# Evolving Diverse Sets of Tours for the Travelling Salesperson Problem

arXiv: https://arxiv.org/abs/2004.09188 · HTML: https://mlsanity.com/paper/2004.09188/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Towards a Stronger Theory for Permutation-based Evolutionary Algorithms](https://mlsanity.com/paper/2204.07637/) (2022)
- [Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax](https://mlsanity.com/paper/2307.07248/) (2023)
- [Runtime Analysis for Permutation-based Evolutionary Algorithms](https://mlsanity.com/paper/2207.04045/) (2022)

## Abstract (arXiv)

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.