mlsanityData to 7 Oct 2026

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics

arXiv 2111.04077 ↗First posted 7 Nov 2021cs.NEcited by 24

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Abstract (arXiv)

We present IOHexperimenter, the experimentation module of the IOHprofiler project, which aims at providing an easy-to-use and highly customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, Bayesian optimization techniques, etc. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other components of IOHprofiler such as IOHanalyzer, the module for interactive performance analysis and visualization. IOHexperimenter provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. These logs are fully compatible with existing tools for interactive data analysis, which significantly speeds up the deployment of a benchmarking pipeline. The main components of IOHexperimenter are the environment to build customized problem suites and the various logging options that allow users to steer the granularity of the data records.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2021.

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Citing papers per year: 2022: 1; 2023: 4; 2024: 7; 2025: 10; 2026: 2.

24 papers.

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  1. 2024LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristicscited by 92
  2. 2024A Systematic Survey on Large Language Models for Algorithm Designcited by 65
  3. 2023MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learningcited by 35
  4. 2024In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristicscited by 28
  5. 2025LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithmscited by 10
  6. 2023To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Featurescited by 9
  7. 2023MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contextscited by 7
  8. 2025Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systemscited by 6
  9. 2024Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarkscited by 4
  10. 2025Behaviour Space Analysis of LLM-driven Meta-heuristic Discoverycited by 4
  11. 2022The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyondcited by 1
  12. 2023Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfilercited by 1
12 more
  1. 2025Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarkscited by 1
  2. 2025Bencher: Simple and Reproducible Benchmarking for Black-Box Optimizationcited by 1
  3. 2025Comparing Optimization Algorithms Through the Lens of Search Behavior Analysiscited by 1
  4. 2024Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysiscited by 0
  5. 2024A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectoriescited by 0
  6. 2024A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severitycited by 0
  7. 2025Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformationscited by 0
  8. 2025Bayesian Optimization by Kernel Regression and Density-based Explorationcited by 0
  9. 2025SEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarkingcited by 0
  10. 2025From Performance to Understanding: A Vision for Explainable Automated Algorithm Designcited by 0
  11. 2026Does Dimensionality Reduction via Random Projections Preserve Landscape Features?cited by 0
  12. 2026On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimizationcited by 0