Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@InProceedings{agapitos_etal:ppsn2010,
author = "Alexandros Agapitos and Michael O'Neill and
Anthony Brabazon",
title = "Evolutionary Learning of Technical Trading Rules
without Data-mining Bias",
booktitle = "PPSN 2010 11th International Conference on Parallel
Problem Solving From Nature",
pages = "294--303",
year = "2010",
volume = "6238",
editor = "Robert Schaefer and Carlos Cotta and
Joanna Kolodziej and Guenter Rudolph",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
isbn13 = "978-3-642-15843-8",
address = "Krakow, Poland",
month = "11-15 " # sep,
keywords = "genetic algorithms, genetic programming",
doi = "
doi:10.1007/978-3-642-15844-5_30",
abstract = "In this paper we investigate the profitability of
evolved technical trading rules when controlling for
data-mining bias. For the first time in the
evolutionary computation literature, a comprehensive
test for a rule's statistical significance using
Hansen's Superior Predictive Ability is explicitly
taken into account in the fitness function, and
multi-objective evolutionary optimisation is employed
to drive the search towards individual rules with
better generalisation abilities. Empirical results on a
spot foreign-exchange market index suggest that
increased out-of-sample performance can be obtained
after accounting for data-mining bias effects in a
multi-objective fitness function, as compared to a
single-criterion fitness measure that considers solely
the average return.",
}
Genetic Programming entries for Alexandros Agapitos Michael O'Neill Anthony Brabazon