Stochastic search algorithms strongly use randomised decisions while
searching for solutions to a given problem. They play an increasingly
important role for practically solving hard combinatorial problems
>from various domains of Artificial Intelligence and Operations
Research, such as satisfiability, constraint satisfaction, planning,
scheduling, and many application areas. Over the past few years there
has been considerable success in developing stochastic local search
algorithms as well as randomised systematic search methods for solving
these problems, and to date, for many problem domains, the best known
algorithms are based on stochastic search techniques. The increasing
relevance and popularity of these methods in AI are reflected by a
growing number of publications in journals, such as Artificial
Intelligence and the Journal of Automated Reasoning, and at major AI
conferences, such as IJCAI, ECAI, and AAAI.
This workshop will bring together researchers from different areas of
AI and Operations Research in order to discuss various topics in
stochastic search techniques, including the following:
-- stochastic local search algorithms
-- randomised systematic search methods
-- design and implementation of stochastic search algorithms
-- metaheuristics, learning techniques, and self-tuning algorithms
-- parallelisation and portfolios of stochastic search algorithms
-- empirical analysis and evaluation of stochastic search algorithms
-- theoretical results on stochastic search algorithms
-- new applications of stochastic search