These are travelling salesman problems that were created by an evolutionary algorithm with the objective function to maximise the time it takes to solve these problems by one of two Lin-Kernighan heuristic solvers.
See these papers for a detailed description:
- Discovering the suitability of optimisation algorithms by learning from evolved instances (K. Smith-Miles, J.I. van Hemert), In Annals of Mathematics and Artificial Intelligence, volume 61, 2011.
- Evolving combinatorial problem instances that are difficult to solve (J.I. van Hemert), In Evolutionary Computation, volume 14, 2006.
- Property analysis of symmetric travelling salesman problem instances acquired through evolution (J.I. van Hemert), In Evolutionary Computation in Combinatorial Optimization (G. Raidl, J. Gottlieb, eds.), Springer, 2005.
- Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation (J.I. van Hemert, N.B. Urquhart), In Parallel Problem Solving from Nature (Xin Yao, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervós, John A. Bullinaria, Jonathan Rowe, Peter Ti\vno Ata Kabán, Hans-Paul Schwefel, eds.), Springer, volume 3242, 2004.