The insights from this study on numerical black-box optimization can be extended to other domains where automated algorithm selection is applied, such as combinatorial optimization or machine learning hyperparameter tuning, in the following ways:
Feature Engineering: Similar to numerical black-box optimization, feature engineering plays a crucial role in algorithm selection for combinatorial optimization and hyperparameter tuning. By identifying relevant features that capture the characteristics of the problem instances, AAS models can make more informed decisions.
Instance Representation: The concept of generating diverse problem instances, as explored in numerical black-box optimization, can be applied to combinatorial optimization and hyperparameter tuning. By creating a varied set of instances that cover different problem structures and complexities, AAS models can better generalize to unseen scenarios.
Transfer Learning: The use of transfer learning techniques, inspired by the study on numerical black-box optimization, can be beneficial in combinatorial optimization and hyperparameter tuning. By transferring knowledge from known instances to new problem domains, AAS models can adapt more efficiently and improve their performance on diverse problem sets.
Domain-Specific Adaptations: Tailoring the insights and methodologies from numerical black-box optimization to the specific characteristics of combinatorial optimization and hyperparameter tuning can enhance the effectiveness of AAS models in these domains. By considering the unique challenges and requirements of each domain, AAS models can be optimized for better performance and generalization.