Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Swarm intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm
IEEE Transactions on Neural Networks
An Optimization Methodology for Neural Network Weights and Architectures
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Evolving multilayer feedforward neural network using adaptive particle swarm algorithm
International Journal of Hybrid Intelligent Systems
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Training neural networks is a complex task of great importance in problems of supervised learning. The Particle Swarm Optimization (PSO) consists of a stochastic global search originated from the attempt to graphically simulate the social behavior of a flock of birds looking for resources. In this work we analyze the use of the PSO algorithm and two variants with a local search operator for neural network training and investigate the influence of the GL5 stop criteria in generalization control for swarm optimizers. For evaluating these algorithms we apply them to benchmark classification problems of the medical field. The results showed that the hybrid GCPSO with local search operator had the best results among the particle swarm optimizers in two of the three tested problems.