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Conflicting goals are introduced, in the form of a population of nodes actively seeking to acquire neighbours of a type different from their own, resulting in decreased local homogeneity. The distribution of optimal strategies on a Pareto front, however, suggests that there may be a ‘best-fit’ optimal strategy.In this paper, we investigate the global self-aggregation dynamics arising from local decision-based rewiring of an overlay network, used as an abstraction for an autonomic service-oriented architecture.Genetic Algorithms may be used together with Pareto Optimality in the process of selection of a suitable video content adaptation strategy, the former to return best or fittest solutions that have evolved over many generations and the latter to evaluate and rank each generation's solutions against a set of objectives without the need to assign weights to each one. The outcome of this is a Pareto front of optimal strategies, all of which would satisfy the objectives. Finally, we introduce dynamic goals by making the preferred neighbour type a function of the local characteristics of a simulated workload. We demonstrate that in this context, an overlay rewiring process based purely on local decisions and interactions can result in efficient load-balancing without central planning. Scalability issues with respect to the key parameters of system size and diversity are extensively discussed.ArrayThe Capsule Reviews are intended to provide a short succinct review of each paper