Run-time Reconfigurable Acceleration for Genetic Programming Fitness Evaluation in Trading Strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Funie2018,

• author = "Andreea-Ingrid Funie and Paul Grigoras and Pavel Burovskiy and Wayne Luk and Mark Salmon",
• title = "Run-time Reconfigurable Acceleration for Genetic Programming Fitness Evaluation in Trading Strategies",
• journal = "Journal of Signal Processing Systems",
• year = "2018",
• volume = "90",
• number = "1",
• pages = "39--52",
• month = "1 " # jan,
• keywords = "genetic algorithms, genetic programming, Fitness evaluation, High-frequency trading, Run-time reconfiguration",
• ISSN = "1939-8115",
• URL = "http://hdl.handle.net/10044/1/52831",
• URL = "https://spiral.imperial.ac.uk/bitstream/10044/1/52831/5/s11265-017-1244-8.pdf",
• DOI = "doi:10.1007/s11265-017-1244-8",
• URL = "https://rdcu.be/dnO7k",
• size = "14 pages",
• abstract = "Genetic programming can be used to identify complex patterns in financial markets which may lead to more advanced trading strategies. However, the computationally intensive nature of genetic programming makes it difficult to apply to real world problems, particularly in real-time constrained scenarios. In this work we propose the use of Field Programmable Gate Array technology to accelerate the fitness evaluation step, one of the most computationally demanding operations in genetic programming. We propose to develop a fully-pipelined, mixed precision design using run-time reconfiguration to accelerate fitness evaluation. We show that run-time reconfiguration can reduce resource consumption by a factor of 2 compared to previous solutions on certain configurations. The proposed design is up to 22 times faster than an optimised, multi-threaded software implementation while achieving comparable financial returns.",
• notes = "See also \cite{Cross-AI-2018-PhD-Thesis}",

}

Genetic Programming entries for Andreea Ingrid Cross Paul Grigoras Pavel Burovskiy Wayne Luk Mark Salmon

Citations