- author = "Su Nguyen and Dhananjay Thiruvady and Mengjie Zhang and Kay Chen Tan",
- title = "A Genetic Programming Approach for Evolving Variable Selectors in Constraint Programming",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2021",
- volume = "25",
- number = "3",
- pages = "492--507",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Job-Shop Scheduling, Makespan, Tardiness, Heuristics",
- ISSN = "1089-778X",
-
DOI = "
10.1109/TEVC.2021.3050465",
- size = "16 pages",
- abstract = "Operational researchers and decision modelers have aspired to optimisation technologies with a self-adaptive mechanism to cope with new problem formulations. Self-adaptive mechanisms not only free users from low-level and complex development tasks to enhance optimisation efficiency, but also allow them to focus on addressing high-level real-world operational requirements. In recent years, there has been a growing interest in applying machine learning and artificial intelligence techniques to improve self-adaptive mechanisms. However, learning to optimise hard combinatorial optimisation problems remains a challenging task. This paper proposes a new genetic programming approach to evolve efficient variable selectors to enhance the search mechanism in constraint programming. Starting with a set of training instances for a specific combinatorial optimisation problem, the proposed approach evaluates variable selectors and evolves them to be more efficient over a number of generations. The novelties of our proposed approach are threefold: (a) a new representation of variable selectors; (b) a new mechanism for fitness evaluations; and (c) a pre-selection technique. We examine performance of the proposed approach on different job shop scheduling problems, and the results show that variable selectors can be evolved efficiently. In particular, there are substantial reductions in the computational effort required for the search component of the constraint solver as well as increased chances of finding the optimal solutions. Further analyses also confirm the efficacy of our approach in respect to scalability, generalisation, and interpretability of the evolved variable selectors.",
-
notes = "also known as \cite{9319512}
Centre for Data Analytics and Cognition, La Trobe University, Australia.",
