# Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis

arXiv: https://arxiv.org/abs/2403.06568 · HTML: https://mlsanity.com/paper/2403.06568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics](https://mlsanity.com/paper/2111.04077/) (2021)
- [IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics](https://mlsanity.com/paper/2007.03953/) (2020)
- [Benchmarking Discrete Optimization Heuristics with IOHprofiler](https://mlsanity.com/paper/1912.09237/) (2019)
- [Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance](https://mlsanity.com/paper/2106.06304/) (2021)
- [COCO: Performance Assessment](https://mlsanity.com/paper/1605.03560/) (2016)
- [BandMaxSAT: A Local Search MaxSAT Solver with Multi-armed Bandit](https://mlsanity.com/paper/2201.05544/) (2022)
- [Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT Solvers](https://mlsanity.com/paper/2108.09988/) (2021)

## Abstract (arXiv)

Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated based on the quality, e.g., fitness, of the best-found solutions obtained within a given running time budget. However, concerning solely the final obtained solutions regarding specific time budgets may restrict us from comprehending the behavior of the solvers along the convergence process. This paper demonstrates that Empirical Cumulative Distribution Functions can be used to compare MaxSAT stochastic local search solvers' anytime performance across multiple problem instances and various time budgets. The assessment reveals distinctions in solvers' performance and displays that the (dis)advantages of solvers adjust along different running times. This work also exhibits that the quantitative and high variance assessment of anytime performance can guide machines, i.e., automatic configurators, to search for better parameter settings. Our experimental results show that the hyperparameter optimization tool, i.e., SMAC, can achieve better parameter settings of solvers when using the anytime performance as the cost function, compared to using the metrics based on the fitness of the best-found solutions.