KNOWLEDGE-BASED SELF-ADAPTATION IN EVOLUTIONARY SEARCH

Abstract

Self-adaptation has been frequently employed in evolutionary computation. Angeline1 defined three distinct adaptive levels which are: population, individual and component levels. Cultural Algorithms 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 population level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems over population-only evolutionary systems.