Published September 25, 2023 | Version v1
Dataset Open

Comparison of Low-budget Black-box Optimization Algorithms on BBOB

Authors/Creators

  • 1. Leiden University (LIACS)
  • 2. Université d'Antananarivo, Madagascar
  • 3. Meta AI Research
  • 4. Sorbonne Université (LIP6, CNRS)

Description

Data to replicate the results presented in the paper "Low-budget Black-box Optimization Algorithms in BBOB and OpenAI Gym", submitted to IEEE Transactions on Evolutionary Computation. Here, we offer the data comparing Black-Box Optimization tools for machine learning with more classical heuristics on the well-known BBOB benchmark suite from the COCO environment (24 noiseless functions with different landscape characteristics: uni/multi-modality, separability, good/weak global structure, etc.)

Files

Data_TEVC_2023_Zenodo_BBOB.zip

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