Genetic Programming using Cooperative Coevolution and Problem Decomposition for Solving Large-scale Symbolic Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Sopov:2021:InfoTech,

• author = "Evgenii Sopov and Mariia Semenkina",
• title = "Genetic Programming using Cooperative Coevolution and Problem Decomposition for Solving Large-scale Symbolic Regression Problems",
• booktitle = "2021 International Conference on Information Technologies (InfoTech)",
• year = "2021",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/InfoTech52438.2021.9548435",
• month = sep,
• notes = "Also known as \cite{9548435}",

}

Genetic Programming entries for Evgenii Sopov Mariia Semenkina

Citations