Comparison of Representations to Evolve Weighted Contact Networks with Epidemic Properties

Two evolutionary algorithms are presented for the construction of weighted graphs: one based on self-driving automata (SDA), and one based on “editing” the edges of a graph. The algorithms are evaluated for their success at generating weighted contact networks likely to exhibit specified epidemic behaviour. Two main problems are considered: maximizing the length of the epidemic, and matching the profile (“curve”) of an epidemic, including one based on real-life data. Both algorithms significantly improve upon previous results using unweighted graphs. In most experiments the best overall results are obtained by the SDA algorithm, while the edge-editing algorithm usually has better mean fitness. This result is in part due to the SDA algorithm being more exploratory when compared to the one based on edge editing, which is more exploitative.