Black-box complexity, counting the number of queries needed to find the optimum of a problem without having access to an explicit problem description, was introduced by Droste, Jansen, and Wegener [S. Droste, T. Jansen, I. Wegener, Upper and lower bounds for randomized search heuristics in black-box optimization, Theory of Computing Systems 39 (2006) 525-544] to measure the difficulty of solving an optimization problem via generic search heuristics such as evolutionary algorithms, simulated annealing or ant colony optimization. Since then, a number of similar complexity notions were introduced. However, so far these new notions were only analyzed for artificial test problems. In this paper, we move a step forward and analyze the different black-box complexity notions for two classic combinatorial problems, namely the minimum spanning tree and the single-source shortest path problem. Besides proving bounds for their black-box complexities, our work reveals that the choice of how to model the optimization problem has a significant influence on its black-box complexity. In addition, when regarding the unbiased (symmetry-invariant) black-box complexity of combinatorial problems, it is important to choose a meaningful definition of unbiasedness.© 2012 Elsevier B.V. All rights reserved.
Doerr, B., Kötzing, T., Lengler, J., & Winzen, C. (2013). Black-box complexities of combinatorial problems. Theoretical Computer Science, 471, 84–106. https://doi.org/10.1016/j.tcs.2012.10.039