Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-32034-2_59 | HYBRID ARTIFICIAL INTELLIGENT SYSTEMS |
Field | DocType | Volume |
Data mining,Matrix (mathematics),Computer science,Gene expression,Artificial intelligence,Probabilistic logic,Biclustering,Machine learning,Encoding (memory),Complex problems | Conference | 9648 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Javier Gil-Cumbreras | 1 | 0 | 0.68 |
| Raúl Giráldez | 2 | 105 | 10.26 |
| Jesús S. Aguilar-ruiz | 3 | 625 | 59.56 |