Towards a theory of randomized search heuristics

There is a well-developed theory about the algorithmic complexity of optimization problems. Complexity theory provides negative results which typically are based on assumptions like NPnot equalP or NPnot equalRP. Positive results are obtained by the design and analysis of clever algorithms. These algorithms are well-tuned for their specific domain. Practitioners, however, prefer simple algorithms which are easy to implement and which can be used without many changes for different types of problems. They report surprisingly good results when applying randomized search heuristics like randomized local search, tabu search, simulated annealing, and evolutionary algorithms. Here a framework for a theory of randomized search heuristics is presented. It is discussed how randomized search heuristics can be delimited from other types of algorithms. This leads to the theory of black-box optimization. Lower bounds in this scenario can be proved without any complexity-theoretical assumption. Moreover, methods how to analyze randomized search heuristics, in particular, randomized local search and evolutionary algorithms are presented.