A genetic programming based iterated local search for software project scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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",
• pages = "1364--1370",
• address = "Kyoto, Japan",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, Software Project Scheduling Problem, Combinatorial Optimisation,Scheduling, Iterated Local Search",
• isbn13 = "978-1-4503-5618-3",
• URL = "http://www.cmap.polytechnique.fr/~nikolaus.hansen/proceedings/2018/GECCO/proceedings/proceedings_files/pap414s3-file1.pdf",
• DOI = "doi:10.1145/3205455.3205557",
• size = "7 pages",
• 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 Mashaan Turky Andy Song

Citations