Connection Topologies for Combining Genetic and Least Square Methods for Neural Learning
Ranadhir Ghosh · Journal of Intelligent Systems · 2004
In the last few years, there have been many works in the area of hybrid neural learning algorithms combining a global and local based method for training artificial neural networks.In this paper, we discuss various connection strategies that can be applied to a special kind of a hybrid neural learning algorithm group, one that combines a genetic algorithm-based method with various least square-based methods like QR factorization.The relative advantages and disadvantages of the different connection types are studied to find a suitable connection topology for combining the two different learning methods.The methodology also finds the optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture.We have tested our proposed approach on XOR, 10 bit odd parity, and some other real-world benchmark data sets, such as the handwriting character dataset from CEDAR, Breast cancer, and Heart Disease from the UCI machine learning repository.