Abstract

Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions are produced that are robust to non-trivial changes in the environment? We explore two new approaches. The first approach uses subsets of extreme environments during training and the second approach uses a voting committee of GP individuals with differing phenotypic behaviour.

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