scholarly journals Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jing Xu
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Convergence Speed ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Teaching Resources ◽  
College English ◽  
Conflict Problem ◽  
Fitness Value

In this paper, an improved genetic algorithm is designed to solve the above multiobjective optimization problem for the scheduling problem of college English courses. Firstly, a variable-length decimal coding scheme satisfying the same course that can be scheduled at different times, different classrooms, and different teaching weeks per week is proposed, which fully considers the flexibility of classrooms and time arrangements of the course and makes the scheduling problem more reasonable. Secondly, a problem-specific local search operator is designed to accelerate the convergence speed of the algorithm. Finally, under the framework of optimal individual retention, the selection operator, crossover operator, and variation operator are improved. It is experimentally demonstrated that the designed algorithm not only has a faster convergence speed but also improves the diversity of individuals to a certain extent to enhance the search space and jump out of the local optimum. Research shows that the improved genetic algorithm has improved average fitness value and time compared with traditional genetic algorithm. At the same time, the use of the largest fuzzy pattern algorithm effectively solves the conflict problem of college English lesson scheduling, thereby improving the solution of college English lesson scheduling. Through the research of this article, the management system of college English course scheduling has been made more intelligent, and the rational allocation of teaching resources and the completion of education and teaching plans have been improved.

Improved Genetic Algorithm Based on Operation Order Matrix Encoding for Job Shop Scheduling Problem

2011 ◽  
Vol 189-193 ◽  
pp. 4212-4215
Author(s):  
Hong Zhan ◽  
Jian Jun Yang ◽  
Lu Yan Ju
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Order Matrix ◽  
Fitness Value ◽  
Matrix Encoding

This paper presents an improved genetic algorithm for the job shop scheduling problem. We designed a new encoding method based on operation order matrix, a matrix correspond to a chromosome, the value of elements is not repetitive, that means a processing order number in all operations of all jobs. Aiming at the features of the matrix encoding, we designed the crossover and mutation methods based on jobs, and the infeasible solutions are avoided. Through adjusting the computing method of fitness value, the improved genetic algorithm takes on some self adapting capability. The proposed approach is tested on some standard instances and compared with two other approaches. The computation results validate the algorithm is efficient.

scholarly journals Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Ahmad Miftah Fajrin ◽  
Chastine Fatichah
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Local Optimum ◽  
Soft Constraints ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Fitness Value ◽  
Hard And Soft Constraints ◽  
The Right ◽  
Course Timetabling Problem

A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

An Improved Genetic Algorithm for Digital Facility Layout Optimization Oriented Multi-Species and Variable-Batch Production Mode

2013 ◽  
Vol 694-697 ◽  
pp. 3632-3635
Author(s):  
Dao Guo Li ◽  
Zhao Xia Chen
Keyword(s):  
Genetic Algorithm ◽  
Facility Layout ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Batch Production ◽  
Production Mode ◽  
Slow Convergence ◽  
Weak Points

When solving facility layout problem for the digital workshop to optimize the production, the traditional genetic algorithm has its flaws with slow convergence speed and that the accuracy of the optimal solution is not ideal. This paper analyzes those weak points and proposed an improved genetic algorithm according to the characteristics of multi-species and variable-batch production mode. The proposed approach improved the convergence speed and the accuracy of the optimal solution. The presented model of GA also has been tested and verified by simulation.

Application of an Improved Genetic Algorithm with the Search Space Compression in TSP

2013 ◽  
Vol 765-767 ◽  
pp. 687-689
Author(s):  
Yi Song ◽  
Ni Ni Wei
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Random Search ◽  
Search Space ◽  
Traveling Salesman ◽  
Improved Genetic Algorithm ◽  
Space Compression ◽  
Accelerate Convergence ◽  
The Traveling Salesman Problem ◽  
Exponential Relation

The Traveling Salesman Problem is a combinatorial optimization problem, the problem has been shown to belong to the NPC problem, the possible solution of Traveling Salesman Problem and the scale of the cities have the exponential relation, so the more bigger of the scale. In this paper, improve the search process of the genetic algorithm by introducing the idea is to compress the search space. The simulation results show that for solving the TSP, the algorithm can quickly obtain multiple high-quality solutions. It can reduce the blindness of random search and accelerate convergence of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document