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EvoTorch: Scalable Evolutionary Computation in Python

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arxiv 2302.12600 v3 pith:YX5DMTHF submitted 2023-02-24 cs.NE cs.AI

classification cs.NEcs.AI
keywords evolutionaryoptimizationcomputationevotorchproblemslibraryrequirementscalable
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network

    cs.NE 2024-12 conditional novelty 5.0 of 10

    GAPA is a GPU acceleration framework that turns the genetic operators used in graph perturbation optimization into matrix operations and demonstrates large speedups in benchmarking.

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