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A genetic algorithm with a self-reproduction operator to solve systems of nonlinear equations

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  • William La Cruz

    (Universidad Central de Venezuela)

Abstract

A genetic algorithm for solving systems of nonlinear equations that uses a self-reproduction operator bases on residual approaches is presented and analyzed. To ensure convergence the elitist model is used. A convergence analysis is given. With the aim of showing the advantages of the proposed genetic algorithm an extensive set of numerical experiments with standard test problems and some specific applications are reported.

Suggested Citation

  • William La Cruz, 2022. "A genetic algorithm with a self-reproduction operator to solve systems of nonlinear equations," Journal of Global Optimization, Springer, vol. 84(4), pages 1005-1032, December.
    • Handle: RePEc:spr:jglopt:v:84:y:2022:i:4:d:10.1007_s10898-022-01189-1
    DOI: 10.1007/s10898-022-01189-1
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    References listed on IDEAS

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    1. Haishan Feng & Tingting Li, 2020. "An Accelerated Conjugate Gradient Algorithm for Solving Nonlinear Monotone Equations and Image Restoration Problems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, October.
    2. L. Xiao & S. Boyd, 2006. "Optimal Scaling of a Gradient Method for Distributed Resource Allocation," Journal of Optimization Theory and Applications, Springer, vol. 129(3), pages 469-488, June.
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