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An improved version of Turbo algorithm for the Black-box optimization (BBO) competition organized by NeurIPS 2020

This implemention is our solution for the BBO competition organized by NeurIPS 2020 conference:

Final results

Our algorithm ranked the 4th place on the “warm start friendly” leaderboard: It also ranked 17th place on the final leaderboard:

Result on “warm start friendly” leaderboard


Main contributions

  1. Reduce the bounds of hyper-parameters of each machine learning classifier
  2. Improve the diversity in selecting batch elements
  3. Increase the number of explored trust regions by using a base-length decay


This implemention is based on Turbo algorithm. Please find the required packages from the Turbo's Github:

How to run

This implemention is developed for optimizing machine learning models in the BBO competition. Please find the instruction from the competition Github:


This implemention is released under Apache License 2.0 license except for the code derived from Turbo.