A Zero Crossing-Virus Evolutionary Genetic Algorithm (VEGA) to Solve Nonlinear Equations
ARIF, M. Ziaul
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Nonlinear equation is a mathematical problem that is quite difficult to solve. Its analytic solution is not easily discovered. There are several methods used to solve nonlinear equations and the obtained results is in the form of approximation to the analytical solution. Most of the numerical method need appropriate initial value to perform the accuration of the method. However, it will diverge if the initial value is inappropriate. Therefore, we propose discovering the solutions of nonlinear equations by applying metaheuristic methods. In this paper, we present the virus Evolutionary Genetic Algorithm (VEGA) combined with Zero Crossing Method at an early stage to solve nonlinear equations. This study was conducted to test the performance and accuracy of the combined both of the method by providing some examples.
- LSP-Conference Proceeding