Performance evaluation of simulated annealing and genetic algorithm in solving examination timetabling problem

2012 ◽  
Vol 7 (17) ◽  
Author(s):  
Oyeleye, C. Akinwale
Keyword(s):  
Genetic Algorithm ◽  
Performance Evaluation ◽  
Simulated Annealing ◽  
Examination Timetabling ◽  
Timetabling Problem

scholarly journals Performance Evaluation of Genetic Algorithm and Simulated Annealing in Solving Kirkman Schoolgirl Problem.

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Christopher A Oyeleye ◽  
Victoria O Dayo-Ajayi ◽  
Emmanuel Abiodun ◽  
Alabi O Bello
Keyword(s):  
Genetic Algorithm ◽  
Performance Evaluation ◽  
Simulated Annealing ◽  
Software Complexity ◽  
Program Effort

This paper provides performance evaluation of Genetic Algorithm and Simulated Annealing in view of their software complexity and Simulation runtime. Kirkman Schoolgirl is about arranging fifteen schoolgirls into five triplets in a week with a distinct constraint of no two schoolgirl must walk together in a week. The developed model was simulated using Matlab version R2015a. The performance evaluation of both Genetic algorithm and Simulated Annealing was carried out in terms of program size, program volume, program effort and the intelligent content of the program. The results obtained show that the runtime for GA and SA are 11.23sec and 6.20sec respectively. The program size for GA and SA are 2.01kb and 2.21kb, respectively. The lines of code for GA and SA are 324 and 404, respectively. The program volume for GA and SA are 1121.58 and 3127.92, respectively. The program effort for GA and SA are 135021.70 and 30633.26 respectively, while the intelligent content of the program for GA and SA are 72.461 and 41.06, respectively. Both Algorithms are good solvers, however it can be concluded that Genetic Algorithm outperformed simulated Annealing in most of the evaluated parameters. Keywords:   Genetic Algorithm, Simulated Annealing, Kirkman Schoolgirl, software complexity and simulation runtime

scholarly journals Automated university lecture timetable using Heuristic Approach

2020 ◽  
Vol 39 (1) ◽  
pp. 1-14 ◽  
Author(s):  
A.M. Hambali ◽  
Y.A. Olasupo ◽  
M. Dalhatu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Heuristic Approach ◽  
Space Complexity ◽  
Tertiary Institution ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Hard Constraints ◽  
Course Timetabling Problem

There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses. Keywords: Heuristic approach (HA), Genetic algorithm (GA), Course Timetabling, Space Complexity.

scholarly journals A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yu Lei ◽  
Maoguo Gong ◽  
Licheng Jiao ◽  
Wei Li ◽  
Yi Zuo ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Memetic Algorithm ◽  
Population Diversity ◽  
Genetic Operators ◽  
Examination Timetabling ◽  
Timetabling Problem ◽  
Uncapacitated Examination Timetabling Problem

A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.

scholarly journals Genetic Algorithm for Solving Multi-Objective Optimization in Examination Timetabling Problem

2021 ◽  
Vol 16 (11) ◽  
pp. 4
Author(s):  
Son Tung Ngo ◽  
Jafreezal B Jaafar ◽  
Izzatdin Abdul Aziz ◽  
Giang Hoang Nguyen ◽  
Anh Ngoc Bui
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Hard Problem ◽  
Multi Objective Optimization ◽  
Time Requirement ◽  
Examination Timetabling ◽  
Education Management ◽  
Timetabling Problem ◽  
Multi Objective ◽  
Np Hard Problem

Examination timetabling is one of 3 critical timetabling jobs besides enrollment timetabling and teaching assignment. After a semester, scheduling examinations is not always an easy job in education management, especially for many data. The timetabling problem is an optimization and Np-hard problem. In this study, we build a multi-objective optimizer to create exam schedules for more than 2500 students. Our model aims to optimize the material costs while ensuring the dignity of the exam and students' convenience while considering the rooms' design, the time requirement of each exam, which involves rules and policy constraints. We propose a programmatic compromise to approach the maximum tar-get optimization model and solve it using the Genetic Algorithm. The results show the effectiveness of the introduced algorithm.

Sign in / Sign up

Export Citation Format

Share Document