Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process. The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.
Improved generic acceptance function for Multi-point Metropolis algorithm
