Abstract
In brain-computer interfaces (BCIs), a feature selection approach using an adaptive genetic algorithm (AGA) is described in this paper. In the AGA, each individual among the population has its own crossover probability and mutation probability. The probabilities of crossover and mutation are varied depending on the fitness values of the individuals. The adaptive probabilities of crossover and mutation are propitious to maintain diversity in the population and sustain the convergence capacity of the genetic algorithms (GAs). The performance of the AGA is compared with those of the Standard GA (SGA) and the Filter method in selecting feature subset for BCIs. The results show that the classification accuracy obtained by the AGA is significantly higher than those obtained by other methods. Furthermore, the AGA has a higher convergence rate than the SGA.
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Yan, Gz., Wu, T., Yang, Bh. (2006). Automated Feature Selection Based on an Adaptive Genetic Algorithm for Brain-Computer Interfaces. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_73
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DOI: https://doi.org/10.1007/11903697_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
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