scholarly journals Lecture Scheduling Automation Using Genetic Algorithm

2019 ◽  
Vol 8 (5C) ◽  
pp. 1444-1447
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Results ◽  
Scheduling Problems ◽  
Scheduling System ◽  
Parameter Values

This paper aims produce an academic scheduling system using Genetic Algorithm (GA) to solve the academic schedule. Factors to consider in academic scheduling are the lecture to be held, the available room, the lecturers and the time of the lecturer, the suitability of the credits with the time of the lecture, and perhaps also the time of Friday prayers, and so forth. Genetic Algorithms can provide the best solution for some solutions in dealing with scheduling problems. Based on the test results, the resulting system can automate the scheduling of lectures properly. Determination of parameter values in Genetic Algorithm also gives effect in producing the solution of lecture schedule

scholarly journals SCHEDULING USING GENETIC ALGORITHM AND ROULETTE WHEEL SELECTION METHOD CONSIDERING LECTURER TIME

2019 ◽  
Vol 2 (1) ◽  
pp. 24
Author(s):  
Herman Herman ◽  
Lukman Syafie ◽  
Irawati Irawati ◽  
Lilis Nur Hayati ◽  
Harlinda Harlinda
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Science ◽  
Selection Method ◽  
Scheduling Problems ◽  
Scheduling System ◽  
Roulette Wheel Selection ◽  
Roulette Wheel

Scheduling lectures is not something easy, considering many factors that must be considered. The factors that must be considered are the courses that will be held, the space available, the lecturers, the suitability of the credits with the duration of courses, the availability of lecturers' time, and so on. One algorithm in the field of computer science that can be used in lecture scheduling automation is Genetic Algorithms. Genetic Algorithms can provide the best solution from several solutions in handling scheduling problems and the selksi method used is roulette wheel. This study produces a scheduling system that can work automatically or independently which can produce optimal lecture schedules by applying Genetic Algorithms. Based on the results of testing, the resulting system can schedule lectures correctly and consider the time of lecturers. In this study, the roulette wheel selection method was more effective in producing the best individuals than the rank selection method.

scholarly journals Determining Basis Test Paths Using Genetic Algorithm and J4

2018 ◽  
Vol 8 (5) ◽  
pp. 3333
Author(s):  
Achmad Arwan ◽  
Denny Sagita Rusdianto
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Results ◽  
Code Metrics ◽  
Efficiency Test ◽  
Number Of Iterations

<span lang="EN">Basis test paths is a method that uses a graph contains nodes as a representation of codes and the lines as a sequence of code execution steps. Determination of basis test paths can be generated using a Genetic Algorithm, but the drawback was the number of iterations affect the possibility of visibility of the appropriate basis path. When the iteration is less, there is a possibility the paths do not appear all. Conversely, if the iteration is too much, all the paths have appeared in the middle of iteration. This research aims to optimize the performance of Genetic Algorithms for the generation of Basis Test Paths by determining how many iterations level corresponding to the characteristics of the code. Code metrics Node, Edge, VG, NBD, LOC were used as features to determine the number of iterations. J48 classifier was employed as a method to predict the number of iterations. There were 17 methods have selected as a data training, and 16 methods as a data test. The system was able to predict 84.5% of 58 basis paths. Efficiency test results also show that our system was able to seek Basis Paths 35% faster than the old system.</span>

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

scholarly journals Modifikasi Algoritma Genetika untuk Penyelesaian Permasalahan Penjadwalan Pelajaran Sekolah

2014 ◽  
Vol 10 (1) ◽  
pp. 111
Author(s):  
Rahman Erama ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Effective Algorithm ◽  
Scheduling Problems ◽  
The Core ◽  
Alternative Algorithm ◽  
Crossover Phase ◽  
School Scheduling ◽  
Core Problem ◽  
Standard Genetic Algorithm

AbstrakModifikasi Algoritma Genetika pada penelitian ini dilakukan berdasarkan temuan-temuan para peneliti sebelumnya tentang kelemahan Algoritma Genetika. Temuan-temuan yang dimakasud terkait proses crossover sebagai salah satu tahapan terpenting dalam Algoritma Genetika dinilai tidak menjamin solusi yang lebih baik oleh beberapa peneliti. Berdasarkan temuan-temuan oleh beberapa peneliti sebelumnya, maka penelitian ini akan mencoba memodifikasi Algoritma Genetika dengan mengeliminasi proses crossover yang menjadi inti permasalahan dari beberapa peneliti tersebut. Eliminasi proses crossover ini diharapkan melahirkan algoritma yang lebih efektif sebagai alternative untuk penyelesaian permasalahan khususnya penjadwalan pelajaran sekolah.Tujuan dari penelitian ini adalah Memodifikasi Algoritma Genetika menjadi algoritma alternatif untuk menyelesaikan permasalahan penjadwalan sekolah, sehingga diharapkan terciptanya algoritma alternatif ini bisa menjadi tambahan referensi bagi para peneliti untuk menyelesaikan permasalahan penjadwalan lainnya.Algoritma hasil modifikasi yang mengeliminasi tahapan crossover pada algoritma genetika ini mampu memberikan performa 3,06% lebih baik dibandingkan algoritma genetika sederhana dalam menyelesaikan permasalahan penjadwalan sekolah. Kata kunci—algoritma genetika, penjadwalan sekolah, eliminasi crossover  AbstractModified Genetic Algorithm in this study was based on the findings of previous researchers about the weakness of Genetic Algorithms. crossover as one of the most important stages in the Genetic Algorithms considered not guarantee a better solution by several researchers. Based on the findings by previous researchers, this research will try to modify the genetic algorithm by eliminating crossover2 which is the core problem of several researchers. Elimination crossover is expected to create a more effective algorithm as an alternative to the settlement issue in particular scheduling school.This study is intended to modify the genetic algorithm into an algorithm that is more effective as an alternative to solve the problems of school scheduling. So expect the creation of this alternative algorithm could be an additional resource for researchers to solve other scheduling problems.Modified algorithm that eliminates the crossover phase of the genetic algorithm is able to provide 2,30% better performance than standard genetic algorithm in solving scheduling problems school. Keywords—Genetic Algorithm, timetabling school, eliminate crossover

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

Abstract As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms were used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for the automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are descrobed. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

A Analysis and Research on the Methods of Object Oriented Software Testing

2013 ◽  
Vol 709 ◽  
pp. 616-619
Author(s):  
Jing Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Data ◽  
Object Oriented ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Path Coverage ◽  
Parameter Values

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

scholarly journals Optimasi Pendistribusian Kelas Pada Dosen di STMIK STIKOM Indonesia Menggunakan Algoritma Genetika

2019 ◽  
Vol 2 (1) ◽  
pp. 145-154
Author(s):  
Aniek Suryanti Kusuma ◽  
Komang Sri Aryati
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Parameters ◽  
Fitness Function ◽  
Genetic Process ◽  
Fitness Value ◽  
Distribution Process ◽  
Class Scheduling ◽  
System Requirements

The stage of class scheduling starts from scheduling courses in classes, then distributing the class to lecturers. The process of distributing classes to lecturers becomes an obstacle for the STMIK STIKOM Indonesia academic body because the academic body must adjust the existing class with the lecturer who is interested in it as well as the lecturer chosen to support a class so that it does not have classes that have a time conflict. One method for solving these problems is by using genetic algorithms that work by generating a number of random solutions and then processing the collection of solutions in a genetic process. There are eight genetic algorithm procedures, which are random chromosome generation procedures, chromosome repair to validate chromosomes from their limits, fitness function to calculate the feasibility of a solution, crossover, mutation, child repair and elitism. The output of this research is in the form of an analysis and determination of the system requirements that must exist. In addition, it produces a trial report on the effect of genetic parameters to determine the effect of changes in the value of genetic parameters on the fitness value and the time used to carry out the distribution process.  

scholarly journals Penentuan Jarak Terpendek Pada Jalur Pengiriman Musae Chips Dengan Menggunakan Algoritma Genetika

2019 ◽  
Vol 6 (1) ◽  
pp. 19-23
Author(s):  
Muhammad Bahrul Arif
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Distribution Channel ◽  
Optimal Solutions ◽  
Distribution Lines ◽  
Good Path ◽  
Crossover Probability ◽  
Study Results ◽  
Genetic Algorithm Method

Combination of good path distribution by land can optimize travel time and costs. However, not all of these path distribution combinations will provide the best solution. The study was conducted to determine the distribution of goods so that the best solution is achieved. To simplify the process of determining the goods distribution channel, it is supported by software development. Genetic algorithms that have reliability in producing optimal solutions can be used to solve this problem. The application of the genetic algorithm method is applied in software. In the software that is built, there are several inputs needed, namely: cities destination distribution as the initial chromosome number, number of generations, crossover probability and probability of mutation. The result of processing is a combination of goods distribution lines to be taken which represent the solution to this problem. Only the best chromosomes will be given as a result. Through the software that was built, the determination of goods distribution lines is expected to be better and can optimize the time and cost of travel. Based on the research, 5 road combinations are used as chip distribution routes used for testing. This study results from the first fitness gene get the highest fitness 7.4, fitness lowest 5.6 and the second gene get the highest fitness 9.3, fitness lowest 5.6.

Penjadwalan Produksi Dengan Algoritma Genetik Untuk Meniminisasi Makespan di PT AAA

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
M. Ghassan Fattah ◽  
Rosnani Ginting
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Machine Scheduling ◽  
Optimal Timing ◽  
Scheduling Problems ◽  
Fitness Value ◽  
Genetic Algorithm Method

PT. AAA dari bulan Januari sampai Desember mendapat total 88 order dengan jumlah keterlambatan 12 order maka persentase keterlembatan adalah 13,63%. Tujuan penelitian ini adalah untuk merancangan penerapan algoritma genetik yang dapat menghindari keterlambatan order yaitu untuk mengukur makespan produk dan merancang urutan penjadwalan mesin. Penyelesaian masalah penjadwakan dengan algoritma genetik. Algoritma genetik merupakan teknik search stochastic yang berdasarkan mekanisme seleksi alam dan genetika natural dengan melakukan proses inisialisasi awal lalu dicari nilai fitness dari setiap individu, yang akan menjadi induk adalah yang memiliki nilai fitness terbaik lalu dilakukan proses penyilangan dan mutasi dan pemilihan waktu optimal. Dari hasil perhitungan dengan menggunakan metode Algoritma Genetika diperoleh urutan penjadwalan mesin terbaik dan dengan nilai makespan terkecil.   PT. AAA from January to December received a total of 88 orders with the number of delays of 12 orders, the percentage of bridges was 13.63%. The purpose of this study is to design the application of a genetic algorithm that can avoid delay in order to measure product makespan and design the order of machine scheduling. Resolving scheduling problems with genetic algorithms. Genetic algorithm is a search stochastic technique that is based on the mechanism of natural selection and natural genetics by carrying out the initial initialization process and then looks for the fitness value of each individual, who will be the parent who has the best fitness value and then the process of crossing and mutation and optimal timing. From the results of calculations using the Genetic Algorithm method, the best sequence of machine scheduling is obtained and with the smallest makespan value.

SIMULATION OF GENETIC ALGORITHMS ON MIMD MULTICOMPUTERS

1992 ◽  
Vol 02 (04) ◽  
pp. 381-389 ◽  
Author(s):  
I. DE FALCO ◽  
R. DEL BALIO ◽  
E. TARANTINO ◽  
R. VACCARO
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Diffusion Processes ◽  
Travelling Salesman Problem ◽  
Test Results ◽  
Parallel Genetic Algorithm ◽  
Genetic Operators ◽  
Parallel Diffusion ◽  
Fully Connected ◽  
Chordal Ring

In this paper, a Parallel Genetic Algorithm has been developed and mapped onto a coarse grain MIMD multicomputer whose processors have been configured in a fully connected chordal ring topology. In this way, parallel diffusion processes of good local information among processors have been carried out. The Parallel Genetic Algorithm has been applied, specifically, to the Travelling Salesman Problem. Many experiments have been performed with different combinations of genetic operators; the test results suggest that PMX crossover can be avoided by using only the inversion genetic operator and that a diffusion process leads to improved search in Parallel Genetic Algorithms.

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