The insights from this work on continuous optimization can be applied to algorithm selection and performance prediction in combinatorial optimization domains by adapting the trajectory-based approach to suit the specific characteristics of combinatorial optimization problems. In combinatorial optimization, the search space is discrete, and the objective is to find the best combination of variables rather than optimizing a continuous function.
One way to apply these insights is to develop trajectory-based methods tailored to combinatorial optimization problems. Instead of using continuous trajectories, the approach could involve generating trajectories that capture the search process of combinatorial optimization algorithms, such as genetic algorithms or ant colony optimization, on specific instances. These trajectories could then be used as input to ML models for algorithm selection and performance prediction in combinatorial optimization domains.
Additionally, the meta-algorithm approach of tuning parameters for trajectory generation could be extended to combinatorial optimization algorithms. By tuning the parameters of combinatorial optimization algorithms to produce discriminatory trajectories, the approach could enhance algorithm selection and performance prediction in combinatorial optimization domains. This adaptation would involve considering the unique characteristics and requirements of combinatorial optimization problems to ensure the effectiveness of the trajectory-based approach.