An Invariant Information Geometric Method for High-Dimensional Online Optimization

This is the official repository of paper 'An Invariant Information Geometric Method for High-Dimensional Online Optimization', including the source-code and full version of the work.

Except for the common modules (e.g., numpy, scipy), our source code depends on the following modules.

• Mandatory

  • PyPop7 (https://github.com/Evolutionary-Intelligence/pypop)
  • mujoco-py (https://github.com/openai/mujoco-py)
  • gym (https://github.com/openai/gym)

• Optional

  • Botorch (https://github.com/pytorch/botorch)

To run SynCMA as well as other evolutionary baselines, use the file exp.py. For example:

python exp.py --optimizer SynCMA --func ackley --dim 32 --eval_num 10000 --rep 100 --lam 2

To run TuRBO over our mentioned benchmarks, use bo_baseline.py inside the baseline folder:

cd baseline

python bo_baseline.py --func ackley --dim 32  --tr_num 1 --eval_num 5000 --repeat_num 30 --gpu_idx 0