scholarly journals Membangun Sistem Informasi Penjadwalan Dengan Metode Algoritma Genetika Pada Laboratorium Teknik Informatika Universitas Muhammadiyah Maluku Utara

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Agil Assagaf ◽  
Adelina Ibrahim ◽  
Catur Suranto
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Parameter ◽  
Optimal Schedule ◽  
Optimization Method ◽  
Computational Approach ◽  
Biological Processes ◽  
Practice Schedule ◽  
Parameter Values ◽  
Number Of Individuals

Abstrak : Penjadwalan praktikum merupakan proses penyusunan jadwal pelaksanaan yang menginformasikan sejumlah mata kuliah, dosen yang mengajar, ruang, serta waktu kegiatan perkuliahan di laboratorium. Perlu diperhatikan beberapa aspek untuk menyusun jadwal perkuliahan yang sesuai dengan kebutuhan. Aspek yang perlu diperhatikan antara lain adalah aspek dari dosen yang mengajar, mata kuliah yang diajar. Penyusunan jadwal secara manual cenderung membutuhkan waktu yang lebih lama dan ketelitian yang cukup bagi pembuat jadwal. Untuk dapat membuat jadwal yang optional, dibutuhkan metode optimasi. Pada penelitian ini, akan diuji coba metode optimasi dalam pembuatan jadwal praktikum yaitu Algoritma Genetika. Algoritma genetika merupakan pendekatan komputasional untuk menyelesaikan masalah yang dimodelkan dengan proses biologi dari evolusi. Parameter-parameter Algoritma Genetika yang mempengaruhi jadwal perkuliahan yang dihasilkan adalah jumlah individu, probabilitas crossover, probabilitas mutasi serta metode seleksi, crossover yang digunakan. Pengujian dilakukan dengan cara mencari nilai parameter-parameter algoritma genetika yang paling optimal dalam jadwal perkuliahan. Hasil penelitian menunjukkan bahwa dengan jumlah generasi, jumlah individu, probabilitas crossover dan probabilitas mutasi dapat menghasilkan jadwal yang paling optimal.Kata kunci: Optimasi, Penjadwalan, Seleksi, Crossover, Mutasi, Algoritma GenetikaAbstract : Practical scheduling is the process of preparation of an implementation schedule that informs a number of courses, lecturers who teach, space, and time of lecture activities in the laboratory. It should be noted several aspects to arrange lecture schedule in accordance with the needs. Aspects that need to be considered include aspects of lecturers who teach, courses taught. Manual scheduling tends to take longer and enough accuracy for the schedule maker. To be able to create an optional schedule, an optimization method is required. In this research, will be tested the optimization method in the preparation of the practice schedule that is Genetic Algorithm. Genetic algorithms are a computational approach to solving problems modeled by biological processes of evolution. The parameters of the Genetic Algorithm affecting the course schedule are the number of individuals, the probability of crossover, the probability of mutation and the method of selection, the crossover used. Testing is done by finding the most optimal parameter values of genetic algorithm in lecture schedule. The results show that with the number of generations, the number of individuals, the probability of crossover and the probability of mutation can produce the most optimal schedule.  Keywords: Optimization, Scheduling, Selection, Crossover, Mutation, Genetic Algorithm

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

Optimization of Radial Cams with Translating Roller Follower through Genetic Algorithms

2015 ◽  
Vol 783 ◽  
pp. 83-94
Author(s):  
Alberto Borboni
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Contact Pressure ◽  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Process ◽  
Radius Of Curvature ◽  
Design Problems ◽  
Computational Optimization ◽  
Different Types

In this work, the optimization problem is studied for a planar cam which rotates around its axis and moves a centered translating roller follower. The proposed optimization method is a genetic algorithm. The paper deals with different design problems: the minimization of the pressure angle, the maximization of the radius of curvature and the minimization of the contact pressure. Different types of motion laws are tested to found the most suitable for the computational optimization process.

Mill Gear Box of Intelligent Diagnosis Based on Support Vector Machine Parameters Optimization

2014 ◽  
Vol 697 ◽  
pp. 239-243 ◽  
Author(s):  
Xiao Hui Liu ◽  
Yong Gang Xu ◽  
De Ying Guo ◽  
Fei Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Rolling Mill ◽  
Optimal Parameter ◽  
Structural Parameters ◽  
Parameters Optimization ◽  
Support Vector ◽  
Intelligent Diagnosis ◽  
Gear Box ◽  
Parameter Values

For mill gearbox fault detection problems, and puts forward combining support vector machine (SVM) and genetic algorithm, is applied to rolling mill gear box fault intelligent diagnosis methods. The choice of parameters of support vector machine (SVM) is a very important for the SVM performance evaluation factors. For the selection of structural parameters of support vector machine (SVM) with no theoretical support, select and difficult cases, in order to reduce the SVM in this respect, puts forward the genetic algorithm to optimize parameters, and the algorithm of the model is applied to rolling mill gear box in intelligent diagnosis, using the global searching property of genetic algorithm and support vector machine (SVM) of the optimal parameter values. Results showed that the suitable avoided into local solution optimization, the method to improve the diagnostic accuracy and is a very effective method of parameter optimization, and intelligent diagnosis for rolling mill gear box provides an effective method.

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 Implementation of Genetic Algorithms and Momentum Backpropagation in Classification of Subtype Cells Acute Myeloid Leukimia

2020 ◽  
Vol 14 (2) ◽  
pp. 189
Author(s):  
Dian Mustikaningrum ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
White Blood Cells ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Learning Rate ◽  
Algorithm Optimization ◽  
Selection Parameter ◽  
Acute Myeloid ◽  
Random Trial

 Acute Myeloid Leukimia (AML) is a type of cancer which attacks white blood cells from myeloid. AML subtypes M1, M2, and M3 are affected by the same type of cells called myeloblasts, so it needs more detailed analysis to classify.Momentum Backpropagation  is used to classified. In its application, optimal selection of architecture, learning rate, and momentum is still done by random trial. This is one of the disadvantage of Momentum Backpropagation. This study uses a genetic algorithm (GA) as an optimization method to get the best architecture, learning rate, and momentum of artificial neural network. Genetic algorithms are one of the optimization techniques that emulate the process of biological evolution.The dataset used in this study is numerical feature data resulting from the segmentation of white blood cell images taken from previous studies which has been done by Nurcahya Pradana Taufik Prakisya. Based on these data, an evaluation of the Momentum Backpropagation process was conducted the selection parameter in a random trial with the genetic algorithm. Furthermore, the comparison of accuracy values was carried out as an alternative to the ANN learning method that was able to provide more accurate values with the data used in this study.The results showed that training and testing with genetic algorithm optimization of ANN parameters resulted in an average memorization accuracy of 83.38% and validation accuracy of 94.3%. Whereas in other ways, training and testing with momentum backpropagation random trial resulted in an average memorization accuracy of 76.09% and validation accuracy of 88.22%.

scholarly journals Scheduling Courses using Genetic Algorithms

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Schedule ◽  
Heuristic Method ◽  
Biological Evolution ◽  
Time Data ◽  
Fitness Evaluation ◽  
Class Schedule ◽  
Student College ◽  
Crossover And Mutation

Preparation of courses at every university is done by hand. This method has limitations that often cause collisions schedule. In lectures and lab scheduling frequent collision against the faculty member teaching schedule, collisions on the class schedule and student, college collision course with lab time, the allocation of the use of the rooms were not optimal. Heuristic method of genetic algorithm based on the mechanism of natural selection; it is a process of biological evolution. Genetic algorithms are used to obtain optimal schedule that consists of the initialization process of the population, fitness evaluation, selection, crossover, and mutation. Data used include the teaching of data, the data subjects, the room data and time data retrieved from the database of the Faculty of Computer Science, Universitas Pembangunan Panca Budi. The data in advance through the stages of the process of genetic algorithms to get optimal results The results of this study in the form of a schedule of courses has been optimized so that no error occurred and gaps.

Optimal Concurrent Design Based Upon Distributed Database and Knowledge Base Modeling

2001 ◽  
Author(s):  
F. Zhang ◽  
D. Xue
Keyword(s):  
Knowledge Base ◽  
Optimal Parameter ◽  
Distributed Database ◽  
Optimization Method ◽  
Concurrent Design ◽  
Swarm Optimization ◽  
Multi Level ◽  
Realization Process ◽  
Product Realization ◽  
Parameter Values

Abstract This research introduces an optimal concurrent design approach based upon a previously developed distributed database and knowledge base modeling method. In this approach, the product realization process alternatives and relevant activities are modeled at different locations that are connected through the Internet. The optimal product realization process alternative and its parameter values are identified using a multi-level optimization method. Genetic Programming (GP) and Particle Swarm Optimization (PSO) are employed for identifying the optimal product realization process alternative and the optimal parameter values of the feasible alternatives, respectively.

Robust Adaptable Design of Mechanical Products

2013 ◽  
Author(s):  
Jian Zhang ◽  
Yongliang Chen ◽  
Deyi Xue ◽  
Peihua Gu
Keyword(s):  
Optimal Design ◽  
Functional Performance ◽  
Optimal Parameter ◽  
Design Method ◽  
Optimization Method ◽  
Adaptable Design ◽  
Operation Stage ◽  
Mechanical Products ◽  
Tree Product ◽  
Parameter Values

For an adaptable product, both configuration and parameter values associated with the configuration can be adapted in the product operation stage to satisfy different requirements. This research aims at developing a new design approach to identify the adaptable product whose functional performance is the least sensitive to parameter variations caused by uncertainties. First different configuration candidates in design and different product configurations in operation stage to satisfy design requirements are modeled by a novel hybrid AND-OR tree. Product/operating parameters associated with configurations are also modeled. A two-level optimization method is developed for identifying the optimal design configuration and the parameter values: design configuration optimization for identifying the optimal design configuration and parameter optimization for identifying the optimal parameter values associated with this design configuration. Case study of an adaptable vibratory feeder is developed to demonstrate the effectiveness of the newly developed robust adaptable design method.

MEMS Accelerometer Design Optimization Using Genetic Algorithm

2013 ◽  
Vol 705 ◽  
pp. 288-294 ◽  
Author(s):  
V.S. Krushnasamy ◽  
A. Vimala Juliet
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
High Efficiency ◽  
Microelectromechanical Systems ◽  
Optimal Parameter ◽  
Low Cost ◽  
Complex Nature ◽  
Mems Accelerometer ◽  
Mems Technology ◽  
Parameter Values

MEMS (Microelectromechanical Systems) refers to the technology integrating electrical and mechanical components with feature size of 1~1000 microns. Due to its small size, low cost, low power consumption and high efficiency, MEMS technology has been widely used in many fields.In this paper,the design optimization of MEMS accelerometer is discussed.The main objective of this investigation is to find a optimum design of MEMS,which satisfies a set of given constraints. The accelerometer employs a double folded beam flexure system and the mass being displaced is the proof mass.Due to the complex nature of the problem,a genetic algorithm (GA) is developed for the optimization of MEMS.The GA attempts to minimize the die area and so the four optimal parameter values can be determined. MEMS accelerometers can be used in air-bag deployment systems in automobiles.The experimental results will show the optimal design of MEMS.

Study on a Robust Optimization Model for Project Scheduling Policies

2011 ◽  
Vol 110-116 ◽  
pp. 2866-2871
Author(s):  
Yu Zhang ◽  
Teng Fei Yin
Keyword(s):  
Genetic Algorithm ◽  
Robust Optimization ◽  
Objective Function ◽  
Project Scheduling ◽  
Optimization Model ◽  
Optimal Schedule ◽  
Optimization Method ◽  
Construction Process ◽  
Scheduling Policies ◽  
Project Environment

The genetic algorithm discussed in this paper for project scheduling solution to this problem can be obtained the near optimal schedule programs. This has established the objective function and constraints that have a certain scope; it requires the duration of each process that is determined in advance for enterprises. If the project is more familiar with the history with more experience, and more complete database, the project environment can be controlled well. It can accurately determine the time with the construction plan, construction process and the optimization method with the good trial.

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