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Genetically Evolved Solution to Timetable Scheduling Problem

by Sandesh Timilsina, Rohit Negi, Yashika Khurana, Jyotsna Seth
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 18
Year of Publication: 2015
Authors: Sandesh Timilsina, Rohit Negi, Yashika Khurana, Jyotsna Seth
10.5120/20077-2100

Sandesh Timilsina, Rohit Negi, Yashika Khurana, Jyotsna Seth . Genetically Evolved Solution to Timetable Scheduling Problem. International Journal of Computer Applications. 114, 18 ( March 2015), 12-17. DOI=10.5120/20077-2100

@article{ 10.5120/20077-2100,
author = { Sandesh Timilsina, Rohit Negi, Yashika Khurana, Jyotsna Seth },
title = { Genetically Evolved Solution to Timetable Scheduling Problem },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 18 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 12-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number18/20077-2100/ },
doi = { 10.5120/20077-2100 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:53:07.475195+05:30
%A Sandesh Timilsina
%A Rohit Negi
%A Yashika Khurana
%A Jyotsna Seth
%T Genetically Evolved Solution to Timetable Scheduling Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 18
%P 12-17
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The simultaneous advancement in genetic modeling and data computational capabilities has prompted profound interest of scientists across the globe in the field of timetable scheduling. The wider usage of timetable scheduling in complex data manipulation and computation has attracted many researchers to put forward their theory regarding the use of genetic algorithms. The progression on this field has increased the efficiency of the timetable to use the limited resources in the given time to get productive results. This paper describes various genetic algorithmic methods.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Timetable Crossover Mutation Constraints Fitness

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