Image restoration by using new AGA optimized BPNN

In this paper a new method of Adaptive Genetic Algorithm (AGA) is introduced to optimize the back propagation neural network for Image restoration. In this new AGA, we added permutation operator in addition to traditional mutation and Pooling Operator is introduced. To increase the convergence rate, we used adaptive crossover rate and mutation rate. It has been observed that with the addition of these two operators, the use of Genetic Algorithms (GA) for navigating the optimal combination of solution is more effective and the convergence can be achieved with more accuracy. In addition, it also decreases the value of Mean Square Error (MSE) significantly.