Cultural algorithms in dynamic environments

Abstract

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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.