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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. In order to make the refined process dynamic, it pursues its implementation using Self-Organising Neural Networks. This article refines 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. Finally, we introduce dynamic goals by making the preferred neighbour type a function of the local characteristics of a simulated workload. Professor Kamareddine is an Associate Editor of The Computer Journal and is based in the Department of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh, UK. The Capsule Reviews were compiled by Fairouz Kamareddine. The outcome of this is a Pareto front of optimal strategies, all of which would satisfy the objectives.ArrayThe Capsule Reviews are intended to provide a short succinct review of each