Multilayer feedforward networks are universal approximators
Neural Networks
Parallel distributed processing: explorations in the microstructure, vol. 2: psychological and biological models
On the Problem of Local Minima in Backpropagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
Reinforcement Learning for Combining Relevance Feedback Techniques
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Solution for the N-bit Parity Problem Using a Single Translated Multiplicative Neuron
Neural Processing Letters
Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Neural Computing and Applications
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
On the Invariance of Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Global artificial bee colony algorithm for boolean function classification
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Learning problems for Neural Network (NN) has widely been explored in the past two decades. Researchers have focused more on population-based algorithms because of its natural behavior processing. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) algorithm produced an easy way for NN training. These social based techniques are mostly used for finding best weight values and over trapping local minima in NN learning. Typically, NN trained by traditional approach, namely the Backpropagation (BP) algorithm, has difficulties such as trapping in local minima and slow convergence. The new method named Global Hybrid Ant Bee Colony (G-HABC) algorithm which can overcome the gaps in BP is used to train the NN for Boolean Function classification task. The simulation results of the NN when trained with the proposed hybrid method were compared with that of Levenberg-Marquardt (LM) and ordinary ABC. From the results, the proposed G-HABC algorithm has shown to provide a better learning performance for NNs with reduced CPU time and higher success rates.