scholarly journals Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling

2020 ◽  
Author(s):  
Jiawei LI ◽  
Tad Gonsalves
Keyword(s):  
Genetic Algorithm ◽  
Real Data ◽  
Initial Population ◽  
Exam Timetabling ◽  
Timetabling Problem ◽  
Training Approach ◽  
Chromosome Representation ◽  
Genetic Algorithm Approach ◽  
Hard And Soft Constraints ◽  
Microsoft Office

This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

scholarly journals Modeling the Municipal Waste Collection Using Genetic Algorithms

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 513
Author(s):  
Elisabete Alberdi ◽  
Leire Urrutia ◽  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Real Data ◽  
Municipal Waste ◽  
Neighborhood Search ◽  
Initial Population ◽  
Waste Collection ◽  
Reduction Of Emissions ◽  
Apparent Reasons ◽  
Unsolved Issue

Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs.

scholarly journals Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
ByoungWook Kim ◽  
JaMee Kim ◽  
WonGyu Lee
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Item Response ◽  
Input Data ◽  
Real Data ◽  
Initial Population ◽  
Response Data ◽  
Item Parameters ◽  
Leibler Divergence ◽  
Simulation Input

The item response data is thenm-dimensional data based on the responses made bymexaminees to the questionnaire consisting ofnitems. It is used to estimate the ability of examinees and item parameters in educational evaluation. For estimates to be valid, the simulation input data must reflect reality. This paper presents the effective combination of the genetic algorithm (GA) and Monte Carlo methods for the generation of item response data as simulation input data similar to real data. To this end, we generated four types of item response data using Monte Carlo and the GA and evaluated how similarly the generated item response data represents the real item response data with the item parameters (item difficulty and discrimination). We adopt two types of measurement, which are root mean square error and Kullback-Leibler divergence, for comparison of item parameters between real data and four types of generated data. The results show that applying the GA to initial population generated by Monte Carlo is the most effective in generating item response data that is most similar to real item response data. This study is meaningful in that we found that the GA contributes to the generation of more realistic simulation input data.

The Simple Genetic Algorithm Approach for Optimization of Nesting of Sheet Metal Parts in Blanking Operation

2015 ◽  
Vol 14 (01) ◽  
pp. 41-53 ◽  
Author(s):  
K. Ramesh ◽  
N. Baskar
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Optimization Problem ◽  
Cutting Stock ◽  
Metal Parts ◽  
Sheet Metal Parts ◽  
Chromosome Representation ◽  
Genetic Algorithm Approach ◽  
Hard Problems ◽  
Maximum Utilization

The two-dimensional (2D) cutting stock is a common problem arising in the sheet metal industries, lock industries, textile industries, etc. Here, the problem is to reduce the wastage in order to increase the profit. This problem is also called as the general 2D problem or NP hard problems. The choice of chromosome representation in genetic algorithm (GA) depends on the variables of the optimization problem being solved. The main objectives of the work are the maximum utilization of part in the sheet and also minimizing the wastage.

scholarly journals An genetic algorithm approach for profiling computational performance measures

2015 ◽  
Author(s):  
Matheus Sant Ana Lima
Keyword(s):  
Genetic Algorithm ◽  
Performance Measures ◽  
Fitness Function ◽  
Initial Population ◽  
Roulette Wheel Selection ◽  
Computational Performance ◽  
Genetic Algorithm Approach ◽  
Clustering Data ◽  
The Individual ◽  
Elitist Selection

This paper present a Genetic Algorithm(GA) approach for clustering data metric of computational performance measures collected from vmstat and sar tools. The proposed work models the genes, chromosomes, species and environment based on the dataset and presents an algorithm to analyze patterns and classify the records. The proposed method submits the performance information to an N-Dimensional Histogram in order to obtain the distribution of data that is used as input to the cluster initialization. The individual from each species undergoes successive crossover, mutation and selection operations to improve and evolve the initial population to a given environment state. The fitness-function is determined by the N-Dimensional Euclidean distance. The selection method is based on the Roulette-Wheel Selection, Elitist Selection and Truncation Selection. The results presented were obtained from seven test scenarios.

scholarly journals Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

2018 ◽  
Vol 13 (06) ◽  
pp. 29
Author(s):  
Wang Wen-jing
Keyword(s):  
Genetic Algorithm ◽  
Model Building ◽  
Colleges And Universities ◽  
Adaptive Genetic Algorithm ◽  
Initial Population ◽  
Teaching Management ◽  
Course Scheduling ◽  
Individual Fitness ◽  
Comparison Results ◽  
Hard And Soft Constraints

Traditional artificial intelligence and computer-aided course scheduling schemes can no longer meet the increasing demands caused by the informatization of teaching management in colleges and universities. To address this problem, this study designed an improved adaptive genetic algorithm that is based on hard and soft constraints for course scheduling. First, the mathematical model of the genetic algorithm was established. The combination of time, teacher, and course number was regarded as the gene coding. The weekly course schedule of each class was a chromosome, and the course schedule of the entire school was the initial population. The fitness was designed according to the priority of each class, curriculum dispersion, and teacher satisfaction. Local columns between individuals were selected through the roulette principle for a variation of crossover and random columns. Iterative calculation was implemented on the basis of the default mutation and crossover rates to study the optimal course scheduling scheme. Experimental results demonstrate that the improved adaptive genetic algorithm is superior to the original genetic algorithm. When the number of iterations is 150, population evolution is optimal and the fitness does not increase. When the population size is 150 classes, the average scheduling time is the shortest. The basic, adaptive, and improved adaptive genetic algorithms are compared in terms of the number of average iterations required for convergence, maximum individual fitness, and average individual fitness. Comparison results show that the improved adaptive genetic algorithm is superior to the two other algorithms. This study provides references for the model building and evaluation of course scheduling in colleges and universities.

Scheduling for the Flexible Job-Shop Problem Based on Genetic Algorithm (GA)

2012 ◽  
Vol 457-458 ◽  
pp. 616-619
Author(s):  
Shun Cheng Fan ◽  
Jin Feng Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Initial Population ◽  
Flexible Job Shop ◽  
Chromosome Representation ◽  
Local Optimal Solution ◽  
Crossover And Mutation ◽  
Flexible Job Shop Problem

In this paper, we analyze the characteristics of the flexible job-shop scheduling problem(FJSP). A novel genetic algorithm is elaborated to solve the FJSP. An improved chromosome representation is used to conveniently represent a solution of the FJSP. Initial population is generated randomly. The relevant selection, crossover and mutation operation is also designed. It jumped from the local optimal solution, and the search area of solution is improved. Finally, the algorithm is tested on instances of 4 jobs and 6 machines. Computational results prove the proposed genetic algorithm effective for solving the FJSP.

scholarly journals Genetic Algorithm Based Timetabling Program

2020 ◽  
Vol 2 (2) ◽  
pp. 20-31
Author(s):  
Mutlu YAPICI ◽  
Ömer Faruk BAY
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Initial Population ◽  
Test Results ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Resource Limitations ◽  
Real World Problem ◽  
Solution Methods ◽  
Course Timetabling Problem

Course Timetabling Problem is concerned with assigning a number of courses and instructors to classrooms by taking the constraints into consideration. Generally, this problem is typically resolved manually; and due to the large variety of constraints, resource limitations and complicated human factors involved, it takes a lot of time and manpower. It is considered as one of the most time-consuming problems faced by universities and colleges today. In this study, we aimed to develop a genetic algorithm-based timetabling software to bring a solution to course timetabling problem, which is a real world problem. This software allows constraints to be entered easily and allows that optimal solutions are found. To find the most suitable solution for optimization, two different solution methods, a full-genetic algorithm and a partial-genetic algorithm, were tested. Test results showed that when we start the full genetic algorithms from randomly generated initial population, it takes quite some time to obtain the appropriate solution. With the partial-genetic algorithm, an optimal solution was achieved much more quickly than the full genetic algorithm.

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