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 models that map features to expected performance. Features capture properties such as size, structure, or domain-specific statistics. The portfolio encompasses diverse methods and tunings to cover different instance types. Performance is typically measured by runtime, memory usage, or solution quality.

Algsele procedures typically proceed in three stages: (1) feature extraction from problem instances, (2) performance modeling using historical data to predict outcomes, and (3) decision rules that select an algorithm or configure it before deployment. Models may be rule-based or learned via machine learning, including meta-models that predict total time or success probability.

Applications span optimization, SAT/SMT solving, constraint programming, and machine learning hyperparameter tuning. In practice, algsele interplays with algorithm portfolios and hyperparameter optimization to reduce average solution time and to improve reliability across diverse workloads.

Related concepts include the algorithm selection problem, algorithm portfolios, and meta-learning for algorithm configuration. As a research area, algsele emphasizes reproducibility, robust evaluation, and transparent reporting of features and models.