Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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
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
Soft computing decision support for a steel sheet incremental cold shaping process
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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In previous works a methodology was 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 The GAP algorithm implements the automatic search for crisp trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk 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 This work probes that the proposed methodology is valid for different assets in a different market than previous work.