Evolutionary Multi-Agent Systems in Non-Stationary Environments
AbstractIn 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.
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