Papers by Ashwani Kaushik
2011 International Conference on Communication Systems and Network Technologies, 2011
Present study in the paper is concerned with the development of new genetic operators to optimize... more Present study in the paper is concerned with the development of new genetic operators to optimize the performance of the system. To improve the production facilities, a set of jobs are executed on the set of machines. For better performance there are large numbers of constraints. Process scheduling theory has been developed to meet all side constraints. Process Schedule is done in such a way that the resulting solution minimizes the given objective function. Many variants of the basic scheduling problem can be formulated by differentiating between machine environments, side constraints and objective functions. Genetic algorithm have been applied to OSPSP. The study shows that proposed operators shows better results
Journal of Global Research in Computer Science, 2011
An important assumption to maximize the performance of genetic algorithm is to study the converge... more An important assumption to maximize the performance of genetic algorithm is to study the convergence state of genetic algorithm. Genetic algorithm is a Mata-heuristic search technique; this technique is based on the Darwin theory of Natural Selection. The important property of this algorithm is that it has worked on multiple state of solution. This algorithm is work with some finite set of population. The population contains 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 study the effect of crossover and inversion probability on the convergence of genetic algorithm .The convergence of genetic algorithm is depends upon the parameter setting of genetic algorithm.
This paper present the implementation of genetic algorithm for operating system process schedulin... more This paper present the implementation of genetic algorithm for operating system process scheduling. Scheduling in operating systems has a significant role in overall system performance and throughput. An efficient scheduling is vital for system performance. The scheduling is ...
A multibody dynamics system simulation code HOTINT is reviewed in this paper. The HOTINT software... more A multibody dynamics system simulation code HOTINT is reviewed in this paper. The HOTINT software has been consistently used for different research purpose during past years with different features as compared to other commercial and research software. Differential algebraic equations of motion can be solved with the help of this simulation software. These problems are in the form of first or second order differential equations, algebraic equations and inequalities, which may or may not be nonlinear. The main objective of this review is simulation study of HOTINT& multibody dynamics systems with some details developed by different researchers.
Genetic algorithm is a Mata-heuristic search technique; this technique is based on the Darwin the... more 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
An important assumption to maximize the performance of genetic algorithm is to study the converge... more An important assumption to maximize the performance of genetic algorithm is to study the convergence state of genetic algorithm. Genetic algorithm is a Mata-heuristic search technique; this technique is based on the Darwin theory of Natural Selection. The important property of this algorithm is that it has worked on multiple state of solution. This algorithm is work with some finite set of population. The population contains 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 study the effect of crossover and inversion probability on the convergence of genetic algorithm .The convergence of genetic algorithm is depends upon the parameter setting of genetic algorithm.
There are numerous approach to scheduling problems. Scheduling problem is a NP hard problem. This... more There are numerous approach to scheduling problems. Scheduling problem is a NP hard problem. This paper present the modified cross over genetic algorithm approach to single processor process scheduling. Single processor machine efficiency depends upon the efficient scheduling of single processor. The work present in this paper shows that processor scheduling can be optimize by apply efficient scheduling algorithm. Extensive computational experiments are carried out to get optimum efficiency of the proposed algorithm. Efficiency of the scheduling algorithm can be examined on number of factors. In this paper we consider average waiting time, turn around time and weighted turn around time as an optimization criteria of scheduling algorithms. Simulation in this paper evaluates the performance and efficiency of proposed algorithm. Experimental results indicates that MCOGA shoes better results than that of traditional scheduling algorithms.
This paper describe the effect of crossover probability on the convergence of genetic algorithm. ... more 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.
This paper present the implementation of genetic algorithm for operating system process schedulin... more This paper present the implementation of genetic algorithm for operating system process scheduling. Scheduling in operating systems has a significant role in overall system performance and throughput. An efficient scheduling is vital for system performance. The scheduling is considered as NP hard problem .In this paper , we use the power of genetic algorithm to provide the efficient process scheduling. the aim is to obtain an efficient scheduler to allocate and schedule the process to CPU. we will evaluate the performance and efficiency of the proposed algorithm using simulation results.
Renewable & Sustainable Energy Reviews, 2010
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Papers by Ashwani Kaushik