Ranking-based Black-box Complexity

Randomized search heuristics such as evolutionary algorithms, simulated annealing, and ant colony optimization are a broadly used class of general-purpose algorithms. Analyzing them via classical methods of theoretical computer science is a growing field. While several strong runtime analysis results have appeared in the last 20 years, a powerful complexity theory for such algorithms is yet to be developed. We enrich the existing notions of black-box complexity by the additional restriction that not the actual objective values, but only the relative quality of the previously evaluated solutions may be taken into account by the black-box algorithm. Many randomized search heuristics belong to this class of algorithms. We show that the new ranking-based model can give more realistic complexity estimates. The class of all binary-value functions has a black-box complexity of O(\log n) in the previous black-box models, but has a ranking-based complexity of \Theta(n). On the other hand, for the class of all \onemax functions, we present a ranking-based black-box algorithm that has a runtime of \Theta(n / \log n), which shows that the \onemax problem does not become harder with the additional ranking-basedness restriction