Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection

TL;DR

This paper introduces a method to identify easy instances that can be quickly solved without complex analysis, significantly reducing computational costs in algorithm selection pipelines and improving overall optimization performance.

Contribution

The paper presents a novel approach to detect easy instances to enhance efficiency in ML-based algorithm selection, saving computational resources and boosting performance.

Findings

Substantial savings in function evaluations achieved.

Re-allocating saved budget improves solver performance.

Method effective on BBOB dataset in batch and streaming settings.

Abstract

Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely on an analysis phase, e.g. where features are computed by sampling solutions and used as input in a machine-learning model. For AS to be efficient, it is therefore important that this analysis phase is not computationally expensive. We propose a method for identifying easy instances which can be solved quickly using a generalist solver without any need for algorithm-selection. This saves computational budget associated with feature-computation which can then be used elsewhere in an AS pipeline, e.g., enabling additional function evaluations on hard problems. Experiments on the BBOB dataset in two settings (batch and streaming) show that identifying easy instances results in substantial savings in function evaluations. Re-allocating the saved budget to hard problems provides gains in performance compared to both the virtual best solver (VBS) computed with the original budget, the single best solver (SBS) and a trained algorithm-selector.