Using promoters and functional introns in genetic algorithms for neuroevolutionary learning in non-stationary problems

Author:

Bellas F.,Becerra J.A.,Duro R.J.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference24 articles.

1. F. Gomez, J. Schmidhuber, R. Miikkulainen, Efficient non-linear control through neuroevolution, in: Proceedings of the European Conference on Machine Learning, 2006.

2. Evolving artificial neural networks;Yao;Proceedings of the IEEE,1999

3. K. Trojanowski, Z. Michalewicz, Evolutionary approach to non-stationary optimisation tasks, in: Lecture Notes In Computer Science, vol. 1609, 1999, pp. 538–546.

4. D. Dasgupta, D. McGregor, A structured genetic algorithm: the model and first results. Computer Science Department IKBS-2-91, University of Strathclyde, Glasgow, UK, 1991.

5. D. Dasgupta, D.R. MacGregor, Nonstationary function optimization using the structured genetic algorithm, in: Proceedings of the Parallel Problem Solving from Nature, vol. 2, 1992, pp. 145–154.

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