A new random search method for neural network learning-RasID

This paper presents a novel random searching scheme called RasID for neural networks training. The idea is to introduce a sophisticated probability density function (PDF) for generating search vector. The PDF provides two parameters for realizing intensified search in the area where it is likely to find good solutions locally or diversified search in order to escape from a local minimum based on the success-failure of the past search. Gradient information is used to improve the search performance. The proposed scheme is applied to layered neural networks training and is benchmarked against other deterministic and nondeterministic methods.