Name
Abstract | ||
|---|---|---|
This paper proposes an Indicator-Based Multi-objective Gene Expression Programming (IBM-GEP) to solve Workflow Scheduling Problem (WSP). The key idea is to use Genetic Programming (GP) to learn heuristics to select resources for executing tasks. By using different problem instances for training, the IBM-GEP is capable of learning generic heuristics that are applicable for solving different WSPs. Besides, the IBM-GEP can search for multiple heuristics that have different trade-offs among multiple objectives. The IBM-GEP was tested on instances with different settings. Compared with several existing algorithms, the heuristics found by the IBM-GEP generally perform better in terms of minimizing the cost and completed time of the workflow. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3067695.3075600 | GECCO (Companion) |
Keywords | Field | DocType |
Workflow scheduling, Multi-objective optimization, Genetic programming | Multi objective genetic programming,Gene expression programming,Mathematical optimization,Workflow scheduling,Computer science,Genetic programming,Multi-objective optimization,Heuristics,Artificial intelligence,Workflow,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qin-zhe Xiao | 1 | 1 | 0.36 |
| Jing-hui Zhong | 2 | 380 | 33.00 |
| Wen-neng Chen | 3 | 1 | 0.36 |
| Zhi-hui Zhan | 4 | 1789 | 86.72 |
| Jun Zhang | 5 | 2491 | 127.27 |