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Improving Performance of Algorithm Selection Pipelines on Large Instance Sets via Dynamic Reallocation of Budget.

Quentin Renau¹, Emma Hart²

¹School of Computing, Engineering and the Built Environment, Edinburgh Napier University, UK q.renau2@napier.ac.uk.

Evolutionary Computation

|December 19, 2025

Summary

This summary is machine-generated.

This study introduces an improved algorithm selection (AS) pipeline. It saves computational budget on easy or stalled instances and reallocates it to others, boosting overall performance for both batch and streaming data.

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Area of Science:

Optimization
Machine Learning
Computational Performance

Background:

Algorithm selection (AS) is crucial for optimizing solver performance from a portfolio.
Large instance sets, whether streamed or batched, present opportunities to improve efficiency.
Current AS methods may not fully leverage budget-saving and reallocation strategies.

Purpose of the Study:

To develop an enhanced AS pipeline that optimizes function evaluation budget.
To improve overall performance by intelligently saving and reallocating computational resources.
To evaluate the proposed pipeline in both batch and streaming scenarios.

Main Methods:

Implemented an AS pipeline with three key strategies: identifying easy instances, curtailing stalled runs, and reallocating saved budget.
Utilized an intelligent strategy for predicting which instances benefit most from additional function evaluations.
Conducted experiments on the BBOB dataset in both batch and streaming settings.

Main Results:

The enhanced AS pipeline significantly outperformed a standard pipeline in both batch and streaming settings.
Identifying easy instances and curtailing stalled runs effectively saved computational budget.
Intelligent budget reallocation led to improved performance on downstream instances.

Conclusions:

Augmenting AS pipelines with budget-saving and reallocation strategies enhances overall performance.
The proposed pipeline offers a significant improvement for managing computational resources in large-scale optimization.
This approach is effective for both batch processing and real-time data streams.