Ripple-Spreading Model and Genetic Algorithm for Random Complex Networks: Preliminary Study

Recently complex network theory has been broadly applied in various domains. How to effectively and efficiently optimize the topology of complex networks remains largely an unsolved fundamental question. When applied to the network topology optimization, Genetic Algorithms (GAs) are often confronted with permutation representation, memory-inefficiency and stochastic modeling problems, as well as difficulties in the design of problem-specific evolutionary operators. This paper, inspired by the natural ripple spreading phenomenon, reports a deterministic model of random complex networks. Unlike existing stochastic models, the topology of a random network can be thoroughly determined by some ripple-spreading related parameters in the new model. Therefore, the network topology can be improved by optimize these ripple-spreading related parameters. As a result, no purpose-designed GA is required, but a very basic binary GA, compatible to all classic evolutionary operators, can be applied in a straightforward way. Preliminary simulation results demonstrate the potential of the proposed ripple-spreading model and GA for the topology optimization of random complex networks.