POPULATION SIZE MODELING FOR GA IN TIME-CRITICAL TASK SCHEDULING

2011 ◽  
Vol 22 (03) ◽  
pp. 603-620 ◽  
Author(s):  
WEI SUN
Keyword(s):  
Genetic Algorithms ◽  
Population Size ◽  
Task Scheduling ◽  
Scheduling Problems ◽  
Success Ratio ◽  
Critical Task ◽  
Time Critical

Genetic algorithms (GAs) have been well applied in solving scheduling problems and their performance advantages have also been recognized. However, practitioners are often troubled by parameters setting when they are tuning GAs. Population Size (PS) has been shown to greatly affect the efficiency of GAs. Although some population sizing models exist in the literature, reasonable population sizing for task scheduling is rarely observed. In this paper, based on the PS deciding model proposed by Harik, we present a model to represent the relation between the success ratio and the PS for the GA applied in time-critical task scheduling, in which the efficiency of GAs is more necessitated than in solving other kinds of problems. Our model only needs some parameters easy to know through proper simplifications and approximations. Hence, our model is applicable. Finally, our model is verified through experiments.

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 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 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

Design and Application of Task Scheduling Module Based on Priority Table Query in Wireless Sensor Network Node

2014 ◽  
Vol 536-537 ◽  
pp. 731-734
Author(s):  
Kang Hong Duan ◽  
Li Ting Zhang ◽  
Long Ma ◽  
Hong Xin Zhang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Task Scheduling ◽  
Wireless Sensor ◽  
Network Node ◽  
Driven Systems ◽  
Driven System ◽  
Event Driven ◽  
Time Critical ◽  
Priority Table

Event-driven systems and thread-driven systems are two major design philosophies of operating system in wireless sensor networks. Systems based on multi-threaded are more timeliness than the event-driven systems, which can meet the requirements of time-critical tasks by means of task preemption, while systems based on event-driven are more energy efficient.The article introduces a task scheduling module in event-driven system. The module takes a kind of priority table query method to choose the most important task with highest priority to schedule.It is seen that the module is suitable wireless sensor network.

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