Evolving test instances of the Hamiltonian completion problem

Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there usually no standard set of to benchmark performance. Second, using existing generators results in a restricted spectrum difficulty the resulting graphs are not diverse enough draw sound conclusions. That why recent work proposes new methodology generate by an evolutionary algorithm. We can then analyze get key insights into which attributes most related also fill observed gaps instance space order with previously unseen combinations features. This applied Hamiltonian completion problem two different solvers, namely Concorde TSP Solver multi-start local search