A new gradient-free learning algorithm

Summary form only given, as follows. A new supervised learning algorithm which does not require any gradient computation is presented. In the new gradient-free (G-F) algorithm, the error between the actual output and the desired output is not measured by the least-squared norm as in the backpropagation algorithm, but by the up-norm. In the G-F algorithm, the weights are updated in each iteration only after incorporating all the input patterns. The authors use the example of the XOR problem to evaluate the performance of the algorithm. A Monte-Carlo simulation is performed and the results obtained are encouraging.<>