# Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification

arXiv: https://arxiv.org/abs/2409.01446 · HTML: https://mlsanity.com/paper/2409.01446/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning](https://mlsanity.com/paper/1711.08921/) (2017)
- [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)
- [Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules](https://mlsanity.com/paper/2102.12905/) (2021)
- [DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis](https://mlsanity.com/paper/2304.01219/) (2023)
- [MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts](https://mlsanity.com/paper/2312.11083/) (2023)
- [BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances](https://mlsanity.com/paper/2211.16318/) (2022)
- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Using Affine Combinations of BBOB Problems for Performance Assessment](https://mlsanity.com/paper/2303.04573/) (2023)
- [Challenges of ELA-guided Function Evolution using Genetic Programming](https://mlsanity.com/paper/2305.15245/) (2023)
- [A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems](https://mlsanity.com/paper/2104.13060/) (2021)

## Built on by (most cited first, 1 of 1)

- [OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization](https://mlsanity.com/paper/2512.12809/) (2025)

## Abstract (arXiv)

In landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical applications. In this work, we investigate the potential of randomly generated functions (RGF) for the model training, which cover a much more diverse set of optimization problem classes compared to the widely-used black-box optimization benchmarking (BBOB) suite. Correspondingly, we focus on automated algorithm configuration (AAC), that is, selecting the best suited algorithm and fine-tuning its hyperparameters based on the landscape features of problem instances. Precisely, we analyze the performance of dense neural network (NN) models in handling the multi-output mixed regression and classification tasks using different training data sets, such as RGF and many-affine BBOB (MA-BBOB) functions. Based on our results on the BBOB functions in 5d and 20d, near optimal configurations can be identified using the proposed approach, which can most of the time outperform the off-the-shelf default configuration considered by practitioners with limited knowledge about AAC. Furthermore, the predicted configurations are competitive against the single best solver in many cases. Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.