Evolutionary multi-agent systems in non-stationary environments

In this 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 behavior of EMAS against classical evolutionary techniques. Then, the properties of energy-based selection are investigated to show how they 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.