A Crossover Probability Distribution in Genetic Algorithm Under process Scheduling Problem
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Abstract
This paper describe the effect of crossover probability on the convergence of genetic algorithm. Genetic Algorithm have been
designed as general purpose optimization method. The power of the genetic algorithm is depends upon the proper use of their operators such as
selection, crossover and mutation. In this paper we apply the varying crossover probability under the operating system process scheduling
problem. The diversity of the population of the individuals are determine by the probability of crossover in the genetic algorithm. Crossover is
work as a diversifier for the population of solution in the genetic algorithm. The experiments shows that as the probability of cross over increase
more easily the GA adapt to the problem and converge to the best solution state.
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Keywords: Genetic algorithm, NP-hard, Process Scheduling, Crossover
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