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.

scholarly journals Design and Application of an Improved Genetic Algorithm to a Class Scheduling System

2021 ◽  
Vol 16 (01) ◽  
pp. 44
Author(s):  
Xiangliu Chen ◽  
Xiao-Guang Yue ◽  
Rita Yi Man Li ◽  
Ainur Zhumadillayeva ◽  
Ruru Liu
Keyword(s):  
Genetic Algorithm ◽  
Educational Administration ◽  
Strong Support ◽  
Colleges And Universities ◽  
Hill Climbing ◽  
Teaching Management ◽  
Scheduling System ◽  
Course Scheduling ◽  
Class Scheduling ◽  
Classroom Resources

The current expansion of national colleges and universities or the increase in the number of enrolments requires teaching management to ensure the quality of teaching. The problem of scheduling is a very complicated prob-lem in teaching management, and there are many restrictions. If the number of courses scheduled is large, it will be necessary to repeat the experiment and make adjustments. This kind of work is difficult to accomplish accu-rately by manpower. Moreover, for a comprehensive university, there are many subjects, many professional settings, limited classroom resources, limited multimedia classroom resources, and other factors that limit and constrain the results of class scheduling. Such a large data volume and com-plicated workforce are difficult to complete accurately. Therefore, manpow-er scheduling cannot meet the needs of the educational administration of colleges and universities. Today, computer technology is highly developed. It is very economical to use software technology to design a course schedul-ing system and let the computer complete this demanding and rigorous work. Common course scheduling systems mainly include hill climbing al-gorithms, tabu search algorithms, ant colony algorithms, and simulated an-nealing algorithms. These algorithms have certain shortcomings. In this re-search, we investigated the mutation genetic algorithm and applied the algo-rithm to the student’s scheduling system. Finally, we tested the running speed and accuracy of the system. We found that the algorithm worked well in the course scheduling system and provided strong support for solving the tedious scheduling work of the educational administration staff.

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.

Solving Lot-Sizing and Sequencing Integrated Optimization Problems in Mixed-Model Production Systems

2012 ◽  
Vol 472-475 ◽  
pp. 3335-3338 ◽  
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Production Systems ◽  
Optimization Problems ◽  
Lot Sizing ◽  
Flow Line ◽  
Adaptive Genetic Algorithm ◽  
Integrated Optimization ◽  
Comparison Results ◽  
Mixed Model Assembly Line

This paper is concerned about the lot-sizing and sequencing integrated optimization problems in mixed-model production systems composed of one mixed-model assembly line and one fabrication flow line. The optimization objective is minimizing the total makespan cost in regular hour, the overtime makespan cost and the holding cost in the whole production system. The mathematic models are presented and an adaptive genetic algorithm is developed for solving this problem. A traditional genetic algorithm is also designed for testing the optimization performance of the adaptive genetic algorithm. Computational experiments are conducted and the optimization results are compared between the above two algorithms. The comparison results show that the adaptive genetic algorithm is a feasible and effective method for solving this problem.

scholarly journals Limited adaptive genetic algorithm for inner-plant economical operation of hydropower station

2012 ◽  
Vol 44 (4) ◽  
pp. 583-599 ◽  
Author(s):  
Jiao Zheng ◽  
Kan Yang ◽  
Xiuyuan Lu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Convergence Speed ◽  
Hydropower Station ◽  
Adaptive Genetic Algorithm ◽  
Parameter Method ◽  
Initial Population ◽  
Solution Strategy ◽  
Adaptive Parameter ◽  
Solution Generation

A limited adaptive genetic algorithm (LAGA) is proposed in the paper for inner-plant economical operation of a hydropower station. In the LAGA, limited solution strategy, with the feasible solution generation method for generating an initial population and the limited perturbation mutation operator, is presented to avoid hydro units operating in cavitation–vibration regions. The adaptive probabilities of crossover and mutation are introduced to improve the convergence speed of the genetic algorithm (GA). Furthermore, the performance of the limited solution strategy and the adaptive parameter controlling improvement are checked against the historical methods, and the results of simulating inner-plant economical operation of the Three Gorges hydropower station demonstrate the effectiveness of the proposed approach. First, the limited solution strategy can support the safety operations of hydro units by avoiding cavitation–vibration region operations, and it achieves a better solution, because the non-negative fitness function is achieved. Second, the adaptive parameter method is shown to have better performance than other methods, because it realizes the twin goals of maintaining diversity in the population and advancing the convergence speed of GA. Thus, the LAGA is feasible and effective in optimizing inner-plant economical operation of hydropower stations.

Research on Multi-Objective Reservoir Optimal Operation Based on Adaptive Genetic Algorithms

2014 ◽  
Vol 926-930 ◽  
pp. 2042-2045 ◽  
Author(s):  
Jiang Chang ◽  
Guang Wen Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Operation ◽  
Adaptive Genetic Algorithm ◽  
Control Parameters ◽  
Multi Objective ◽  
Adaptive Genetic Algorithms ◽  
Individual Fitness ◽  
Dispersion Degree ◽  
Poor Convergence ◽  
Standard Genetic Algorithm

The comprehensive reservoir scheduling is based on multi-objective reservoir operation, should try to coordinate between each target scheduling of progression. In this paper, based on the characteristics of scheduling period, this paper proposes a reservoir optimal operation based on adaptive genetic algorithm solution of the problem. Because of the adaptive genetic algorithm can during evolution according to individual fitness and dispersion degree of genetic control parameters are adjusted automatically, can better solve encountered in the application of the standard genetic algorithm in the problem of poor convergence and prematurity.

An Improved Fuzzy Adaptive Genetic Algorithm for Function Optimization

2011 ◽  
Vol 403-408 ◽  
pp. 2598-2601
Author(s):  
Lan Yao ◽  
Yu Lian Jiang ◽  
Jian Xiao
Keyword(s):  
Genetic Algorithm ◽  
Population Diversity ◽  
Function Optimization ◽  
Adaptive Genetic Algorithm ◽  
Adaptive Selection ◽  
Mutation Probability ◽  
Individual Fitness ◽  
Fitness Value ◽  
Crossover Probability ◽  
Fuzzy Adaptive

The critical operators for genetic algorithms to avoid premature and improve globe convergence is the adaptive selection of crossover probability and mutation probability. This paper proposed an improved fuzzy adaptive genetic algorithm in which the variance of population and individual fitness value are used to measure the overall population diversity and individual difference, meanwhile, both of them are applied to design fuzzy reference system for adaptively estimation of crossover probability and mutation probability. Simulation results of function optimization show that the new algorithm can converge faster and is more effective at avoiding premature convergence in comparison with standard genetic algorithm.

Study on Technique of Tolerance Optimal Allocation Based on Adaptive Genetic Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 1436-1440
Author(s):  
Yan Ting Ai ◽  
Jing Tian ◽  
Feng Ling Zhang ◽  
Xue Zhai ◽  
Shu Sheng Bai
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Cost Minimization ◽  
Tolerance Allocation ◽  
Production Costs ◽  
Adaptive Genetic Algorithm ◽  
Manufacturing Cost ◽  
Crossover Operator ◽  
Individual Fitness ◽  
Rational Allocation

The rational allocation of tolerances is the key to reduce production costs and guarantee the performance. In this paper, in the premise of ensuring the performance, manufacturing cost minimization of assembly parts is set as objective function, and an adaptive genetic algorithm is proposed to optimize the design of tolerance allocation. The adaptive mechanism is introduced mainly for crossover operator and mutation operator to overcome the traditional adaptive genetic algorithm’s easy "premature" shortcomings according to individual fitness of population. And a penalty function is used to handle constraints of assembly dimension chain. Finally, using the algorithm to optimize assembly chain tolerances of a gear reducer shaft, the effectiveness of the adaptive genetic algorithm to optimize tolerance allocation has been verified.

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