Stochastic local search (SLS) algorithms are established tools for the solution of
computationally hard problems arising in computer science business adm- istration engineering
biology and various other disciplines. To a large extent their success is due to their
conceptual simplicity broad applicability and high performance for many important problems
studied in academia and enco- tered in real-world applications. SLS methods include a wide
spectrum of te- niques ranging from constructive search procedures and iterative improvement
algorithms to more complex SLS methods such as ant colony optimization evolutionary
computation iterated local search memetic algorithms simulated annealing tabu search and
variable neighborhood search. Historically the development of e?ective SLS algorithms has been
guided to a large extent by experience and intuition. In recent years it has become -
creasingly evident that success with SLS algorithms depends not merely on the adoption and
e?cient implementation of the most appropriate SLS technique for a given problem but also on
the mastery of a more complex algorithm - gineering process. Challenges in SLS algorithm
development arise partly from the complexity of the problems being tackled and in part from the
many - grees of freedom researchers and practitioners encounter when developing SLS algorithms.
Crucial aspects in the SLS algorithm development comprise al- rithm design empirical analysis
techniques problem-speci?c background and background knowledge in several key disciplines and
areas including computer science operations research arti?cial intelligence and statistics.
