Exploring the Feature Space of TSP Instances Using Quality Diversity
This work addresses the need for varied TSP instances to improve algorithm selection methods, though it is incremental as it builds on existing evolutionary computation techniques.
The paper tackles the problem of generating diverse Traveling Salesman Problem (TSP) instances for algorithm selection by introducing a quality diversity (QD) approach, which successfully explores the entire feature space and produces instances that differentiate solver performance, outperforming a baseline evolutionary algorithm.
Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by a $(μ+1)$-EA for TSP instance generation.