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

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Nguyen:2018:GECCOc,

• 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

Citations