Evolving Neurocontrollers for the Control of Information Diffusion in Social Networks

This paper presents a comparison of two Evolutionary Artificial Neural Network (EANN) variants acting as the autonomous control system for instances of the θ-Consensus Avoidance Problem (θ-CAP). A novel variant of EANN is proposed by adopting characteristics of a well-performing heuristic into the structural bias of the neurocontroller. Information theoretic landscape measures are used to analyze the problem space as well as variants of the EANN. The results obtained indicate that the two neurocontroller variants learn distinctly different approaches to the θ-CAP, however, the newly proposed variant demonstrates improvements in both solution quality and execution time. A ramped-difficulty evolution scheme is demonstrated to be effective at creating higher quality results as compared to the standard scheme for EANNs. A correlation between the proposed instance difficulty and identifiable landscape characteristics is discovered as well.