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Using Improved Genetic Algorithm to Solve the Equations

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Geo-informatics in Sustainable Ecosystem and Society (GSES 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 980))

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Abstract

The origins of traditional genetic algorithms are based on the selection of better biological individuals by selection of species. In this paper, the improved genetic algorithm is adopted to solve the problem of equations, and the optimized punch-wheel algorithm is used to reduce the redundancy and duplication of code, instead of the traditional bubbling sorting and array sorting. Through the calculation of the mathematical model, the genetic algorithm can better solve the problem of solving the equation, the reader can better understand the process of solving the equation. Function model solving based on genetic algorithm proves that genetic algorithm opens up new ideas for solving equations, which can make people better understand the process of solving equations and divergent the thinking of solving equations.

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References

  1. He, Y., Ma, C., Fan, B.: A new l-m method for solving nonlinear equations. J. Fujian Normal Univ. (Nat. Sci. Ed.) (02) (2014)

    Google Scholar 

  2. Li, C.: MPRP derivative free algorithm for symmetric nonlinear equations. J. Southwest Univ. (Nat. Sci. Ed.) 36(01), 67–71 (2014)

    Google Scholar 

  3. Ru, Q.: Existence and nonexistence of solutions for a class of nonlinear reaction-diffusion equations on Riemannian manifolds. Appl. Math. (04) (2013)

    Google Scholar 

  4. Jiang, L., Xu, N.: Scattering of nonlinear Schrodinger equations. Appl. Math. (02) (2013)

    Google Scholar 

  5. Pai, X., Wang, Z.: Application of genetic algorithm with gradient information in solving nonlinear equations. J. China Petrol. Univ. (Nat. Sci. Ed.) (03) (2009)

    Google Scholar 

  6. Zeng, Y.: Application of improved genetic algorithm in solving nonlinear equations. J. East China Jiaotong Univ. (04) (2004)

    Google Scholar 

  7. Hu, N., Pan, Q.: Genetic algorithm for solving multiple equations. J. Jingzhou Normal Univ. (02) (2002)

    Google Scholar 

  8. Tsoulos, I.G.: Modifications of real code genetic algorithm for global optimization. Appl. Math. Comput. 203(2), 598–607 (2008)

    MathSciNet  MATH  Google Scholar 

  9. Kaelo, P., Ali, M.M.: Integrated crossover rules in real coded genetic algorithms. Eur. J. Oper. Res. (1) (2005)

    Google Scholar 

  10. McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184(1), 205–222 (2005)

    Article  MathSciNet  Google Scholar 

  11. Nyarko, E.K., Scitovski, R.: Solving the parameter identification problem of mathematical models using genetic algorithms. Appl. Math. Comput. 153(3), 651–658 (2004)

    MathSciNet  MATH  Google Scholar 

  12. Lin, C.T., Lee, C.S.G.: Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. J. Women’s Health (1996)

    Google Scholar 

  13. Holland, J.H.: Adaptation in natural and artificial system. J. Women’s Health (1975)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China

    Yifan Zhang & Dekang Zhao

Authors
  1. Yifan Zhang
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  2. Dekang Zhao
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Corresponding author

Correspondence to Yifan Zhang .

Editor information

Editors and Affiliations

  1. Eastern Michigan University, Ypsilanti, MI, USA

    Yichun Xie

  2. Hebei University of Engineering, Handan, China

    Anbing Zhang

  3. Hebei University of Engineering, Handan, Hebei, China

    Haixin Liu

  4. Hebei University of Engineering, Handan, China

    Lili Feng

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Zhang, Y., Zhao, D. (2019). Using Improved Genetic Algorithm to Solve the Equations. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds) Geo-informatics in Sustainable Ecosystem and Society. GSES 2018. Communications in Computer and Information Science, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-13-7025-0_27

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  • DOI: https://doi.org/10.1007/978-981-13-7025-0_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7024-3

  • Online ISBN: 978-981-13-7025-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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