Ripple-Spreading Network Model Optimization by Genetic Algorithm

Abstract

Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM) is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs) of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA) to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.

A Game Theoretical Approach for Solving Winner Determination Problems

“…The simulation results are compared with the general GA and the Casanova. The GA is outstanding in solving optimization problems, such as optimizing [17] and solving the WDP [10]. The Casanova converges rapidly, so we combine findNE() with the Casanova, termed as the Hybrid, in the anytime performance evaluation.…”

Epidemic Modelling by Ripple-Spreading Network and Genetic Algorithm

“…Figure 1 gives an illustration of the development of a ripple-spreading network. For more details, readers are referred to [21,22].…”

“…Who has infected whom during an epidemic outbreak can be illustrated by a growing network that is simulated by which stake's ripple has triggered which following stake to generate a new ripple. Now, one may get a feeling that the ripplespreading network model invented in [21,22] can be used to simulate the outbreak of plagues. To this end, we first need to, based on the work reported in [21,22], develop a mathematical ripple-spreading network model of epidemic, which hereafter is called ripple-spreading epidemic model, denoted as RSEM.…”

“…In the proposed RSEM, there are two groups of parameters. The first group is those of the general ripple-spreading network model as reported in [21,22], but some modifications may be necessary in order to fit them in the scope of epidemiology. In this group, first we have parameters for EISR epicenters of initial stimulating ripples (EISRs), which are related to those initial cases in an epidemic outbreak.…”

“…Inspired by the natural ripple-spreading phenomenon on liquid surfaces, this paper reports a novel complex system based bottom-up epidemic model: ripple-spreading epidemic model (RSEM), which is an application-focused extension of our recent work on general ripple-spreading network models [21,22]. As widely acknowledged, random contacts and movements of individuals impose a big challenge to epidemic modelling.…”