Abstract
This paper introduces a metapopulation evolutionary algorithm (MEA) for multi-objective optimisation problems. Insights from landscape ecology and population dynamics are used to develop a robust algorithm that combines the diffusion properties of cellular parallel genetic algorithms and island properties of distributed models. Two alternate selection mechanisms-a Pareto based technique and a novel environmental gradient aggregation technique-are analysed. Preliminary results suggest that the hypothesis of improved performance for spatially heterogenous populations is correct. The dynamic selection pressure, which emerges as a result of the changing environmental structure, helps to maintain population diversity and subsequently solution quality.
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