An Evolutionary Approach to Network Self-Organization and Resilient Data Diffusion

Abstract—Data diffusion techniques enable a distributed system to replicate and propagate data across a potentially unreliable network in order to provide better data protection and availability. This paper presents a novel evolutionary computation approach to developing network construction al-gorithms and data diffusion strategies. The proposed approach combines a linear genetic program with a cellular automaton to evolve digital organisms (agents) capable of self-organizing into different types of networks and self-adapting to changes in their surrounding environment, such as link failures and node churn. We assess the effectiveness of the proposed approach by conducting several experiments that explore different net-work structures under different environmental conditions. The results suggest the combined methods are able to produce self-organizing and self-adaptive agents that construct networks and efficiently distribute data throughout the network, while balancing competing concerns, such as minimizing energy consumption and providing reliability. Keywords-Evolutionary algorithm, genetic programming, cel-lular automata, self-organization, data diffusion. I