A modified genetic algorithm for neurocontrollers

Abstract

Genetic algorithms are getting more popular nowadays because of their simplicity and robustness. Genetic algorithms are global search techniques for optimizations and many other problems. A feed-forward neural network that is widely used in central applications usually learns by back propagation algorithm (BP). However, when there exist certain constraints, BP cannot be applied. We apply a genetic algorithm to such a case. To improve hill-climbing capability and speed up the convergence, we propose a modified genetic algorithm (MGA). The validity and efficiency of the proposed algorithm. MGA are shown by various simulation examples of system identification and nonlinear system control such as cart-pole systems and robot manipulators