# Automated Algorithm Selection for Radar Network Configuration

arXiv: https://arxiv.org/abs/2205.03670 · HTML: https://mlsanity.com/paper/2205.03670/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 7 of 7)

- [IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics](https://mlsanity.com/paper/1810.05281/) (2018)
- [Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco](https://mlsanity.com/paper/1708.05258/) (2017)
- [Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy](https://mlsanity.com/paper/2006.11135/) (2020)
- [IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics](https://mlsanity.com/paper/2007.03953/) (2020)
- [Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants](https://mlsanity.com/paper/2006.09855/) (2020)
- [Evolving the Structure of Evolution Strategies](https://mlsanity.com/paper/1610.05231/) (2016)
- [The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection](https://mlsanity.com/paper/2104.09272/) (2021)

## Built on by (most cited first, 2 of 2)

- [On the Utility of Probing Trajectories for Algorithm-Selection](https://mlsanity.com/paper/2401.12745/) (2024)
- [Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples](https://mlsanity.com/paper/2404.05359/) (2024)

## Abstract (arXiv)

The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed. Classic optimization algorithms can therefore not be applied to this problem, and we rely on "trial-and-error" black-box approaches. In this paper, we study the performances of 13 black-box optimization algorithms on 153 radar network configuration problem instances. The algorithms perform considerably better than human experts. Their ranking, however, depends on the budget of configurations that can be evaluated and on the elevation profile of the location. We therefore also investigate automated algorithm selection approaches. Our results demonstrate that a pipeline that extracts instance features from the elevation of the terrain performs on par with the classical, far more expensive approach that extracts features from the objective function.