Visual Analysis of Evolutionary Search for Explainable Models of Epidemic Propagation

This paper presents a visualization method useful for understanding the behaviour of an evolutionary algorithm tasked with discovering the propagation mechanism driving the spread of an epidemic. In this paper, the explainability in computational intelligence is addressed at two levels: first, a bi-objective Genetic Programming approach is used, which allows constructing a human-readable description of the disease propagation model based on observations or simulation results. Second, a visualization technique is used for understanding the working of the evolutionary algorithm itself with the aim of discovering clusters of similar solutions, which can later be analyzed in detail. Experimental results show that the proposed Genetic Programming approach is able to discover a human-readable representation of the epidemic propagation mechanism effectively. The visual analysis allowed discovering two clusters of solutions, one in which solutions are similar to the exact representation of the propagation mechanism, and second in which solutions are logically equivalent to the propagation mechanism but are much more bloated.