A genetic programming based iterated local search for software project scheduling

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

@InProceedings{Sabar:2018:GECCO,
  author =       "Nasser R. Sabar and Ayad Turky and Andy Song",
  title =        "A genetic programming based iterated local search for
                 software project 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 =        "1364--1370",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205557",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Project Scheduling Problem (PSP) plays a crucial role
                 in large-scale software development, directly affecting
                 the productivity of the team and on-time delivery of
                 software projects. PSP concerns with the decision of
                 who does what and when during the software project
                 lifetime. PSP is a combinatorial optimisation problem
                 and inherently NP-hard, indicating that approximation
                 algorithms are highly advisable for real-world
                 instances which are often large in size. In this work,
                 we propose an iterated local search (ILS) algorithm for
                 PSP. ILS is a simple, yet effective for combinatorial
                 optimisation problems. However, its performance highly
                 depends on its perturbation operator which is to guide
                 the search to new starting points. Hereby, we propose a
                 Genetic Programming (GP) approach to evolve
                 perturbation operators based on a range of low-level
                 operators and rules. The evolution process will go
                 along with the iterated search process and supply
                 better operators continuously. The GP based ILS
                 algorithm is tested using a set of well known PSP
                 benchmark instances and compared with state-of-the-art
                 algorithms. The experimental results demonstrated the
                 effectiveness of GP generated perturbation operators as
                 they can outperform existing leading methods.",
  notes =        "Also known as \cite{3205557} 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 Nasser R Sabar Ayad Turky Andy Song

Citations