An Effect of Single Point Inversion Operator on the Performance of Genetic Algorithm for Operating System Process Scheduling Problem
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Abstract
Genetic algorithm is a Mata-heuristic search technique; this technique is based on the Darwin theory of Natural
Selection. This algorithm is work with some finite set of population. The population contain set of individual which represent the
solution . Each member of the population is represented by a string written over fixed alphabets and also each member has a merit
value associated with it, which represent its suitability for the problem under consideration. There are many coding techniques
have been implemented for genetic algorithm. In this paper we consider the operating system process scheduling problem.
Scheduling problem is considered to be the NP hard problem. We have used Permutation coding to represent the solution
candidate. Permutation coding technique is well suited to process scheduling problem. In permutation coding scheme we can use
inversion operator .The performance of the genetic algorithm is greatly depends upon the proper use of genetic operator . The
premature convergence of genetic algorithm is diverse effect of improper application of operators. We have examined the effect of
varying inversion probability on the performance of genetic algorithm. As inversion operator is the explorative operator because it
avoid premature convergence of genetic algorithm. It has been observed that when GA converge to local optima and cross over is
not sufficient to diversify the population and also GA comes out from the Premature convergence towards local optima inversion
operation diversify the population . Varying probability of inversion operator has positive and negative effect on the GA
performance. If inversion probability is very high and very low then GA performance is degraded, so proper inversion probability
is applied which neither can be too high or too low. In this paper we have considered four cases of varying inversion probability
and find out that varying inversion probability have effect on the performance of the genetic algorithm
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Keywords: Genetic algorithm, NP-hard, Process, Scheduling, Inversion.
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