backpropagation learning with a (1+1) es
We present an improvement to backpropagation (BP) learning for neural networks using a (1+1) evolutionary strategy (ES). The goal is to provide a method than can adaptively change the learning parameters used in BP in an unconstrained manner. The BP/ES algorithm we propose is simple to implement and can be used with various versions of BP. In our experiments there is a substantial increase in performance for time series prediction
backpropagation neural networks evolutionary strategies