A Measurement of the Board Governance Performance by Means of Neural Networks and Genetic Algorithms

A Neural Network (NN) models is developed and applied to measure the company board governance capacity in China, incorporating Genetic Algorithm (GA) techniques for detecting the networks´ structure. By introducing Genetic Algorithm, NNGA Model can improve the global search capability and robust. Empirical results show that NNGA model improved the networks´ performance comparing with traditional NN model. The stochastic nature of NNGA networks´ structures develop more heterogeneous structures than NN model which were chosen through a fixed procedure.