Run-time Reconfigurable Acceleration for Genetic Programming Fitness Evaluation in Trading Strategies
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @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",
-
URL = "https://rdcu.be/dnO7k",
-
DOI = "doi:10.1007/s11265-017-1244-8",
-
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