FFX: Fast, Scalable, Deterministic Symbolic Regression Technology
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{McConaghy:2011:GPTP,
-
author = "Trent McConaghy",
-
title = "{FFX}: Fast, Scalable, Deterministic Symbolic
Regression Technology",
-
booktitle = "Genetic Programming Theory and Practice IX",
-
year = "2011",
-
editor = "Rick Riolo and Ekaterina Vladislavleva and
Jason H. Moore",
-
series = "Genetic and Evolutionary Computation",
-
address = "Ann Arbor, USA",
-
month = "12-14 " # may,
-
publisher = "Springer",
-
chapter = "13",
-
pages = "235--260",
-
keywords = "genetic algorithms, genetic programming, technology,
symbolic regression, pathwise, regularisation,
real-world problems, machine learning, lasso, ridge
regression, elastic net, integrated circuits",
-
isbn13 = "978-1-4614-1769-9",
-
URL = "http://trent.st/content/2011-GPTP-FFX-paper.pdf",
-
DOI = "doi:10.1007/978-1-4614-1770-5_13",
-
slides_url = "http://www.trent.st/content/2011-GPTP-FFX-slides.pdf",
-
size = "27 pages",
-
abstract = "Symbolic regression is a common application for
genetic programming (GP). we present a new
non-evolutionary technique for symbolic regression
that, compared to competent GP approaches on real-world
problems, is orders of magnitude faster (taking just
seconds), returns simpler models, has comparable or
better prediction on unseen data, and converges
reliably and deterministically. I dub the approach FFX,
for Fast Function Extraction. FFX uses a recently
developed machine learning technique, pathwise
regularised learning, to rapidly prune a huge set of
candidate basis functions down to compact models. FFX
is verified on a broad set of real-world problems
having 13 to 1468 input variables, out performing GP as
well as several state-of-the-art regression
techniques.",
-
notes = "part of \cite{Riolo:2011:GPTP}",
-
affiliation = "Solido Design Automation Inc., Saskatoon, Canada",
- }
Genetic Programming entries for
Trent McConaghy
Citations