Genetic Programming using Cooperative Coevolution and Problem Decomposition for Solving Large-scale Symbolic Regression Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Sopov:2021:InfoTech,
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author = "Evgenii Sopov and Mariia Semenkina",
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title = "Genetic Programming using Cooperative Coevolution and
Problem Decomposition for Solving Large-scale Symbolic
Regression Problems",
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booktitle = "2021 International Conference on Information
Technologies (InfoTech)",
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year = "2021",
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abstract = "Symbolic regression using genetic programming (SRGP)
is one of the most popular machine learning approaches
for building human-readable interpretable models. At
the same time, SRGP usually fails in solving
high-dimensional problems. Large-scale problems lead to
rapid bloating of trees and require special techniques
for preventing always-destructive genetic operations
and the loss of variables in trees. In the study, we
have proposed a novel approach, which performs random
decomposition of large-scale symbolic regression
problems into sub-problems with less number of
variables and uses cooperative coevolution for merging
sub-solutions at the fitness evaluation stage. We have
discussed the general conception and some alternative
realizations. The results of numerical experiments
using some large-scale artificial and real-world
benchmark problems have demonstrated that the proposed
approach outperforms the standard SRGP algorithm and
some of its variations.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/InfoTech52438.2021.9548435",
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month = sep,
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notes = "Also known as \cite{9548435}",
- }
Genetic Programming entries for
Evgenii Sopov
Mariia Semenkina
Citations