# Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

arXiv: https://arxiv.org/abs/1901.11120 · HTML: https://mlsanity.com/paper/1901.11120/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Multi-Fidelity Methods for Optimization: A Survey](https://mlsanity.com/paper/2402.09638/) (2024)
- [Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2401.02160/) (2024)
- [Automated Few-Shot Time Series Forecasting based on Bi-level Programming](https://mlsanity.com/paper/2203.03328/) (2022)
- [Art-Attack: Black-Box Adversarial Attack via Evolutionary Art](https://mlsanity.com/paper/2203.04405/) (2022)

## Abstract (arXiv)

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes.