DOTS-Benchmark

DOTS-Benchmark is a suite containing benchmark functions and algorithms for optimisation, as introduced in Derivative-free stOchastic Tree Search.

Current results

Available algorithms

The currently available algorithms are:

• Derivative-free stOchastic Tree Search (DOTS, Wei et al., 2024)
• MCTS_Greedy
• MCTS_eGreedy
• Dual Annealing
• Differential Evolution
• CMA-ES

Please send us a PR to add your algorithm!

Available oracle functions

The currently available functions are:

• Ackley
• Rastrigin
• Rosenbrock
• Levy
• Schwefel
• Michalewicz
• Griewank

Installation

The code requires python>=3.9. Installation Tensorflow and Keras with CUDA support is stroongly recommended.

Install DOTS:

pip install git+https://github.com/poyentung/DOTS-Benchmark.git

or clone the repository to local devices:

git clone git@github.com:poyentung/DOTS-Benchmark.git
cd DOTS; pip install -e .

Quick start

Here we evaluate DOTS on Ackley in 10 dimensions for 1000 samples.

• Using exact oracle function:

python3 -m dots_benchmark.scripts.run_oracle\
        --func ackley\
        --dims 10\
        --samples 1000\
        --method DOTS

• Using neural network surrogate:

python3 -m dots_benchmark.scripts.run_surrogate\
        --func ackley\
        --dims 10\
        --samples 1000\
        --method DOTS

Running unit tests

License

The source code is released under the MIT license, as presented in here.