Evolving Diverse Sets of Tours for the Travelling Salesperson Problem Do, Anh Viet Bossek, Jakob Neumann, Aneta Neumann, Frank diversity maximisation evolutionary algorithms travelling salesperson problem Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years. With this paper, we contribute to this area of research by examining evolutionary diversity optimisation approaches for the classical Traveling Salesperson Problem (TSP). We study the impact of using different diversity measures for a given set of tours and the ability of evolutionary algorithms to obtain a diverse set of high quality solutions when adopting these measures. Our studies show that a large variety of diverse high quality tours can be achieved by using our approaches. Furthermore, we compare our approaches in terms of theoretical properties and the final set of tours obtained by the evolutionary diversity optimisation algorithm. Association for Computing Machinery 2020 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/48879 Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’20. Association for Computing Machinery; 2020:681–689. doi:<a href="https://doi.org/10.1145/3377930.3389844">10.1145/3377930.3389844</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1145/3377930.3389844 info:eu-repo/semantics/altIdentifier/isbn/978-1-4503-7128-5 info:eu-repo/semantics/closedAccess