Evolving Multilayer Perceptrons
Neural Processing Letters
Swarm intelligence
Earthquake classifying neural networks trained with random dynamic neighborhood PSOs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks
Neural Processing Letters
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Growing Particle Swarm Optimizers with a Population-Dependent Parameter
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Design of artificial neural networks using differential evolution algorithm
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Pattern recognition using spiking neurons and firing rates
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Training spiking neurons by means of particle swarm optimization
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
An evolutionary feature-based visual attention model applied to face recognition
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
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In the last years, bio-inspired algorithms have shown their power in different non-linear optimization problems. Due to the efficiency and adaptability of bio-inspired algorithms, in this paper we explore a new way to design an artificial neural network (ANN). For this task, a modified PSO algorithm was used. We do not only study the problem of finding the optimal synaptic weights of an ANN but also its topology and transfer functions. In other words, given a set of inputs and desired patterns, with the proposal we are able to find the best topology, the number of neurons, the transfer function for each neuron, as well as the synaptic weights. This allows to designing an ANN to be used to solve a given problem. The proposal is tested using several non-linear problems.