| Autor: |
Joseph Alexander Brown, Sheridan Houghten, Adel Zakirov, Tyler K. Collins |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
CIBCB |
| DOI: |
10.1109/cibcb.2017.8058564 |
| Popis: |
Storage and processing of biological networks is challenging and costly due to the large sizes of many of these networks. Compression of such graphs is one possible solution to this problem. This study presents two single-objective genetic algorithms, along with one multi-objective algorithm, to address the problem of graph compression. The fitness functions were both based on the concept of merging nodes based on “similarity” but each defined that similarity in a different way. The multiobjective GA based on NSGA-II worked to find a balance between the compression ratio and the similarity. The methods were applied to three different biological networks with different characteristics. The single-objective GAs were first applied to these networks for a fixed compression ratio. Then based on the results of the multiobjective GA, target compression ratios were chosen for each graph and the single-objective GAs were applied to this target. Applying the single-objective GAs to a target identified in this manner was significantly more successful than using the results from the multiobjective GA for the same compression ratio. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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