algsele
Algsele is a theoretical framework in computer science for selecting and evaluating algorithms for specific problem instances. The term blends algorithm selection with empirical evaluation, emphasizing a disciplined approach to choosing the most effective algorithm or configuration for a given task. It models the selection problem as a supervised or reinforcement-like process, guided by observed performance on representative data.
Core concepts include a portfolio of candidate algorithms, a set of features describing problem instances, and
Algsele procedures typically proceed in three stages: (1) feature extraction from problem instances, (2) performance modeling
Applications span optimization, SAT/SMT solving, constraint programming, and machine learning hyperparameter tuning. In practice, algsele interplays
Related concepts include the algorithm selection problem, algorithm portfolios, and meta-learning for algorithm configuration. As a