Stochastic local search (SLS) algorithms are among the most powerful techniques for
solving computationally hard problems in many areas of computing science, operations
research and engineering. SLS techniques range from rather simple constructive and
iterative improvement algorithms to general-purpose SLS methods such as ant colony
optimization, iterated local search, memetic algorithms and tabu search. In recent
years, it has become evident that the development of effective SLS algorithms is a
complex engineering process that typically combines aspects of algorithm design and
implementation with empirical analysis and problem-specific background knowledge.

In this talk, we first give a concise introduction to stochastic local search and
present an overview of important topics in the path towards an engineering
methdology for stochastic local search algorithms. We further illustrate some of
these topics by results of our research in this area, including the (further)
development of "simple" SLS methods, tools for the automatic tuning of SLS
algorithms, and experimental methodologies for the analysis of SLS algorithms. We
end this talk by a discussion of relevant future research topics in SLS.