- author = "Francisco Baeta and Joao Correia and Tiago Martins and Penousal Machado",
- title = "Speed Benchmarking of Genetic Programming Frameworks",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference",
- year = "2021",
- editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
- pages = "768--775",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Parallelisation, Vectorisation, TensorFlow, GPU Computing",
- isbn13 = "9781450383509",
-
DOI = "
doi:10.1145/3449639.3459335",
- size = "8 pages",
- abstract = "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 vectorisation, in particular, is arguably still the most attractive strategy due to the parallel nature of GP. In this work,we employ a series of benchmarks meant to compare both the performance and evolution capabilities of different vectorised and iterative implementation approaches across several existing frameworks. Namely, TensorGP, a novel open source engine written in Python, is shown to greatly benefit from the TensorFlow library to accelerate the domain evaluation phasein GP. The presented performance benchmarks demonstratethat the TensorGP engine manages to pull ahead, with relativespeedups above two orders of magnitude for problems with ahigher number of fitness cases. Additionally, as a consequenceof being able to compute larger domains, we argue that TensorGP performance gains aid the discovery of more accurate candidate solutions.",
-
notes = "University of Coimbra
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
