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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
