HACKB
Online judge for black-box optimization
Implement algorithms against hidden objective functions and compete to find the best solution within a limited evaluation budget. Try anything from CMA-ES and Bayesian optimization to your own regression models.
What is black-box optimization?
Finding a good x for a function f(x) whose internals (formula, gradient) are unknown — using only a limited number of evaluations.
You only see y. The function is a black box.
The internals are hidden
No formula, no gradients — only the scalar y = f(x) is returned for each input x.
Each evaluation is expensive
A single evaluation can be a real experiment, a simulation, or a model training run. You must search wisely under a strict budget.
Found everywhere in practice
Hyperparameter tuning, materials and drug discovery, engineering design — black-box optimization shows up across many real domains.
Optimization mode
Right now HACKB ships a single Optimization mode. We plan to introduce additional evaluation modes in the future.
Optimization mode
Find the x that minimizes f(x) within a fixed evaluation budget
Call the worker-provided evaluate(x) and search for the best point with any algorithm of your choice. The number of evaluate() calls per submission is capped per problem.
def solve(problem, evaluate):
...How it works
From signup to scoring in four steps.
- 1
Create an account
Sign up with your email and a password.
- 2
Pick a contest and a problem
Open a problem from a public contest and review its details.
- 3
Write submission.py
Implement a Python function for each problem and submit it.
- 4
See results and standings
Check your judge results and ranking. Other participants' submissions are also visible.