Optimization of systemic stability of directed network using genetic algorithm

Flow dynamics in directed network can lead to cascade failures from node and link removal, and this is used as a paradigm for systemic risks in financial systems where the flow is a money flow. In order to reduce systemic risk, we analyze the network topology and find ways of rewiring to ensure that the time for the first node failure can be maximized. The analysis is numerical using genetic algorithm to evolve a network by rewiring towards one with higher systemic stability. The results show that a network can become more systemic stable if the incoming flow of all the nodes becomes more similar to that of the outgoing flow. For financial network, the way to reduce the risk of cascade bankruptcies is to share the systemic risk in the form of the fluctuation of capital value transfer by all banks. Our simple model of directed network shows that one way to improve the systemic stability of a network is to rewire it towards a perfect Watts-Strogatz network.