A Semantic-based Hoist Mutation Operator for Evolutionary Feature Construction in Regression [Hot off the Press]
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{zhang:2024:GECCOcomp,
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author = "Hengzhe Zhang and Qi Chen and Bing Xue and
Wolfgang Banzhaf and Mengjie Zhang",
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title = "A Semantic-based Hoist Mutation Operator for
Evolutionary Feature Construction in Regression [Hot
off the Press]",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference Companion",
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year = "2024",
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editor = "Marcus Gallagher",
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pages = "65--66",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, bloat
control, automated machine learning, evolutionary
feature construction",
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isbn13 = "979-8-4007-0495-6",
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DOI = "doi:10.1145/3638530.3664071",
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size = "2 pages",
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abstract = "This Hot-off-the-Press paper summarizes our recently
published work, {"}A Semantic-based Hoist Mutation
Operator for Evolutionary Feature Construction in
Regression{"} [9] published in IEEE Transactions on
Evolutionary Computation. Our study introduces a
semantic-based hoist mutation operator to control tree
bloat and reduce tree sizes in genetic programming (GP)
based evolutionary feature construction algorithms. The
proposed operator identifies the most informative
subtree with the largest cosine similarity to the
target semantics and then hoists the subtree to the
root as a new GP tree. This process reduces the tree
sizes without compromising learning capability. The
proposed operator is supported by the probably
approximately correct (PAC) learning theory, ensuring
that it does not degrade the generalization upper bound
of GP models. By employing a hashing-based redundancy
checking strategy, the proposed method outperforms
seven bloat control methods on 98 datasets in reducing
model size while maintaining test performance at the
same level. These findings demonstrate the capability
of using semantic hoist mutation for bloat control in
GP-based evolutionary feature construction.",
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notes = "GECCO-2024 A Recombination of the 33rd International
Conference on Genetic Algorithms (ICGA) and the 29th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Hengzhe Zhang
Qi Chen
Bing Xue
Wolfgang Banzhaf
Mengjie Zhang
Citations