In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
Software Technology Research
|
App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 4
1158-1166

Neural network operator—novel genetic programming approach for symbolic regression

Chen Boa
Dang Longzhenga
Chen Guohongb
a. College of Computer & Data Science, b. School of Economics & Management, Fuzhou University, Fuzhou 350108, China
DOI: 10.19734/j.issn.1001-3695.2024.09.0337

Abstract

Genetic programming methods in symbolic regression suffer from a lack of direction in random search within expression space and inefficiency due to not utilizing data features during population evolution. This paper proposed a novel mutation operator called the neural network operator to address these problems. This operator used recurrent neural networks to learn features of a given dataset, optimized expressions in the population, guided the population to evolve towards lower error, and improved evolutionary efficiency. Experimental results show that the genetic programming method combined with the neural network operator outperforms the original method in both formula recovery rate and population evolution speed, and achieves a high coefficient of determination on macroeconomic datasets. The conclusion demonstrates that the neural network operator can effectively guide the genetic programming population to perform feature-oriented search, significantly improve evolutionary efficiency, and have potential for practical applications.

Foundation Support

国家自然科学基金资助项目(70973022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0337
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 4
Section: Software Technology Research
Pages: 1158-1166
Serial Number: 1001-3695(2025)04-026-1158-09

Publish History

[2025-04-05] Printed Article

Cite This Article

陈勃, 党隆政, 陈国宏. 神经网络算子——一种面向符号回归问题的遗传编程新方法 [J]. 计算机应用研究, 2025, 42 (4): 1158-1166. (Chen Bo, Dang Longzheng, Chen Guohong. Neural network operator—novel genetic programming approach for symbolic regression [J]. Application Research of Computers, 2025, 42 (4): 1158-1166. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)