TensorGP -- Genetic Programming Engine in TensorFlow

• URL: http://arxiv.org/abs/2103.07512v1
• Date: Fri, 12 Mar 2021 20:19:37 GMT
• Title: TensorGP -- Genetic Programming Engine in TensorFlow
• Authors: Francisco Baeta, Jo\~ao Correia, Tiago Martins and Penousal Machado
• Abstract summary: We investigate the benefits of applying data vectorization and fitness caching methods to domain evaluation in Genetic Programming. Our performance benchmarks demonstrate that performance gains of up to two orders of magnitude can be achieved on a parallel approach running on dedicated hardware.
• Score: 1.1470070927586016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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