The fitness function. Each individual string will have a

The work was proposed to solve various dynamicload-balancing problem using genetic algorithm 8a. Centralized Geneticalgorithm based method is used to perform load balancing operation wheresliding window technique is used to initialize a population of possiblesolutions. The objective function such as maxspan, average utilization andnumber of acceptable processor queues are used to obtain fitness function.After completing roulette wheel method for selection, cyclic crossover and swapmutation operation, the population of strings will be evaluated using thefitness function. Each individual string will have a new fitness value and anew probability of surviving into the next generation.

The proposed dynamicload-balancing approach developed using genetic algorithm minimizeinterprocessor communication and total completion time, and provide better performanceby maximum processor utilization. The genetic-based load-balancing algorithm iscompared with the first-fit heuristic in terms of threshold policies,information exchange criteria, interprocessor communication and other issues ofload balancing.A method based on genetic algorithms for schedulingand load balancing in parallel heterogeneous multi-processor systems wasintroduced 1a. Permutations encoding is used to encode chromosomes. Thefitness function is calculated from the random chromosomes generated in theinitial population. Genetic Algorithm uses rotating roulette wheel strategyselection. One point crossover is used in crossover operation and randomlyselecting two tasks and swapping them to perform mutation operation.

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Thesimulations were performed using Matlab software for Genetic Algorithm, LPT,SPT and FIFO. The result obtained is Genetic Algorithm provides better systemutilization with minimum response time compared to that of LPT, SPT and FIFO.Genetic Algorithm for scheduling and load balancing can provide similar resultsin different scales, and proves the robustness of the Genetic algorithm indifferent scales.            Theproposed dynamic load balancing algorithm approach for heterogeneousdistributed system 2a. The sliding window technique is used to initiate apopulation of possible solution.

Centralized genetic algorithm perform loadbalancing operation. LERT-MW method are used to schedule the job from and atrandom interval of time genetic algorithm is applied. Selection, crossover andmutation operation are performed, and fitness function is based on objectivefunction.

The simulation is done using Matlab 6.0 where comparison of GA isdone with LERT-MWM algorithm. Total computation time reduces in GA compared tothat of LERT_MWM algorithm when the number of task increase with betterutilization of resource. The proposed a genetic algorithm method 7a withsuitable fitness function for efficient sender-initiated load balancing indistributed system. An overloaded processors (sender) initiate dynamic loadbalancing by sending excess task to an under loaded processors (receiver).Genetic operators are applied to the population of a processor in distributedsystem. Sender send the request message to the processors.

After receiving therequest message, processors send back accept message or reject message based onits own CPU queue length. Genetic algorithm is used to decide suitablereceivers based on its fitness value, to which the request message should besent. When a sender receivers accept message from a processor, then it send atask to that processor. Several simulation is done to compare proposed methodwith conventional sender-initiated algorithm.

The results shows theeffectiveness of the proposed algorithm, since the performance of the proposedscheme is better than that of the conventional scheme on the response time andmean response time.