Evolving Multilayer Perceptrons
Neural Processing Letters
Swarm intelligence
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks
Neural Processing Letters
Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
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Due to their efficiency and adaptability, bio-inspired algorithms have shown their usefulness in a wide range of different non-linear optimization problems. In this paper, we compare two ways of training an artificial neural network (ANN): Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. The main contribution of this paper is to show which of these two algorithms provides the best accuracy during the learning phase of an ANN. First of all, we explain how the ANN training phase could be seen as an optimization problem. Then, we explain how PSO and DE could be applied to find the best synaptic weights of the ANN. Finally, we perform a comparison between PSO and DE approaches when used to train an ANN applied to different non-linear problems.