Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings
Genetic Programming and Evolvable Machines
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary system for generating investment strategies
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Linear Genetic Programming
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A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors. Both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit, where PAM DGP proved slightly more reactive to market changes than LGP. PAM DGP outperformed, or was competitive with, LGP for all stocks tested. Both implementations had very impressive accuracy in choosing both profitable buy trades and sells that prevented losses, where this occurred in the context of moderately active trading for all stocks. The algorithms also appropriately maintained maximal investment in order to profit from sustained market upswings.