Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem

Assi, Maram; Halawi, Bahia; Haraty, Ramzi A.

doi:10.1016/j.procs.2018.08.024

Cited by 38 publications

(24 citation statements)

References 16 publications

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“…Tetapi terdapat perbedaan di antara keduanya, yaitu pada jumlah mata kuliah yang diujikan, jumlah dosen, sesi perkuliahan satu hari serta hari kegiatan belajar dalam satu minggu. Adanya pengembangan dari GC yang digabungkan dengan Genetic Algorithm (GA) pada makalah [19]. GC mampu mengurangi biaya penalty rata-rata sekitar 70% dan nilai fitness hanya meningkat 10% dari populasi awal setelah 150 generasi.…”

Section: Analisis Algoritmaunclassified

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

Handayani¹,

Fudholi²,

Rani

2020

petir

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Penjadwalan mata kuliah merupakan hal penting yang dilakukan pada awal semester akademik. Proses penyusunan jadwal kuliah secara manual seringkali mengalami kesulitan karena terdapat beberapa konstrain sehingga membutuhkan waktu yang lama. Penelitian ini bertujuan mengkaji algoritma-algoritma yang sesuai dengan masalah penjadwalan mata kuliah. Pencarian dan analisis dilakukan terhadap literatur yang berkaitan dengan optimasi penjadwalan. Proses pencarian literatur dilakukan pada Google Scholar dan Science Direct dengan memasukkan kata kunci utama “course timetable”, “university timetable problem”, “school scheduling”, dan “algoritma penjadwalan”. Hasil analisis literatur meliputi sebaran domain, analisis algoritma serta gap dari penelitian sebelumnya. Pada penelitian sebelumnya terdapat kekurangan seperti algoritma yang tidak dapat menghasilkan solusi optimal. Hasil sebaran domain yang diperoleh ialah universitas dan sekolah dengan persentase 88% dan 12% dari keseluruhan makalah. Adanya temuan 14 sebaran algoritma dapat diklasifikasikan menjadi 3 metode, yaitu heuristic, metaheuristic, dan hyper-heuristic. Berdasarkan hasil analisis, dapat diberikan beberapa rekomendasi. Untuk optimasi yang cepat, Simulated Annealing (SA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.481-10.102s. Untuk solusi waktu dan nilai fitness terbaik, Genetic Algorithm (GA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.964-73.461s dan nilai fitness 1.

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Section: Analisis Algoritmaunclassified

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

Handayani¹,

Fudholi²,

Rani

2020

petir

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“…The above approach deals the problem by encoding the problem in DNA sequences and by performing the operations mentioned above in efficiently due to parallelism capabilities of DNA computing. In another approach researchers have address the university time table problem [26]. The solution of the problem has find by transforming the problem into graph coloring problem and then solve the problem using genetic algorithm based approach.…”

Section: Literature Reviewmentioning

confidence: 99%

An Algorithm Based on Heap of Binary Search Tree to Solve Graph Coloring Problem

Shukla¹,

Bharti²,

Garg³

2019

IJRTE

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Graph coloring problem is one of most frequent studied problem in the graph theory due to its uses in different area of applications like simulation of biological networks, communication networks, register allocation and many more. This problem involves the coloring of the vertices of the given a graph G (V, E) with number of available colors in such a manner that adjacent vertices must assign colors different with each other. In this paper we present a hybrid approach to assign the colors to vertices of the given graph that is based on adjacency matrix and search tree data structure. The coloring process for a particular vertex in the graph will done by getting the feasible colors available in the color list. The feasible colors that may be assigned to a vertex, retrieved from the vertex-color binary search tree generated initially for available colors. The proposed solution for the graph coloring problem is efficient in terms of its running time complexity and it will work without affecting its complexity for any kind of graph.

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“…Adaptive Genetic Algorithm is used to improve the effectiveness in solving problems associated with automated examination timetable in universities [2]. It was analysed using graph colouring, which proved its efficiency in solving problems associated with course timetabling [3]. However, the possibility of premature convergence and stagnation tended to occur while determining solutions [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

Fajrin

Fatichah

2020

register. jurnal. ilm. teknologi. sistem. inf.

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A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling.

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Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem

Cited by 38 publications

References 16 publications

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

An Algorithm Based on Heap of Binary Search Tree to Solve Graph Coloring Problem

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

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