Continuous Search in Constraint Programming: An Initial Investigation

In this work, we present the concept of Continuous Search, the objective of which is to allow any user to eventually get top performance from their constraint solver. Unlike previous approaches (see [9] for a recent survey), Continuous Search does not require the disposal of a large set of representative instances to properly train and learn parameters. It only assumes that once the solver runs in a real situation (often called production mode), instances will come over time, and allow for proper offline continuous training. The objective is therefore not to instantly provide good parameters for top performance, but to take advantage of the real situation to train in the background and improve the performances of the system in an incremental manner.