Computational experience with generalized simulated annealing over continuous variables
American Journal of Mathematical and Management Sciences
Local and global optimization algorithms for generalized learning automata
Neural Computation
Global Optimization for Neural Network Training
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Beyond back propagation: using simulated annealing for training neural networks
Journal of End User Computing
Neutral Networks in Optimization
Neutral Networks in Optimization
A matrix method for optimizing a neural network
Neural Computation
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
A meta-heuristic paradigm for solving the forward kinematics of 6-6 general parallel manipulator
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
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Neural network learning is the main essence of ANN. There are many problems associated with the multiple local minima in neural networks. Global optimization methods are capable of finding global optimal solution. In this paper we investigate and present a comparative study for the effects of probabilistic and deterministic global search method for artificial neural network using fully connected feed forward multi-layered perceptron architecture. We investigate two probabilistic global search method namely Genetic algorithm and Simulated annealing method and a deterministic cutting angle method to find weights in neural network. Experiments were carried out on UCI benchmark dataset.