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Objective function design in a grammatical evolutionary trading system

Author(s)
Bradley, Robert  
Brabazon, Anthony  
O'Neill, Michael  
Uri
http://hdl.handle.net/10197/2740
Date Issued
2010-07
Date Available
2011-01-21T15:10:31Z
Abstract
Designing a suitable objective function is an essential step in successfully applying an evolutionary algorithm to a problem. In this study we apply a grammar-based Genetic Programming algorithm called Grammatical Evolution to the problem of trading model induction and carry out a number of experiments to assess the effect of objective function design on the trading characteristics of the evolved strategies. The paper concludes with in and out-of-sample results, and indicates a number of avenues of future work.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE Press
Copyright (Published Version)
2010 IEEE
Subjects

Trading systems

Objective function de...

Grammatical evolution...

Subject – LCSH
Evolutionary computation
Genetic algorithms
Genetic programming (Computer science)
Stocks
DOI
10.1109/CEC.2010.5586020
Web versions
http://dx.doi.org/10.1109/CEC.2010.5586020
Language
English
Status of Item
Peer reviewed
Journal
2010 IEEE Congress on Evolutionary Computation (CEC) [proceedings]
Conference Details
Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July
ISBN
978-1-4244-6909-3
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
File(s)
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Name

Bradley2010a.pdf

Size

1.18 MB

Format

Adobe PDF

Checksum (MD5)

f799b7c2b9f1f88c8766eea1e230f25b

Owning collection
FMC² Research Collection
Mapped collections
Business Research Collection•
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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