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- W2912333243 abstract "Abstract We present a novel approach based on statistical permutation tests for pruning redundant subtrees from genetic programming (GP) trees that allows us to explore the extent of effective redundancy . We observe that over a range of regression problems, median tree sizes are reduced by around 20% largely independent of test function, and that while some large subtrees are removed, the median pruned subtree comprises just three nodes; most take the form of an exact algebraic simplification. Our statistically-based pruning technique has allowed us to explore the hypothesis that a given subtree can be replaced with a constant if this substitution results in no statistical change to the behavior of the parent tree—what we term approximate simplification. In the eventuality, we infer that more than 95% of the accepted pruning proposals are the result of algebraic simplifications, which provides some practical insight into the scope of removing redundancies in GP trees." @default.
- W2912333243 created "2019-02-21" @default.
- W2912333243 creator A5089203316 @default.
- W2912333243 date "2020-04-20" @default.
- W2912333243 modified "2023-09-27" @default.
- W2912333243 title "Pruning of genetic programming trees using permutation tests" @default.
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- W2912333243 doi "https://doi.org/10.1007/s12065-020-00379-8" @default.
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