Advances in evolutionary feature selection neural networks with co-evolution learning

• Published in: Neural computing & applications 2008-06, Vol.17 (3), p.217-226
• Main Author: Mohamed Ben Ali, Yamina
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Inference from stochastic processes; time series analysis; Learning and adaptive systems; Mathematics; Multivariate analysis; Original Article; Parametric inference; Probability and statistics; Probability and Statistics in Computer Science; Sciences and techniques of general use; Statistics
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-007-0114-x

Training neural networks is a complex task provided that many algorithms are combined to find best solutions to the classification problem. In this work, we point out the evolutionary computing to minimize a neural configuration. For this purpose, a distribution estimation framework is performed to select relevant features, which lead to classification accuracy with a lower complexity in computational time. Primarily, a pruning strategy-based score function is applied to decide the network relevance in the genetic population. Since the complexity of the network (connections, weights, and biases) is most important, the cooling state of the system will strongly relate to the entropy as a minimization function to reach the desired solution. Also, the framework proposes coevolution learning (with discrete and continuous representations) to improve the behavior of the evolutionary neural learning. The results obtained after simulations show that the proposed work is a promising way to extend its usability to other classes of neural networks.