Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan
The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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LPPM ISB Atma Luhur
2020-09-01
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| Series: | Jurnal Sisfokom |
| Subjects: | |
| Online Access: | http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/879 |
| _version_ | 1797284425648570368 |
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| author | Muhammad Ezar Al Rivan Bhagaskara Bhagaskara |
| author_facet | Muhammad Ezar Al Rivan Bhagaskara Bhagaskara |
| author_sort | Muhammad Ezar Al Rivan |
| collection | DOAJ |
| description | The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven to be able to provide a schedule that can meet limitations in scheduling. Besides, it also found a new concept of thought from GA, namely the Fluid Genetic Algorithm (FGA). The most visible difference between FGA and GA is that there is no mutation process in each iteration. FGA has a new stage, namely individual born and new constants, namely global learning rate, individual learning rate, and diversity rate. This concept of thinking was tested in previous studies and found that FGA is superior to GA for the problem of finding the optimum value of a predetermined function, but this function is not included in the multi-objective problem. In this study, the testing and comparison of FGA and GA were conducted for the problem of scheduling lectures at STMIK XYZ. Based on the results obtained, FGA can produce a schedule without any hard constraint violations. FGA can be used to solve multi-objective problems. FGA has a smaller number of generations than GA. However, overall GA is superior in producing schedules without any problems. |
| first_indexed | 2024-03-07T17:48:47Z |
| format | Article |
| id | doaj.art-510efc901ee344bbac65a751d1137d9d |
| institution | Directory Open Access Journal |
| issn | 2301-7988 2581-0588 |
| language | English |
| last_indexed | 2024-03-07T17:48:47Z |
| publishDate | 2020-09-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj.art-510efc901ee344bbac65a751d1137d9d2024-03-02T14:30:18ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882020-09-019335035610.32736/sisfokom.v9i3.879570Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan PerkuliahanMuhammad Ezar Al Rivan0Bhagaskara Bhagaskara1STMIK Global Informatika MDPSTMIK Global Informatika MDPThe lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven to be able to provide a schedule that can meet limitations in scheduling. Besides, it also found a new concept of thought from GA, namely the Fluid Genetic Algorithm (FGA). The most visible difference between FGA and GA is that there is no mutation process in each iteration. FGA has a new stage, namely individual born and new constants, namely global learning rate, individual learning rate, and diversity rate. This concept of thinking was tested in previous studies and found that FGA is superior to GA for the problem of finding the optimum value of a predetermined function, but this function is not included in the multi-objective problem. In this study, the testing and comparison of FGA and GA were conducted for the problem of scheduling lectures at STMIK XYZ. Based on the results obtained, FGA can produce a schedule without any hard constraint violations. FGA can be used to solve multi-objective problems. FGA has a smaller number of generations than GA. However, overall GA is superior in producing schedules without any problems.http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/879genetic algorithmfluid genetic algorithmtimetabling |
| spellingShingle | Muhammad Ezar Al Rivan Bhagaskara Bhagaskara Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan Jurnal Sisfokom genetic algorithm fluid genetic algorithm timetabling |
| title | Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan |
| title_full | Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan |
| title_fullStr | Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan |
| title_full_unstemmed | Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan |
| title_short | Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan |
| title_sort | perbandingan fluid genetic algorithm dan genetic algorithm untuk penjadwalan perkuliahan |
| topic | genetic algorithm fluid genetic algorithm timetabling |
| url | http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/879 |
| work_keys_str_mv | AT muhammadezaralrivan perbandinganfluidgeneticalgorithmdangeneticalgorithmuntukpenjadwalanperkuliahan AT bhagaskarabhagaskara perbandinganfluidgeneticalgorithmdangeneticalgorithmuntukpenjadwalanperkuliahan |