Adaptive charting genetic programming for dynamic flexible job shop scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Su Nguyen and Mengjie Zhang and Kay Chen Tan",
  title =        "Adaptive charting genetic programming for dynamic
                 flexible job shop 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 =        "1159--1166",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205531",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic programming has been considered as a powerful
                 approach to automated design of production scheduling
                 heuristics in recent years. Flexible and variable
                 representations allow genetic programming to discover
                 very competitive scheduling heuristics to cope with a
                 wide range of dynamic production environments. However,
                 evolving sophisticated heuristics to handle multiple
                 scheduling decisions can greatly increase the search
                 space and poses a great challenge for genetic
                 programming. To tackle this challenge, a new genetic
                 programming algorithm is proposed to incrementally
                 construct the map of explored areas in the search space
                 and adaptively guide the search towards potential
                 heuristics. In the proposed algorithm, growing neural
                 gas and principal component analysis are applied to
                 efficiently generate and update the map of explored
                 areas based on the phenotypic characteristics of
                 evolved heuristics. Based on the obtained map, a
                 surrogate assisted model will help genetic programming
                 determine which heuristics to be explored in the next
                 generation. When applied to evolve scheduling
                 heuristics for dynamic flexible job shop scheduling
                 problems, the proposed algorithm shows superior
                 performance as compared to the standard genetic
                 programming algorithm. The analyses also show that the
                 proposed algorithm can balance its exploration and
                 exploitation better than the existing
                 surrogate-assisted algorithm.",
  notes =        "Also known as \cite{3205531} 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 Mengjie Zhang Kay Chen Tan