A genetic programming based iterated local search for software project scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Sabar:2018:GECCO,
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author = "Nasser R. Sabar and Ayad Turky and Andy Song",
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title = "A genetic programming based iterated local search for
software project scheduling",
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booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2018",
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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",
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pages = "1364--1370",
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address = "Kyoto, Japan",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, Software
Project Scheduling Problem, Combinatorial
Optimisation,Scheduling, Iterated Local Search",
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isbn13 = "978-1-4503-5618-3",
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URL = "http://www.cmap.polytechnique.fr/~nikolaus.hansen/proceedings/2018/GECCO/proceedings/proceedings_files/pap414s3-file1.pdf",
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DOI = "doi:10.1145/3205455.3205557",
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size = "7 pages",
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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.",
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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 Mashaan Turky
Andy Song
Citations