A Comparative Analysis of Genetic Algorithm with Variable Crossover and Inversion Probability for Operating System Process Scheduling Problem
نویسنده
چکیده
There are numerous approaches have been developed to solve job shop scheduling problems and machines process scheduling problem. Implementation of genetic algorithm for operating system process scheduling is a new idea . Genetic Algorithm is a robust technique for solve process scheduling and optimization problem. There are many type of genetic algorithms have been developed from simple genetic algorithm to complex parallel genetic algorithm. The performance of any genetic algorithm is depend on the proper parameter setting of operators used for a problem under consideration. In this paper we will analyze the performance of modified cross over genetic algorithm for operating system process scheduling problem. As scheduling problem is defined as NP hard problem. Modified genetic algorithm is usefully implemented for operating system process scheduling problem. We saw through the simulation result that when the probability of crossover and inversion operator changes then the performance and convergence state of genetic algorithm is changed considerably.
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