Evolutionary Multi-Agent Systems in Non-Stationary Environments

In the article the performance of an evolutionary multi-agent system in  dynamic optimization is evaluated in comparison to classical evolutionary  algorithms.  The starting point is a general introduction describing the  background, structure and behaviour of EMAS against classical  evolutionary techniques.  Then the properties of energy-based selection  are investigated to show how it may influence the diversity of the  population in EMAS.  The considerations are illustrated by experimental  results based on the dynamic version of the well-known, high-dimensional Rastrigin function  benchmark