Bradley, RobertRobertBradleyBrabazon, AnthonyAnthonyBrabazonO'Neill, MichaelMichaelO'Neill2011-01-212011-01-212010 IEEE2010-07978-1-4244-6909-3http://hdl.handle.net/10197/2740Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 JulyDesigning 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.1241883 bytesapplication/pdfenPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Trading systemsObjective function designGrammatical evolutionEvolutionary computationGenetic algorithmsGenetic programming (Computer science)StocksConference Publication10.1109/CEC.2010.5586020https://creativecommons.org/licenses/by-nc-sa/1.0/