Indicator-based Multi-objective Genetic Programming for Workflow Scheduling Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Xiao:2017:GECCO,

• author = "Qin-zhe Xiao and Jinghui Zhong and Wen-Neng Chen and Zhi-Hui Zhan and Jun Zhang",
• title = "Indicator-based Multi-objective Genetic Programming for Workflow Scheduling Problem",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "217--218",
• size = "2 pages",
• URL = "http://doi.acm.org/10.1145/3067695.3075600",
• DOI = "doi:10.1145/3067695.3075600",
• acmid = "3075600",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, Gene Expression Programming, multi-objective optimization, workflow scheduling",
• month = "15-19 " # jul,
• 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.",
• notes = "Also known as \cite{Xiao:2017:IMG:3067695.3075600} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Qin-zhe Xiao Jinghui Zhong Wei-Neng Chen Zhi-Hui Zhan Jun Zhang

Citations