String Variables for Constraint-Based Local Search

Abstract: String variables occur as a natural part of many computationally challenging problems. Usually, such problems are solved using problem-specific algorithms implemented from first principles, which can be a time-consuming and error-prone task. A constraint solver is a framework that can be used to solve computationally challenging problems by first declaratively defining the problem and then solving it using specialised off-the-shelf algorithms, which can cut down development time significantly and result in faster solution times and higher solution quality. There are many constraint solving technologies, one of which is constraint-based local search (CBLS). However, very few constraint solvers have native support for solving problems with string variables. The goal of this thesis is to add string variables as a native type to the CBLS solver OscaR/CBLS. The implementation was experimentally evaluated on the Closest String Problem and the Word Equation System problem. The evaluation shows that string variables for CBLS can be a viable option for solving string problems. However, further work is required to obtain even more competitive performance.