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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2015

01.01.2015 | Original Article

Design of stochastic solvers based on genetic algorithms for solving nonlinear equations

verfasst von: Muhammad Asif Zahoor Raja, Zulqurnain Sabir, Nasir Mehmood, Eman S. Al-Aidarous, Junaid Ali Khan

Erschienen in: Neural Computing and Applications | Ausgabe 1/2015

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Abstract

In the present study, a novel intelligent computing approach is developed for solving nonlinear equations using evolutionary computational technique mainly based on variants of genetic algorithms (GA). The mathematical model of the equation is formulated by defining an error function. Optimization of fitness function is carried out with the competency of GA used as a tool for viable global search methodology. Comprehensive numerical experimentation has been performed on number of benchmark nonlinear algebraic and transcendental equations to validate the accuracy, convergence and robustness of the designed scheme. Comparative studies have also been made with available standard solution to establish the correctness of the proposed scheme. Reliability and effectiveness of the design approaches are validated based on results of statistical parameters.
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Metadaten
Titel
Design of stochastic solvers based on genetic algorithms for solving nonlinear equations
verfasst von
Muhammad Asif Zahoor Raja
Zulqurnain Sabir
Nasir Mehmood
Eman S. Al-Aidarous
Junaid Ali Khan
Publikationsdatum
01.01.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1676-z

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