Skip to main content

Financial Data Management System Based on Genetic Algorithm

  • Conference paper
  • First Online:
Frontier Computing (FC 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1031))

Included in the following conference series:

  • 12 Accesses

Abstract

With the rapid development of CS and T and NT, great changes have taken place in the technology of financial MS. The traditional financial management architecture can not adapt to the distribution of management units and the large capacity of management data in the current information MS. This paper discusses the principle of GA, analyzes the key problems in the application of GA and the improved GA, summarizes the basic functional requirements of FD MS and the security requirements of FD analysis MS, and tests the FD analysis MS. The results show that the background data of the S runs well, the whole process S works normally, and the test results are consistent with the expected use case results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hagan, E., Amoah, A., Kuada, J.: Foreign direct investment and economic growth nexus in Africa: new evidence from the new financial fragility measure. Afr. J. Econ. Manag. Stud. 11(1), 1–17 (2019)

    Google Scholar 

  2. Wali, S., Masmoudi, S.M.: Internal control and real earnings management in the French context. J. Fin. Rep. Acc. 18(2), 363–387 (2020)

    Article  Google Scholar 

  3. Rafindadi, A.A., Olanrewaju, Z.A.: The impact of internal control system on the financial accountability of non-governmental organisations in Nigeria: evidence from the structural equation modelling. Int. Rev. Manag. Mark. 9(3), 49–63 (2019)

    Google Scholar 

  4. Rozental, O., White, R.S.: Anesthesia information management systems: evolution of the paper anesthetic record to a multisystem electronic medical record network that streamlines perioperative care - sciencedirect. J. Anesth. Hist. 5(3), 93–98 (2019)

    Article  Google Scholar 

  5. Shakibaei, E.: Role of a hospital accreditation program in developing a process management system: a qualitative study. Int. J. Health Care Qual. Assur. 32(1), 00 (2019)

    Article  Google Scholar 

  6. Dawid, H., Kopel, M.: on economic applications of the genetic algorithm: a model of the cobweb type. J. Evol. Econ. 8(3), 297–315 (2019)

    Article  MATH  Google Scholar 

  7. Wang, Y., Su, Y.Q., Hensen, E., et al.: Finite-temperature structures of supported subnanometer catalysts inferred via statistical learning and genetic algorithm-based optimization. ACS Nano 14(10), 13995–14007 (2020)

    Article  Google Scholar 

  8. Duan, L., et al. Real-time patient-specific ECG arrhythmia detection by quantum genetic algorithm of least squares twin SVM. J. Beijing Inst. Technol. 29(103), 32–40 (2020)

    Google Scholar 

  9. Hraiech, S.E., Chebbi, A.H., Affi, Z., et al.: Genetic algorithm coupled with the Krawczyk method for multi-objective design parameters optimization of the 3-UPU manipulator. Robotica 38(6), 1138–1154 (2020)

    Article  Google Scholar 

  10. Liu, D., Li, H., Wu, L., et al.: Time-to-event supervised genetic algorithm enables induction chemotherapy decision making for nasopharyngeal carcinoma. IEEE Access (99),1–1 (2021)

    Google Scholar 

  11. Zhao, X., Huang, G., Mousoli, R.: A multi-threading solution to multimedia traffic in NIDS based on hybrid genetic algorithm. Int. J. Netw. Secur. 22(3), 427–436 (2020)

    Google Scholar 

  12. Na, A., Fa, A., Dsc, C., et al.: Development and validation of a hybrid aerodynamic design method for curved diffusers using genetic algorithm and ball-spine inverse design method. Alex. Eng. J. 60(3), 3021–3036 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sheng Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, H., Li, S. (2023). Financial Data Management System Based on Genetic Algorithm. In: Hung, J.C., Yen, N.Y., Chang, JW. (eds) Frontier Computing. FC 2022. Lecture Notes in Electrical Engineering, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-99-1428-9_46

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-1428-9_46

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1427-2

  • Online ISBN: 978-981-99-1428-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics