  Computing diverse sets of high-quality solutions has gained increasing
attention among the evolutionary computation community in recent years. It
allows practitioners to choose from a set of high-quality alternatives. In this
paper, we employ a population diversity measure, called the high-order entropy
measure, in an evolutionary algorithm to compute a diverse set of high-quality
solutions for the Traveling Salesperson Problem. In contrast to previous
studies, our approach allows diversifying segments of tours containing several
edges based on the entropy measure. We examine the resulting evolutionary
diversity optimisation approach precisely in terms of the final set of
solutions and theoretical properties. Experimental results show significant
improvements compared to a recently proposed edge-based diversity optimisation
approach when working with a large population of solutions or long segments.
