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E-learning_Timetable_Generator_Using_Genetic_Algorithms -...

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1 E-learning Timetable Generator Using Genetic Algorithms Eng. AHMED HAMDI ABU ABSA University of Palestine E-mail: [email protected] P.O.Box:322 Gaza-Palestine Tel. 082880001 Fax:082880006 Dr. SANA'A WAFA Al-SAYEGH University of Palestine E-mail: [email protected] P.O.Box:322 Gaza-Palestine Tel. 082880001 Fax:082880006 ABSTRACT In this paper we explain the details of the implementation of a computer program which employs Genetic Algorithms (GAs) in the quest for an optimal lecture timetable generator. GA theory is covered with emphasis on less fully encoded systems employing non- genetic operators. The field of Automated Timetabling is also explored. A timetable is explained as, essentially, a schedule with constraints placed upon it. The program, written in java, that has a good object oriented to do it, and it has the special libraries to deal with genetic algorithm which will be used for the implementation. In a simplified university timetable problem it consistently evolves constraint violation free timetables. The effects of altered mutation rate and population size are tested. It is seen that the GA could be improved by the further incorporation of repair strategies, and is readily scalable to the complete timetabling problem. KEY WORDS : AI, Genetic Algorithm, Timetable Generator, E-learning Application. 1. INTRODUCTION Paradigms of design theory are defined for the GA by natural selection as a quantitative measurement of life and ability to contribute to the reproduction process. The second property is the random genetic difference – mutation of the genetic information (this is important for small populations. The third typical property is the process of reproduction, which is the opportunity for genetic information exchange – crossing, which is how the algorithm to be effective. [1] The basic properties of design algorithms are defined as follows: Algorithm design can be an effective tool for optimization of processes. (They are used in cases, in which the aim is a searching of global extreme with a lot of several local extremes of the same type exists). Design algorithms give the tools for monitoring of properties of k -time generation. It can be monitored some changes, which are made by the changes, and by this way of comparison of results of experiments without the changes, and with the change of information in “chromosome”. Design algorithms give the opportunity to “social relations” monitoring between the generations on the basis of information changes of “chromosome”. [1] 2. PREVIOUS WORK The initiator of research in GAs is John Holland. Holland published the book "Adaptation in Natural and Artificial Systems", and then on many dissertations and papers began to be published by different researchers. In 1985 the general approach began to receive wide attention. Two formal conferences on GAs: [2,4] "Proceedings of the International Conference on Genetic Algorithms and their Applications" It is held in odd years from 1985 in United States and "Parallel Problem Solving from Nature" It is held in even years from 1990 in Europe. In addition, the theory-oriented
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