Chan-Jin Chung, Lawrence Technological University and Robert G. Reynolds, Wayne State University
Self-adaptation has been frequently employed in evolutionary search. Angeline  defines three distinct levels of self- adaptation which are: population,individual,and component levels. Cultural Algorithms developed by Reynolds have been shown to provide a framework in which to model self-adaptation at each of these levels.Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the pop- ulation level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. Properties of functional lanscapes as suggested by Winston  are used to classify the functions associated with 27 different benchmark optimization problems. The results suggest that the type of knowledge used to direct effective search for a given problem depends upon the combination of properties that are found in the associated functional landscape.