Genetic programming approach to learning multi-pass heuristics for resource constrained job scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Su Nguyen and Dhananjay Thiruvady and 
                 Andreas Ernst and Damminda Alahakoon",
  title =        "Genetic programming approach to learning multi-pass
                 heuristics for resource constrained job scheduling",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "2018",
  editor =       "Hernan Aguirre and Keiki Takadama and 
                 Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and 
                 Andrew M. Sutton and Satoshi Ono and Francisco Chicano and 
                 Shinichi Shirakawa and Zdenek Vasicek and 
                 Roderich Gross and Andries Engelbrecht and Emma Hart and 
                 Sebastian Risi and Ekart Aniko and Julian Togelius and 
                 Sebastien Verel and Christian Blum and Will Browne and 
                 Yusuke Nojima and Tea Tusar and Qingfu Zhang and 
                 Nikolaus Hansen and Jose Antonio Lozano and 
                 Dirk Thierens and Tian-Li Yu and Juergen Branke and 
                 Yaochu Jin and Sara Silva and Hitoshi Iba and 
                 Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and 
                 Federica Sarro and Giuliano Antoniol and Anne Auger and 
                 Per Kristian Lehre",
  isbn13 =       "978-1-4503-5618-3",
  pages =        "1167--1174",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205485",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "this study considers a resource constrained job
                 scheduling problem. Jobs need to be scheduled on
                 different machines satisfying a due time. If delayed,
                 the jobs incur a penalty which is measured as a
                 weighted tardiness. Furthermore, the jobs use up some
                 proportion of an available resource and hence there are
                 limits on multiple jobs executing at the same time. Due
                 to complex constraints and a large number of decision
                 variables, the existing solution methods, based on
                 meta-heuristics and mathematical programming, are very
                 time-consuming and mainly suitable for small-scale
                 problem instances. We investigate a genetic programming
                 approach to automatically design reusable scheduling
                 heuristics for this problem. A new representation and
                 evaluation mechanisms are developed to provide the
                 evolved heuristics with the ability to effectively
                 construct and refine schedules. The experiments show
                 that the proposed approach is more efficient than other
                 genetic programming algorithms previously developed",
  notes =        "Also known as \cite{3205485} GECCO-2018 A
                 Recombination of the 27th International Conference on
                 Genetic Algorithms (ICGA-2018) and the 23rd Annual
                 Genetic Programming Conference (GP-2018)",

Genetic Programming entries for Su Nguyen Dhananjay Thiruvady Andreas Ernst Damminda Alahakoon