TensorGP -- Genetic Programming Engine in TensorFlow

2021-03-12 · Francisco Baeta, João Correia, Tiago Martins, Penousal Machado

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.

📄 PDF Abstract BibTeX arXiv:2103.07512

Code (1)

AwardOfSky/TensorGP 공식 구현 tf

Speed Benchmarking of Genetic Programming Frameworks

2021-05-25 · Francisco Baeta, João Correia, Tiago Martins, Penousal Machado

Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design. While, over the years, many techniques have been developed to mitigate this issue, data vectorization, in particul…

Benchmarking

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2021-10-15 · Vimarsh Sathia, Venkataramana Ganesh, Shankara Rao Thejaswi Nanditale

Genetic Programming (GP), an evolutionary learning technique, has multiple applications in machine learning such as curve fitting, data modelling, feature selection, classification etc. GP has several inherent parallel s…

Binary Classification feature selection GPU regression+1

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Neural networks can be regarded as a new programming paradigm, i.e., instead of building ever-more complex programs through (often informal) logical reasoning in the programmers' mind, complex 'AI' systems are built by o…

Logical Reasoning valid

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2017-08-30 · Juncai Xu, Zhenzhong Shen, Qingwen Ren, Xin Xie 외

Genetic programming has been widely used in the engineering field. Compared with the conventional genetic programming and artificial neural network, geometric semantic genetic programming (GSGP) is superior in astringenc…

General Classification regression

Improving a Parallel C++ Intel AVX-512 SIMD Linear Genetic Programming Interpreter

2025-12-09 · William B. Langdon arxiv

We extend recent 256 SSE vector work to 512 AVX giving a four fold speedup. We use MAGPIE (Machine Automated General Performance Improvement via Evolution of software) to speedup a C++ linear genetic programming interpre…