Evaluates a function from the IOHexperimenter PBO (Pseudo-Boolean Optimization) problem class — a suite of 25 test functions defined on {0, 1}^n. All problems are maximization problems.

1. Install uv if you don't have it yet:

pip install uv

No extra setup is needed beyond having uv installed. The ioh package is resolved automatically.

Note: This snippet requires Python 3.10. The inline metadata enforces this via requires-python = "==3.10". Make sure a Python 3.10 interpreter is available on your system.

uv run call_pbo.py

The script creates PBO problem 1 (OneMax, instance 1) in 16 dimensions, evaluates it at the all-zeros bitstring, and prints the result. You can adjust the behavior by editing these variables in the script:

• Problem ID — the first argument to ioh.get_problem() selects which of the 25 functions to load (default: 1)
• dim — problem dimensionality, i.e. bitstring length (default: 16)
• instance — problem instance; controls transformations such as objective scaling (default: 1)
• eval_point — the bitstring at which the function is evaluated (default: all zeros)

Note: PBO problems take integer inputs in {0, 1}^n (not floats). The evaluation point should be a list of 0s and 1s.

• PBO problem descriptions
• PBO class documentation
• PBO source code
• IOHexperimenter GitHub repository