# MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts

arXiv: https://arxiv.org/abs/2306.10627 · HTML: https://mlsanity.com/paper/2306.10627/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics](https://mlsanity.com/paper/1810.05281/) (2018)
- [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)
- [Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules](https://mlsanity.com/paper/2102.12905/) (2021)
- [Per-run Algorithm Selection with Warm-starting using Trajectory-based Features](https://mlsanity.com/paper/2204.09483/) (2022)
- [BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances](https://mlsanity.com/paper/2211.16318/) (2022)
- [OPTION: OPTImization Algorithm Benchmarking ONtology](https://mlsanity.com/paper/2104.11889/) (2021)
- [Using Affine Combinations of BBOB Problems for Performance Assessment](https://mlsanity.com/paper/2303.04573/) (2023)
- [Evolving test instances of the Hamiltonian completion problem](https://mlsanity.com/paper/2011.02291/) (2020)
- [RF+clust for Leave-One-Problem-Out Performance Prediction](https://mlsanity.com/paper/2301.09524/) (2023)
- [Generating Instances with Performance Differences for More Than Just Two Algorithms](https://mlsanity.com/paper/2104.14275/) (2021)
- [Continuous Optimization Benchmarks by Simulation](https://mlsanity.com/paper/2008.06249/) (2020)

## Built on by (most cited first, 7 of 7)

- [MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization](https://mlsanity.com/paper/2505.17745/) (2025)
- [carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks](https://mlsanity.com/paper/2506.06143/) (2025)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties](https://mlsanity.com/paper/2601.18846/) (2026)
- [Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model](https://mlsanity.com/paper/2501.11414/) (2025)
- [Pareto-Optimal Anytime Algorithms via Bayesian Racing](https://mlsanity.com/paper/2603.08493/) (2026)
- [A New Approach to Characterising Optimisation Problems Using Programmatic Representation and Complexity Measures](https://mlsanity.com/paper/2608.08898/) (2026)

## Abstract (arXiv)

Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COmparing COntinuous Optimizers (COCO) platform, we propose in this work a further generalization that allows multiple affine combinations of the original instances and arbitrarily chosen locations of the global optima. We demonstrate that the MA-BBOB generator can help fill the instance space, while overall patterns in algorithm performance are preserved. By combining the landscape features of the problems with the performance data, we pose the question of whether these features are as useful for algorithm selection as previous studies suggested. MA-BBOB is built on the publicly available IOHprofiler platform, which facilitates standardized experimentation routines, provides access to the interactive IOHanalyzer module for performance analysis and visualization, and enables comparisons with the rich and growing data collection available for the (MA-)BBOB functions.