Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Genetic Programming in Computational Finance
On the genetic programming of time-series predictors for supply chain management
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Adaptive strategies for predicting bidding prices in supply chain management
Proceedings of the 10th international conference on Electronic commerce
Evolving stochastic processes using feature tests and genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Optimization of the trading rule in foreign exchange using genetic algorithm
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Interday foreign exchange trading using linear genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Learning approaches for developing successful seller strategies in dynamic supply chain management
Information Sciences: an International Journal
The evolution of higher-level biochemical reaction models
Genetic Programming and Evolvable Machines
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This paper describes an extension of the traditional application of Genetic Programming in the domain of the prediction of daily currency exchange rates. In combination with trigonometric operators, we introduce a new set of high-order statistical functions in a unique representation and analyze each system performance using daily returns of the British Pound and Japanese Yen. We will demonstrate that the introduction of high-order statistical functions in combination with trigonometric functions will outperform other traditional models such as Genetic Programming with the basic function set and ARMA models. Performance will be measured on hit percentage, average percentage change, and profit.