Population Learning Metaheuristic for Neural Network Training
Ireneusz Czarnowski, Piotr Jędrzejowicz · 2003
Population based methods handle a population of individuals that evolves with the help of information exchange and self-improvement procedures. In this paper an application of a new metaheuristic called population learning algorithm (PLA) to ANN is investigated. The paper proposes several implementations of the PLA to training feed-forward artificial neural networks. The approach is validated by means of computational experiment in which PLA algorithm is used to train ANN solving a variety of benchmarking problems. Results of the experiment prove that PLA can be considered as a useful and effective tool for training ANN. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.