EvoTorch: Scalable Evolutionary Computation in Python
Created by W.Langdon from
gp-bibliography.bib Revision:1.8772
- @Misc{evotorch2023arxiv,
-
author = "Nihat Engin Toklu and Timothy Atkinson and
Vojtech Micka and Pawel Liskowski and Rupesh Kumar Srivastava",
-
title = "{EvoTorch}: Scalable Evolutionary Computation in
Python",
-
howpublished = "arXiv 2302.12600",
-
year = "2023",
-
month = "21 " # may,
-
keywords = "genetic algorithms, genetic programming, GPU, NSGA-II,
Gym, Brax",
-
primaryclass = "cs.NE",
-
URL = "
https://arxiv.org/abs/2302.12600",
-
code_url = "
https://github.com/nnaisense/evotorch",
-
code_url = "
https://docs.evotorch.ai/latest/examples/notebooks/Genetic_Programming/",
-
size = "25 pages",
-
abstract = "Evolutionary computation is an important component
within various fields such as artificial intelligence
research, reinforcement learning, robotics, industrial
automation and/or optimization, engineering design,
etc. Considering the increasing computational demands
and the dimensionalities of modern optimization
problems, the requirement for scalable, re-usable, and
practical evolutionary algorithm implementations has
been growing. To address this requirement, we present
EvoTorch: an evolutionary computation library designed
to work with high-dimensional optimization problems,
with GPU support and with high parallelization
capabilities. EvoTorch is based on and seamlessly works
with the PyTorch library, and therefore, allows the
users to define their optimization problems using a
well-known API",
-
notes = "Also known as
\cite{toklu2023evotorchscalableevolutionarycomputation}",
- }
Genetic Programming entries for
Nihat Engin Toklu
Timothy Atkinson
Vojtech Micka
Pawel Liskowski
Rupesh Kumar Srivastava
Citations