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- W4287271145 abstract "In this paper, we resort to the TensorFlow framework to investigate the benefits of applying data vectorization and fitness caching methods to domain evaluation in Genetic Programming. For this purpose, an independent engine was developed, TensorGP, along with a testing suite to extract comparative timing results across different architectures and amongst both iterative and vectorized approaches. Our performance benchmarks demonstrate that by exploiting the TensorFlow eager execution model, performance gains of up to two orders of magnitude can be achieved on a parallel approach running on dedicated hardware when compared to a standard iterative approach." @default.
- W4287271145 created "2022-07-25" @default.
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- W4287271145 date "2021-03-12" @default.
- W4287271145 modified "2023-09-24" @default.
- W4287271145 title "TensorGP -- Genetic Programming Engine in TensorFlow" @default.
- W4287271145 doi "https://doi.org/10.48550/arxiv.2103.07512" @default.
- W4287271145 hasPublicationYear "2021" @default.
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