# Automatic Construction of Parallel Portfolios via Explicit Instance Grouping

arXiv: https://arxiv.org/abs/1804.06088 · HTML: https://mlsanity.com/paper/1804.06088/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 11 of 11)

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- [Memory efficient location recommendation through proximity-aware representation](https://mlsanity.com/paper/2310.06484/) (2023)
- [Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning](https://mlsanity.com/paper/2312.01739/) (2023)
- [Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles](https://mlsanity.com/paper/2506.22848/) (2025)
- [RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation](https://mlsanity.com/paper/2608.05646/) (2026)

## Abstract (arXiv)

Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems. However, manually building such solver portfolios typically requires considerable domain knowledge and plenty of human effort. As an alternative, automatic construction of parallel portfolios (ACPP) aims at automatically building effective parallel portfolios based on a given problem instance set and a given rich design space. One promising way to solve the ACPP problem is to explicitly group the instances into different subsets and promote a component solver to handle each of them.This paper investigates solving ACPP from this perspective, and especially studies how to obtain a good instance grouping.The experimental results showed that the parallel portfolios constructed by the proposed method could achieve consistently superior performances to the ones constructed by the state-of-the-art ACPP methods,and could even rival sophisticated hand-designed parallel solvers.