the economic study of transaction costs, an organizational classi er system (OCS) combining explicit use of multiple reputation values and organization sizing operators better distinguishes parasitic (less than optimal) classi ers than a simple classi er system (SCS). The results show that by building a system that autonomously adjusts the degree of individual to collective behavior, it is possible for it to be both e cient and resilient to problem di culty. iii Acknowledgments I thank my parents, although no amount of thanks could su ciently repay their unfailing love and support. I thank Dr. David Goldberg, my adviser, for his input, support, and creative ideas. I thank Kevin Carmody and Je rey Horn for their friendship and support. I thank Rebecca Robinson for taking care of things while I worked on this thesis. Support for this work was provided by the U.S. Air Force O ce of Scienti c Research under Grant No. F49620-94-1-0103 and from the National Aeronautics and Space Administration with Grant No. NGT-50873. iv Table of