@inproceedings{2c1479e261044a0e80baefb7aa9fe54b,
title = "Solving Job Scheduling Problem Using Genetic Algorithm",
abstract = "The efficient scheduling of independent computational jobs in a computing environment is an important problem where there are some deadlines for each job to become complete. Finding optimal schedules for such an environment is (in general) an NP-complete problem, and so heuristic approaches must be used. Genetic algorithms are known to give the best solutions to such problems. The purpose of this paper is to propound a solution to a job scheduling problem using genetic algorithms. The experimental results show that the most important factor on the time complexity of the algorithm is the size of the population and the number of generations.",
author = "Soheila Gheisari and Alireza Rezaee and Farshid Hajati",
year = "2021",
month = dec,
day = "31",
doi = "10.1007/978-3-030-75078-7\_53",
language = "English",
isbn = "9783030750770",
volume = "3",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Cham",
number = "LNNS227",
pages = "533--540",
editor = "Leonard Barolli and Isaac Woungang and Tomoya Enokido",
booktitle = "Lecture Notes in Networks and Systems",
note = "AINA 2021: The 35th International Conference on Advanced Information Networking and Applications ; Conference date: 12-05-2021 Through 14-05-2021",
}