Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method
  and Modified Cuckoo Search
  This paper investigates a new method for improving the learning algorithm of
Mixture of Experts (ME) model using a hybrid of Modified Cuckoo Search (MCS)
and Conjugate Gradient (CG) as a second order optimization technique. The CG
technique is combined with Back-Propagation (BP) algorithm to yield a much more
efficient learning algorithm for ME structure. In addition, the experts and
gating networks in enhanced model are replaced by CG based Multi-Layer
Perceptrons (MLPs) to provide faster and more accurate learning. The CG is
considerably depends on initial weights of connections of Artificial Neural
Network (ANN), so, a metaheuristic algorithm, the so-called Modified Cuckoo
Search is applied in order to select the optimal weights. The performance of
proposed method is compared with Gradient Decent Based ME (GDME) and Conjugate
Gradient Based ME (CGME) in classification and regression problems. The
experimental results show that hybrid MSC and CG based ME (MCS-CGME) has faster
convergence and better performance in utilized benchmark data sets.
