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Indicator-Based Multi-Objective Genetic Programming for Workflow Scheduling Problem
- Issue Date
- Jul-2017
- Publisher
- ASSOC COMPUTING MACHINERY
- Keywords
- Workflow scheduling; Multi-objective optimization; Genetic programming
- Citation
- GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp 217 - 218
- Pages
- 2
- Indexed
- SCIE
- Journal Title
- GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
- Start Page
- 217
- End Page
- 218
- 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 workfkow.
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Related Researcher
- ZHANG, Jun
- ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)