Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
The GA-P: A Genetic Algorithm and Genetic Programming Hybrid
IEEE Expert: Intelligent Systems and Their Applications
Generating trading rules on the stock markets with genetic programming
Computers and Operations Research
A generalized model for financial time series representation and prediction
Applied Intelligence
Soft computing techniques applied to finance
Applied Intelligence
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Information Systems Frontiers
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The principle objective of this paper is to obtain trading rules with a low risk level which are also capable of obtaining high returns. To that purpose a methodology has been defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. In GAP a chromosome is composed of a tree with language operators and a vector with numeric values. The GAP algorithm implements the automatic search for trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. In order to diminish high over-fitting, a technique of incremental training has been used. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. Insert your abstract here. Include keywords, PACS and mathematical subject classification numbers as needed.