Abstract
In this work, we have extended the experimental analysis about an encoding approach for evolutionary-based algorithms proposed in [1], called probabilistic encoding. The potential of this encoding for complex problems is huge, as candidate solutions represent regions, instead of points, of the search space. We have tested in the context of gene expression biclustering problem, in a selection of a well-known expression matrix datasets. The results obtained for the experimental analysis reveals a satisfactory performance in comparison with other evolutionary-based algorithms, and a high exploration power in very large search spaces.
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Notes
- 1.
In [1], IEPR was named as MOBPEOC (Multi-Objective Biclustering with Probabilistic Encoding and Overlapping Control).
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Acknowledgements
This research has been supported by the Spanish Ministry of Economy and Competitiveness under grants TIN2011-28956 and TIN2014-55894-C2-R.
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Gil-Cumbreras, F.J., Giráldez, R., Aguilar-Ruiz, J.S. (2016). Extending Probabilistic Encoding for Discovering Biclusters in Gene Expression Data. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_59
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