Pruning of genetic programming trees using permutation tests

Created by W.Langdon from gp-bibliography.bib Revision:1.8194

@Article{Rockett:2020:EI,

• author = "Peter Rockett",
• title = "Pruning of genetic programming trees using permutation tests",
• journal = "Evolutionary Intelligence",
• year = "2020",
• volume = "13",
• number = "4",
• pages = "649--661",
• keywords = "genetic algorithms, genetic programming, Permutation testing, Tree pruning",
• URL = "https://rdcu.be/cU470",
• DOI = "doi:10.1007/s12065-020-00379-8",
• 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 20percent 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 95percent 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.",

}

Genetic Programming entries for Peter I Rockett

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