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Computing diverse sets of high-quality solutions has gained increasingattention among the evolutionary computation community in recent years. Itallows practitioners to choose from a set of high-quality alternatives. In thispaper, we employ a population diversity measure, called the high-order entropymeasure, in an evolutionary algorithm to compute a diverse set of high-qualitysolutions for the Traveling Salesperson Problem. In contrast to previousstudies, our approach allows diversifying segments of tours containing severaledges based on the entropy measure. We examine the resulting evolutionarydiversity optimisation approach precisely in terms of the final set ofsolutions and theoretical properties. Experimental results show significantimprovements compared to a recently proposed edge-based diversity optimisationapproach when working with a large population of solutions or long segments.

Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem