This thesis work aims at improving the problem solving ability of the Gene Expression Programming (GEP) algorithm to fulfill complex data mining tasks by preserving and utilizing the self-emergence of structures during its evolutionary process. The main contributions include the investigation of the constant creation techniques for promoting good functional structures emergent in the evolution, analysis of the limitation with the current implementation scheme of GEP, and introduction of a novel utilization of the emergent structures to achieve a flexible search process for solutions at a higher level.
Subjects: 1.9 Genetic Algorithms; 12. Machine Learning and Discovery
Submitted: Apr 4, 2005