Decades of innovations in combinatorial problem solving have produced better and more complex algorithms. These new methods are better since they can solve larger problems and address new application domains. They are also more complex which means that they are hard to reproduce and often harder to fine tune to the peculiarities of a given problem. This last point has created a paradox where efficient tools became out of reach for practitioners.
Autonomous Search (AS) represents thus a new research field defined to precisely address the previous challenge. Its major strength and its originality are that problem solvers should now be able to perform self-improvement operations based on an analysis of the performances of the solving process. This includes:
- short-term reactive reconfiguration and long-term improvement through self-analysis of the performance,
- offline tuning and online control,
- adaptive control and supervised control. Autonomous Search has been defined to "cross the chasm" and to provide engineers and practitioners with systems that are able to autonomously self-tune their performance while effectively solving problems.