Building meta-learning algorithms basing on search controlled by machine complexity

Abstract— Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Metalearning algorithm presented in this paper is universal and may be applied to any type of CI problems. The novelty of our proposal lies in complexity controlled testing combined with very useful learning machines generators. The simplest and the best solutions are strongly preferred and are explored earlier. The learning algorithm is augmented by meta-knowledge repository which accumulates information about progress of the search through the space of candidate solutions. The approach facilitates using human experts knowledge to restrict the search space and provide goal definition, gaining meta-knowledge in an automated manner.

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Norbert Jankowski, Krzysztof Grabczewski
• Keywords: Artificial Intelligence, CI Problems, Complexity Controlled Testing, IJCNN 2008, Learning Machines Generators