The behavior of spatially distributed evolutionary algorithms in non-stationary environments
Jayshree Sarma, Kenneth Alan De Jong · 1999
Traditional EAs lose diversity fairly quickly due to the strong selection pressures used to achieve good optimization performance, and thus have difficulty with non-stationary environments (fitness landscapes) unless significant algorithmic changes are made. Decentralized and spatially distributed EAs intuitively appear to be more robust in their ability to perform well in both stationary and non-stationary problem domains. We explore this hypothesis with a set of empirical studies that, although preliminary in nature, supports this claim and provides some additional insights into properties of spatially distributed EAs.