Hybrid Methods Using Evolutionary Algorithms for On-Line Training

A novel hybrid evolutionary approach is presented in this paper for improving the performance of neural network classifiers in slowly varying environments. For this purpose, we investigate a coupling of Differential Evolution Strategy and Stochastic Gradient Descent, using both the global search capabilities of Evolutionary Strategies and the effectiveness of on–line gradient descent. The use of Differential Evolution Strategy is related to the concept of evolution of a number of individuals from generation to generation and that of on–line gradient descent to the concept of adaptation to the environment by learning. The hybrid algorithm is tested in two real-life image processing applications. Experimental results suggest that the hybrid strategy is capable to train on–line effectively leading to networks with increased generalization capability.