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