Name
Title | ||
|---|---|---|
Predictive model for epistasis-based basis evaluation on pseudo-boolean function using deep neural networks. |
Abstract | ||
|---|---|---|
Complexity of a problem can be substantially reduced through basis change, however, it is not easy to find an appropriate basis in representation because of difficulty of basis evaluation. To address this issue, a method has been proposed to evaluate a basis based on the epistasis that shows the problem difficulty. However, the basis evaluation is very time-consuming. In this study, a method is proposed to evaluate a basis quickly by developing a model that estimates the epistasis from the basis by using deep neural networks. As experimental results of variant-onemax and NK-landscape problems, the epistasis has been estimated successfully by using the proposed method.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3319619.3326784 | GECCO |
Keywords | Field | DocType |
basis, deep neural networks, epistasis, pseudo-Boolean function | Computer science,Epistasis,Pseudo-Boolean function,Artificial intelligence,Deep neural networks,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6748-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong-Hoon Kim | 1 | 0 | 0.68 |
| Junghwan Lee | 2 | 50 | 12.51 |
| Yong-Hyuk Kim | 3 | 355 | 40.27 |