solving examination timetabling problem de obaid omar (5 resultados)

Taschenbuch. Condición: Neu. Solving Examination Timetabling Problem By Using Genetic Algorithm | Omar Ibrahim Obaid (u. a.) | Taschenbuch | 184 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659761881 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many researchers over the last decade have established numerous researches and used many methods to handle universities' final examination timetabling problem, such as simulated annealing, tabu search and genetic algorithms. In this Book, Genetic Algorithm (GA) is used to solve the College of Graduate Studies (CoGS) final examination timetabling problem as it is capable of solving many complex problems. This problem belongs to a class of scheduling problems which is highly constrained and known to be NP-hard. The algorithm has been adapted to solve the research problem whose procedure is different from the common algorithm. The Book attempts to find the best solution (best timetable) for CoGS to help UNITEN reduce time and effort for creating examination timetables. New approaches to some of the GAs operators are introduced. These operators include Adaptive Mutation operator that tackles the stasis problem and a crossover scheme called Scattered Crossover to enhance the GA's ability to produce better solutions with best fitness value in lesser generations. 184 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Obaid Omar IbrahimOmar Ibrahim Obaid received B.Sc.Degree in Computer Science from the University of Dyala,Iraq,in 2007.Master of Information Technology, Universiti Tenaga Nasional Malaysia 2011.Working as a Teacher Assistance Colleg.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Many researchers over the last decade have established numerous researches and used many methods to handle universities' final examination timetabling problem, such as simulated annealing, tabu search and genetic algorithms. In this Book, Genetic Algorithm (GA) is used to solve the College of Graduate Studies (CoGS) final examination timetabling problem as it is capable of solving many complex problems. This problem belongs to a class of scheduling problems which is highly constrained and known to be NP-hard. The algorithm has been adapted to solve the research problem whose procedure is different from the common algorithm. The Book attempts to find the best solution (best timetable) for CoGS to help UNITEN reduce time and effort for creating examination timetables. New approaches to some of the GAs operators are introduced. These operators include Adaptive Mutation operator that tackles the stasis problem and a crossover scheme called Scattered Crossover to enhance the GA's ability to produce better solutions with best fitness value in lesser generations.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 184 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many researchers over the last decade have established numerous researches and used many methods to handle universities' final examination timetabling problem, such as simulated annealing, tabu search and genetic algorithms. In this Book, Genetic Algorithm (GA) is used to solve the College of Graduate Studies (CoGS) final examination timetabling problem as it is capable of solving many complex problems. This problem belongs to a class of scheduling problems which is highly constrained and known to be NP-hard. The algorithm has been adapted to solve the research problem whose procedure is different from the common algorithm. The Book attempts to find the best solution (best timetable) for CoGS to help UNITEN reduce time and effort for creating examination timetables. New approaches to some of the GAs operators are introduced. These operators include Adaptive Mutation operator that tackles the stasis problem and a crossover scheme called Scattered Crossover to enhance the GA's ability to produce better solutions with best fitness value in lesser generations.