ISSN: 2250-3005 - ijcer

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International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8Genetic algorithm maintains a population of candidate solutions that evolves over time and ultimately converges.Individuals in the population are represented with chromosomes. Each individual is numeric fitness value that measureshow well this solution solves the problem. Genetic algorithm contains three operators. The selection operator selects thefittest individuals of the current population to serve as parents of the next generation. The crossover operation choosesrandomly a pair of individuals and exchanges some part of the information. The mutation operator takes an individualrandomly and alters it. As natural genetics, the probability of crossover is usually high, the population evolvesiteratively (in the genetic algorithm terminology, through generation) in order to improve the fitness of its individualsThe structure of genetic algorithm is a loop composed of a selection, followed by a sequence of crossovers andmutations. Probabilities of crossover and mutation are constants and fixed in the beginning. Finally, genetic algorithm isexecuted until some termination condition achieved, such as the number of iterations, execution time, execution time,result stability, etc.3. FRAMEWORK FOR GENETIC ALGORITHM3.1 String representationThe genetic representation of individuals is called genotype. The main criteria in selecting the stringrepresentation for the search node is that the new string generated from the application of genetic operator mustrepresents legal search node for the problem. Every process is present and appears only once in the schedule. The strin grepresentation used in this paper is an array of n × m digits, where n is the number of processes and m shows theprocessor that the process is assigned to. Process index shows the order of execution on that processor.3.2 Initial populationA genetic algorithm starts with a set of individuals called initial population. Most GA -Based algorithmsgenerate initial population randomly. Here, each solution i is generated as follows: one of the unscheduled processes israndomly selected, and then assigned to one of the processors. The important point is the processors are selectedcircularly, it means that they are selected respectively form first to last and then come back to first. This operation isrepeated until all of processes have been assigned. An initial population with size of POPSIZE is generated by repeatingthis method.3.3 Fitness functionThe fitness function is essentially the objective function for the problem. It provides a mean to evaluate thesearch nodes and also controls the reproduction process. For the process scheduling in distributed system problem, wecan consider factor such as load-balancing, processors utilization etc. We take into account this objective in followingequation. Fitness function of schedule T isThis equation shows that a fitter solution has less scheduling length and higher processor utilization.3.4 ReproductionThe reproduction process is typically based on the fitness values of the strings. The principal is that string withhigher fitness value will have higher chance of surviving to the next generation. We calculate fitness of the entireindividual in population, picking the best individual of them and then form a group. We can make a slight modificationto basic reproduction operation by always passing the best string in the current generation to the next generation. Thismodification will increase the performance of genetic algorithm.3.5 CrossoverCrossover is generally used to exchange portions between strings. The crossover operation picks top twostrings from best group. Random task Ti from one of these two strings picks and put on the new string with care ofprecedence relation. If Ti task at same position then Ti is coping on the same place for new string. If we randomlychoose two same parents (A=B) and if one of parents e.g. A the best string we use operator mutation on the second Bstring. Otherwise we mutate the first set and child generate randomly.3.6 MutationCrossover operation of genetic algorithms (GAs) cannot generate quite different offspring from their parentsbecause the acquired information is used to crossover the chromosomes. An alternate operator, mutation, can searchnew areas in contrast to the crossover.Mutation is used to change the genes in a chromosome. Mutation replaces thevalue of a gene with a new value from defined domain for that gene.||Issn 2250-3005(online)|| ||December|| 2012 Page 271
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 83.7 Termination conditionWe can apply multiple choices for termination condition: maximum number of generation, equal fitness forfittest selected chromosomes in respective iterations.Genetic Algorithm{Randomly create an initial populationAssign a fitness value to each individualForm Group of best individualWHILE NOT termination criteria DO{Assign a priority value to the individual in groupChoose two best individual from the group;Crossover surviving individuals;Mutation child;Recorded best individual in group and eliminate worst onein group.}}Genetic Algorithm Steps4 . ConclusionThe paper makes an analysis of existing genetic algorithm and their parameters.Scheduling in distributedoperating systems has a significant role in overall system performance and throughput. This algorithm considers multiobjectives in its solution evaluation and solves the scheduling problem in a way that simultaneously minimizesscheduling length and communication cost, and maximizes average processor utilization and load -balance. Most ofexisting approaches tend to focus on one of the objectives. Experimental results prove that our proposed algorithm tendto focus on more objective simultaneously and optimize them.References[1] Dr Apurva Shah And Vinay Hansora “A Modified Genetic Algorithm For Process Scheduling In DistributedSystem” IJCA Special Issue On “Artificial Intelligence Techniques - Novel Approaches & PracticalApplications”AIT, 2011[2] M. Nikravan And M.H.Kashani “ A Genetic Algorithm For Process Scheduling In Distributed OperatingSystems Considering Load Balancing”, European Conference On Modelling And Simulation.[3] C.C.Shen, & W.H.Tsai, “A Graph Matching Approach To Optimal Task Assignment In Distributed ComputingUsing A Minimax Criterion”, IEEE Trans. On Computers, 34(3), 1985, 197-203.[4] G.L.Park, “Performance Evaluation Of A List Scheduling Algorithm In Distributed Memory MultiprocessorSystems”, International Journal Of Future Generation Computer Systems 20, 2004, 249-256[5] L.M.Schmitt, “Fundamental Study Theory Of Genetic Algorithms” , International Journal Of Modelling AndSimulation Theoretical Computer Science 259, 2001, 1 – 61.[6] W.Yao, J.Yao, & B.Li, “Main Sequences Genetic Scheduling For Multiprocessor Systems Using TaskDuplication”, International Journal Of Microprocessors And Microsystems, 28, 2004, 85-94.[7] A.T. Haghighat, K. Faez, M. Dehghan, A. Mowlaei, & Y. Ghahremani, “Multicast Routing With MultipleConstraints In High-Speed networks based on genetic algorithms” , In ICCC 2002 Conf., India, 2002, 243–249.||Issn 2250-3005(online)|| ||December|| 2012 Page 272
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