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MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts

https://doi.org/10.1145/3673908
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-72699-7_2openalex-snapshot
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
inferredcites →https://doi.org/10.1007/978-3-031-14714-2_4openalex-snapshot
Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features
inferredcites →https://doi.org/10.1007/978-3-031-14714-2_41openalex-snapshot
Increasing the Diversity of Benchmark Function Sets Through Affine Recombination
inferredcites →https://doi.org/10.1007/978-3-031-30229-9_19openalex-snapshot
RF+clust for Leave-One-Problem-Out Performance Prediction
inferredcites →https://doi.org/10.1007/978-3-031-30229-9_25openalex-snapshot
BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances
inferredcites →https://doi.org/10.1007/978-3-031-30229-9_27openalex-snapshot
Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features
inferredcites →https://doi.org/10.1007/978-94-015-8330-5_4openalex-snapshot
A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation
inferredcites →https://doi.org/10.1007/s11047-021-09847-1openalex-snapshot
Analyzing randomness effects on the reliability of exploratory landscape analysis
inferredcites →https://doi.org/10.1016/j.asoc.2020.106138openalex-snapshot
Understanding the problem space in single-objective numerical optimization using exploratory landscape analysis
inferredcites →https://doi.org/10.1016/j.ins.2015.05.010openalex-snapshot
Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges
inferredcites →https://doi.org/10.1023/a:1008202821328openalex-snapshot
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
inferredcites →https://doi.org/10.1038/s42256-022-00579-0openalex-snapshot
A critical problem in benchmarking and analysis of evolutionary computation methods
inferredcites →https://doi.org/10.1080/10556788.2020.1808977openalex-snapshot
COCO: a platform for comparing continuous optimizers in a black-box setting
inferredcites →https://doi.org/10.1109/tai.2020.3022339openalex-snapshot
A Recommender System for Metaheuristic Algorithms for Continuous Optimization Based on Deep Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/tevc.2024.3462758openalex-snapshot
Using the Empirical Attainment Function for Analyzing Single-Objective Black-Box Optimization Algorithms
inferredcites →https://doi.org/10.1145/1389095.1389252openalex-snapshot
A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes
inferredcites →https://doi.org/10.1145/2330163.2330209openalex-snapshot
Algorithm selection based on exploratory landscape analysis and cost-sensitive learning
inferredcites →https://doi.org/10.1145/3319619.3322051openalex-snapshot
Limitations of benchmark sets and landscape features for algorithm selection and performance prediction
inferredcites →https://doi.org/10.1145/3319619.3326913openalex-snapshot
Expressiveness and robustness of landscape features
inferredcites →https://doi.org/10.1145/3447929.3447930openalex-snapshot
A SIGEVO impact award for a paper arising from the COCO platform
inferredcites →https://doi.org/10.1145/3449726.3463167openalex-snapshot
Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules
inferredcites →https://doi.org/10.1145/3510426openalex-snapshot
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
inferredcites →https://doi.org/10.1145/3512290.3528809openalex-snapshot
SELECTOR
inferredcites →https://doi.org/10.1145/3572895openalex-snapshot
Instance Space Analysis for Algorithm Testing: Methodology and Software Tools
inferredcites →https://doi.org/10.1145/3583131.3590412openalex-snapshot
Using Affine Combinations of BBOB Problems for Performance Assessment
inferredcites →https://doi.org/10.1145/3583131.3590417openalex-snapshot
Modular Differential Evolution
inferredcites →https://doi.org/10.1162/106365601750190398openalex-snapshot
Completely Derandomized Self-Adaptation in Evolution Strategies
inferredcites →https://doi.org/10.1162/evco_a_00236openalex-snapshot
Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning
inferredcites →https://doi.org/10.1162/evco_a_00242openalex-snapshot
Automated Algorithm Selection: Survey and Perspectives
inferredcites →https://doi.org/10.1162/evco_a_00262openalex-snapshot
Generating New Space-Filling Test Instances for Continuous Black-Box Optimization
inferredcites →https://doi.org/10.1162/evco_a_00341openalex-snapshot
Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python
inferredcites →https://doi.org/10.5281/zenodo.10376911openalex-snapshot
MA-BBOB - Reproducibility and Additional Data
inferredcites →https://doi.org/10.5281/zenodo.7826035openalex-snapshot
Many-Affine BBOB Function Combinations - Data and Figures

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-70068-2_3openalex-snapshot
A Deep Dive Into Effects of Structural Bias on CMA-ES Performance Along Affine Trajectories
inferred← citeshttps://doi.org/10.1007/978-3-031-70068-2_6openalex-snapshot
Landscape-Aware Automated Algorithm Configuration Using Multi-output Mixed Regression and Classification
inferred← citeshttps://doi.org/10.1007/978-3-031-70068-2_9openalex-snapshot
Learned Features vs. Classical ELA on Affine BBOB Functions
inferred← citeshttps://doi.org/10.1007/978-3-031-75623-8_29openalex-snapshot
Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection
inferred← citeshttps://doi.org/10.1007/978-3-032-23607-4_12openalex-snapshot
LLM Driven Design of Continuous Optimization Problems with Controllable High-Level Properties
inferred← citeshttps://doi.org/10.1007/978-3-032-36214-8_30openalex-snapshot
A Generator for Creating Streaming Continuous Optimisation Benchmarks
inferred← citeshttps://doi.org/10.1007/978-3-032-36217-9_7openalex-snapshot
On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization
inferred← citeshttps://doi.org/10.1016/j.swevo.2026.102288openalex-snapshot
A survey of features used for representing black-box single-objective continuous optimization
inferred← citeshttps://doi.org/10.1080/0305215x.2025.2544215openalex-snapshot
Leveraging large language models for the automatic implementation of problems in optimization frameworks
inferred← citeshttps://doi.org/10.1109/cec65147.2025.11042974openalex-snapshot
Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes
inferred← citeshttps://doi.org/10.1109/cec65147.2025.11043120openalex-snapshot
Improving Controllability of Chaotic Landscape Generators by Property Evolution
inferred← citeshttps://doi.org/10.1109/tcyb.2025.3625095openalex-snapshot
Two Novel Instance Selection Methods Combining Algorithm Performance and Landscape Analysis: A Comparative Study in Continuous Optimization
inferred← citeshttps://doi.org/10.1109/tevc.2025.3568053openalex-snapshot
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization
inferred← citeshttps://doi.org/10.1145/3712255.3734347openalex-snapshot
BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics
inferred← citeshttps://doi.org/10.1145/3712255.3735092openalex-snapshot
Neighborhood Adaptive Differential Evolution
inferred← citeshttps://doi.org/10.1145/3795095.3805073openalex-snapshot
Constructing Streams of Optimization Instances for Benchmarking Algorithm Selection and Configuration Approaches in Streaming Scenarios
inferred← citeshttps://doi.org/10.1145/3795095.3805094openalex-snapshot
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
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