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RPGARole-Playing Game Association
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The RPGA employs a stochastic universal selection to create selection pressure towards the global optimal solution.
The RPGA proposes a therapeutic crossover that incorporates a gene-therapy method with a conventional crossover scheme to enhance the exploitation ability and speed up the convergence rate [13].
The proposed RPGA adopts a replacement-with-elitism method to prevent best solutions from being lost through a selection process.
The RPGA will stop when it reaches the predefined maximum iterations.
This proposed RPGA inspired by the Pawlak's RST [16, 17]has been proved better than several existing algorithms for solving a variety of numerical optimization problems [13].
In this paper, the proposed RPGA is evaluated by solving multicast routing problems in MANETs.
Obviously, the RPGA with 6 partitions can achieve better results than other partition settings with respect to all the three performance metrics.
The normalized percentages relative to [alpha] = 1.01 are shown in Figure 7.Noticeably, the RPGA with [alpha] = 1.01 can achieve better results than other settings on all the cost, success rate, and computing time metrics.
The performance of the proposed RPGA is compared with two well-known penalty methods, which are the Wang's penalty (WP) method [22]and the DP method [9] for three kinds of multicast scenario.
For the mean cost in Table 4, the RPGA can find the best result in average 30 runs.
For comparison, all the results are normalized as percentages relative to the results of the RPGA (in Figure 8).
In Figure 9(a), we can observe that both the DP and RPGA methods rapidly converge on the low-cost results after 8 generations; however, the WP method takes 13 generations to slowly converge on a high-cost one.