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Using Improved Genetic Algorithm to Solve the Equations

  • Yifan ZhangEmail author
  • Dekang Zhao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

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.

Keywords

Genetic algorithm Equations Punch-wheel algorithm 

References

  1. 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. 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. 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. 4.
    Jiang, L., Xu, N.: Scattering of nonlinear Schrodinger equations. Appl. Math. (02) (2013)Google Scholar
  5. 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. 6.
    Zeng, Y.: Application of improved genetic algorithm in solving nonlinear equations. J. East China Jiaotong Univ. (04) (2004)Google Scholar
  7. 7.
    Hu, N., Pan, Q.: Genetic algorithm for solving multiple equations. J. Jingzhou Normal Univ. (02) (2002)Google Scholar
  8. 8.
    Tsoulos, I.G.: Modifications of real code genetic algorithm for global optimization. Appl. Math. Comput. 203(2), 598–607 (2008)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Kaelo, P., Ali, M.M.: Integrated crossover rules in real coded genetic algorithms. Eur. J. Oper. Res. (1) (2005)Google Scholar
  10. 10.
    McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184(1), 205–222 (2005)MathSciNetCrossRefGoogle Scholar
  11. 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)MathSciNetzbMATHGoogle Scholar
  12. 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. 13.
    Holland, J.H.: Adaptation in natural and artificial system. J. Women’s Health (1975)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information and Electrical EngineeringHebei University of EngineeringHandanChina

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