Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression

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@Misc{DBLP:journals/corr/abs-2308-00672,

• author = "Nathan Haut and Wolfgang Banzhaf and Bill Punch",
• title = "Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression",
• howpublished = "arXiv",
• volume = "abs/2308.00672",
• year = "2023",
• month = "31 " # jul,
• keywords = "genetic algorithms, genetic programming",
• URL = "https://doi.org/10.48550/arXiv.2308.00672",
• DOI = "doi:10.48550/ARXIV.2308.00672",
• eprinttype = "arXiv",
• eprint = "2308.00672",
• timestamp = "Mon, 21 Aug 2023 01:00:00 +0200",
• biburl = "https://dblp.org/rec/journals/corr/abs-2308-00672.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• size = "26 pages",
• abstract = "we examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can be exploited to select informative training data points by using a model ensemble combined with an uncertainty metric. We explored several uncertainty metrics and found that differential entropy performed the best. We also compared two data diversity metrics and found that correlation asa diversity metric performs better than minimum Euclidean distance, although there are some drawbacks that prevent correlation from being used on all problems. Finally, we combined uncertainty and diversity using a Pareto optimisation approach to allow both to be considered in a balanced way to guide the selection of informative and unique data points for training.",
• notes = "See also \cite{Haut:ieeeTEC}",

}

Genetic Programming entries for Nathaniel Haut Wolfgang Banzhaf William F Punch

Citations