Cultural algorithms in dynamic environments

Reasoning about environmental change in dynamic environments is a key factor in predicting an agent´s next move in those environments. Cultural Algorithms provide a mechanism to reason about environmental dynamics. In this study, cultural algorithms show encouraging results when applied to environments where the problem is to find the highest peak in a multidimensional landscape, where the peaks are moving over time. Here, we use De Jong´s environmental dynamics simulator and observe how tracking of change is affected by the frequency of change, and the magnitude for both a self-adaptive EP and a cultured EP version. It is shown that the cultured system is less sensitive to the environmental changes and outperforms the self-adaptive EP system particularly when the frequency of change becomes very large