INTRODUCTION
ABSTRACT FINAL EXAMINATION TIMETABLE
The current situation that we have at Unisza Campus Besut is timetable allocation for final exam done manually by academic administer. The problem that we are facing we need several people to look for the timetable allocation which it took almost a few days or week to complete task and sometimes leads to human error. It is important to avoid the redundancy of time or date for final exam timetable so that there's no student take a final exam at the same period in a same day.
The proposed method or technique that been used for the final exam timetable allocation is genetic algorithm(GA) These method to find a optimal solution of solving timetable by using a real student data of unisza student. A GA is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution. The algorithm repeatedly modifies a population of individual solutions.
The result for these solution is minimize the time taken in allocate timetable because it been done auto generated and more efficient.
The proposed method or technique that been used for the final exam timetable allocation is genetic algorithm(GA) These method to find a optimal solution of solving timetable by using a real student data of unisza student. A GA is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution. The algorithm repeatedly modifies a population of individual solutions.
The result for these solution is minimize the time taken in allocate timetable because it been done auto generated and more efficient.
INTRODUCTION FINAL EXAMINATION TIMETABLE
This section introduced the project develop system which is Final Examination timetable Allocation using Genetic Algorithm (GA). This chapter provides a basic overview of the whole proposed system which include the background, problem statement, objectives, scope, limitation of work, expected result or outcomes and project planning. Background discuss the general overview of the system while problem statement discuss the issues that happen before develop system. Objective discuss the main goal of the system. Limitation of work state about what is the constraint happen to developer to develop these system. Expected result or outcomes is the expected functionality of the system after fully developed. Lastly, project planning is the Gantt chart for the project development.
OBJECTIVES FINAL EXAMINATION TIMETABLE
1To develop and design the system in term of Academic Management using genetic algorithm is convenience in managing final exam timetable with the aid of web based technology
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2To implement genetic algorithm method in final exam timetable allocation in order to improve and help the Academic Management to manage the final exam timetable more efficiently.
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3To implement genetic algorithm method in final exam timetable allocation in order to improve and help the Academic Management to manage the final exam timetable more efficiently.
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METHODOLOGY
FRAMEWORK FINAL EXAMINATION TIMETABLE
Academic Manager needs to login into the system then academic manager give the input about academic management, facilities details and exam details to the system. While for the Faculty need to login into the system and give input program details, subject details, program requirement details, lecture details, lecture details and the output will be given to generate timetables. Then the system will generate using Genetic Algorithm. All the information will be stored in the database.
ALGORITHM FINAL EXAMINATION TIMETABLE
Genetic algorithm (GA) are a class of powerful and general purpose search algorithm based which model problem based on the principles from Darwin biological evolution. In 1975, it has been formalized by John Holland and growing in popularity, Colorni et al Rawat and Rajami described particularly for solving problem with a large irregular search space. Maintained a population of feasible timetable. To form the basis of next iteration or generation the fittest timetable are selected. Basic operators such as selection, mutation and crossover are applied to get the best result. A population are initialized, the evaluation and the genetic operator been implemented and controlled by a program written in PHP. Each chromosome will be large, for each class to schedule holding by allele. A room and time slots to each class assign by the genetic algorithm and the number of constraint violation would be a function its fitness. Initialization population is generated randomly. The figure above describe the cycle of genetic algorithm.
GA TECHNIQUES FINAL EXAMINATION TIMETABLE
Genetic Algorithm working cycle that has seven steps. First step is Initialization of population which is program, subject, lecturer, lecture, facilities. Second step is evaluation the fitness value and then select the population based on fitness value. Next, crossover and create a new population (mutation). After that, evaluate the child population. If it already satisfied the criteria it will produce the output if not it will goes to selection again.
RESULT
CONCLUSION
1Auto generated timetable process help Academic Management save a lot of time creating and managing final exam time table of the institutes
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2A more efficient and reliable timetable for final exam can be achieved
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3Assign teachers and classroom for periods and optimize allocation of resources in the manner possible
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