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

Created by W.Langdon from gp-bibliography.bib Revision:1.4163

@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 Wen-Neng Chen Zhi-Hui Zhan Jun Zhang

Citations