ENERGIZE

ENERGIZE stands for Energy-efficient NeuroEvolution foR GeneralIZEd learning.

It is an adaptation of the EvoDENSS framework, itself drawing inspiration from Fast-DENSER.

Features

• feature1
• feature2

Installation

In order to run ENERGIZE, one needs to install the relevant dependencies. There are two ways to install the framework:

Conda

A conda environment can be created from an exported yml file that contains all the required dependences:

conda env create -f environment.yml

After the environment is created, just activate it in order to be able to run your code:

conda activate energize

pip

Alternatively, you can use the requirements.txt file, but you will be on your own to install cudatoolkit and other libraries that might be required to enable GPU acceleration.

pip install -r requirements.txt

Note: Installing ENERGIZE as a Python library is not yet supported

Getting Started

Example:

python3 -u -m energize.main \
    -d mnist \
    -c example/example_config.json \
    -g example/energize.grammar \
    --run 0 \
    --gpu-enabled

Command-line flags

• -c/--config-path: Sets the path to the config file to be used;
• -d/--dataset-name: Name of the dataset to be used. At the moment, mnist, fashion-mnist, cifar10 and cifar100 are supported.
• -g/--grammar-path: Sets the path to the grammar to be used;
• -r/--run: Identifies the run id and seed to be used;
• --gpu-enabled: When used, it enables GPU processing.

Documentation

Please visit our GitBook Documentation

Contact

• Gabriel Cortês (cortes@dei.uc.pt)
• Nuno Lourenço (naml@dei.uc.pt)
• Penousal Machado (machado@dei.uc.pt)

Publications

The methods used in these frameworks are mainly described in the following paper:

Cortês, G., Lourenço, N., Machado, P. (2024). Towards Physical Plausibility in Neuroevolution Systems. In: Smith, S., Correia, J., Cintrano, C. (eds) Applications of Evolutionary Computation. EvoApplications 2024. Lecture Notes in Computer Science, vol 14635. Springer, Cham. https://doi.org/10.1007/978-3-031-56855-8_5

Citations

If you benefit from this project or make use of its code, concepts, or materials, please consider citing the following references.

Cortês, G., Lourenço, N., & Machado, P. (2024). ENERGIZE (v1.0). Zenodo. https://doi.org/10.5281/zenodo.11220573