Figure 2: Comparison GraphAfter app

Figure 2: Comparison Graph
After applying genetic operators’ crossover, fitness function and inversion we get final population. Out of the above final population we get minimum waiting for job sequence 1, 3, 4, 2 as 3.25 time unit. Following graph focus on comparison based on average waiting time of algorithms. A time unit on Z-axis and on Y-axis FCFS, SJF, RR and GA algorithms has been presented (Figure 2).
VII. CONCLUSION AND FUTURE SCOPE
The problem of scheduling which computer process run at what time on the central processing unit (CPU) or the processor is explored. Some CPU scheduling algorithms has been briefed. The simplicity of the methods used supports the assumption that GA's can provide a highly flexible and user friendly, near optimal solution to the general job sequencing problem. The Genetic algorithms outperform the conventional procedures in solving optimization problems. The new representation has initially been tested on a data to evaluate its effectiveness. Quite promising results are obtained. The simulation results clearly show that the proposed approach is able to find optimized solution. The experiment carried out is efficient to find best sequence. From result we even conclude that with evolutionary technique we may get more than one best sequence with minimum waiting time. This work can be extended so that technique can be implemented for dynamic scheduling and for similar sequencing problem.

ผลลัพธ์ (อังกฤษ) 2:

Figure 2: Comparison Graph
After applying Genetic operators' crossover, fitness function and we inversion Get Final population. Out of the above final population we get minimum waiting for job sequence 1, 3, 4, 2 as 3.25 time unit. Following graph focus on comparison based on average waiting time of algorithms. A Z-axis and time on UNIT on Y-axis FCFS, SJF, RR and GA algorithms has been Presented (Figure 2).
VII. SCOPE AND FUTURE conclusion
The Problem of scheduling Process Computer Run at what time on which the Central Processing UNIT (CPU) or the processor is explored. Some CPU scheduling algorithms has been briefed. The simplicity of the methods used supports the assumption that GA's can provide a highly flexible and user friendly, near optimal solution to the general job sequencing problem. The Genetic algorithms outperform the conventional procedures in solving optimization problems. The new representation has initially been tested on a data to evaluate its effectiveness. Quite promising results are obtained. The simulation results clearly show that the proposed approach is able to find optimized solution. The experiment carried out is efficient to find best sequence. From result we even conclude that with evolutionary technique we may get more than one best sequence with minimum waiting time. This work can be extended so that technique can be implemented for dynamic scheduling and for similar sequencing problem.

ผลลัพธ์ (อังกฤษ) 3:

Figure 2: Comparison Graph
After applying genetic operators', crossover fitness function and inversion we get final, population. Out of the above final population we get minimum waiting for job sequence 1 3 4,,, as 2 3.25 time unit. Following graph. Focus on comparison based on average waiting time of algorithms. A time unit on Z-axis and on Y-axis FCFS SJF,,RR and GA algorithms has been presented (Figure 2).
VII. CONCLUSION AND FUTURE SCOPE
The problem of scheduling which computer. Process run at what time on the central processing unit (CPU) or the processor is explored. Some CPU scheduling algorithms. Has been briefed. The simplicity of the methods used supports the assumption that GA 's can provide a highly flexible and. User, friendlyNear optimal solution to the general job sequencing problem. The Genetic algorithms outperform the conventional procedures. In solving optimization problems. The new representation has initially been tested on a data to evaluate its, effectiveness. Quite promising results are obtained. The simulation results clearly show that the proposed approach is able to find optimized. Solution.The experiment carried out is efficient to find best sequence. From result we even conclude that with evolutionary technique. We may get more than one best sequence with minimum waiting time. This work can be extended so that technique can be implemented. For dynamic scheduling and for similar sequencing problem.