A comparative study to solve the differential algebraic equation systems by using genetic algorithm and neural networks

In this paper, the solution system of differential algebraic equations (DAES). A novel hybrid method for the solution of ordinary and partial differential equations is presented here. The method creates trial solutions in Genetic Algorithms, neural network form using a scheme based on grammatical evolution. The trial solutions are enhanced periodically using a local optimization procedure. The proposed method is tested on a series of ordinary differential equations, systems of ordinary differential equations as well as on partial differential equations with boundary conditions and the results are reported. The goal is to obtain accurate solution with reduced calculus effort by comparing the solution of the DAES obtained from Genetic algorithms, A domian decomposition method and neural network approach , A domain decomposition method and pade approximation to this problem is very close to the exact solution. An illustrative numerical method is presented for the proposed method.