Enhancing gene expression programming based on space partition and jump for symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{LU:2021:IS,

• author = "Qiang Lu and Shuo Zhou and Fan Tao and Jake Luo and Zhiguang Wang",
• title = "Enhancing gene expression programming based on space partition and jump for symbolic regression",
• journal = "Information Sciences",
• volume = "547",
• pages = "553--567",
• year = "2021",
• ISSN = "0020-0255",
• DOI = "doi:10.1016/j.ins.2020.08.061",
• URL = "https://www.sciencedirect.com/science/article/pii/S0020025520308276",
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Gene expression programming, Multi-armed bandit, Evolutionary computation",
• abstract = "When solving a symbolic regression problem, the gene expression programming (GEP) algorithm could fall into a premature convergence which terminates the optimization process too early, and may only reach a poor local optimum. To address the premature convergence problem of GEP, we propose a novel algorithm named SPJ-GEP, which can maintain the GEP population diversity and improve the accuracy of the GEP search by allowing the population to jump efficiently between segmented subspaces. SPJ-GEP first divides the space of mathematical expressions into k subspaces that are mutually exclusive. It then creates a subspace selection method that combines the multi-armed bandit and the a-greedy strategy to choose a jump subspace. In this way, the analysis is made on the population diversity and the range of the number of subspaces. The analysis results show that SPJ-GEP does not significantly increase the computational complexity of time and space than classical GEP methods. Besides, an evaluation is conducted on a set of standard SR benchmarks. The evaluation results show that the proposed SPJ-GEP keeps a higher population diversity and has an enhanced accuracy compared with three baseline GEP methods",

}

Genetic Programming entries for Qiang Lu Shuo Zhou Fan Tao Jake Luo Zhiguang Wang

Citations