Bag of Baselines

Bag of Baselines implements several multi-objective opimisation methods to create a performance benchmark on two small datasets. To learn more about this work, check out the publication.

Run the example code

Follow these steps to run Python scripts in the examples directory.

# Install Poetry if not exists
# curl -sSL https://install.python-poetry.org | python3 -

# Activate the virtual environment
poetry shell

# Install the dependencies
poetry install

# Run the code
cd examples
python random_search.py

Methods

The following methods are proposed and implemented:

1. SH-EMOA: Speeding up Evolutionary Multi-Objective Algorithms

2. MO-BOHB: Generalization of BOHB to an Arbitrary Number of Objectives

3. MS-EHVI: Mixed Surrogate Expected Hypervolume Improvement

4. MO-BANANAS

5. BULK & CUT

Datasets

Performance of the methods was evaluated using the following datasets: Oxford-Flowers dataset and Fashion-MNIST.

Organization

• The specific code for each of the methods (the main logic of each algorithm) is stored in the methods folder.

• In the examples folder you will find a small Python script to run each of the available methods (for the "Fashion-MNIST" or the "flowers" dataset).

• Code defining the search space and the evaluation function of the two different problems are defined in the problems folder.