The fact that this way, eventually, optimal solutions are found yet seems to
be based on an almost less random search (caused by the high mutation rate) in combination with the application of an elitism strategy.
This was the reason to look for another cross-over operator, the above-mentioned two-point bit equalizer crossover.
In the second series of experiments while applying this operator,a more usual crossover probability (1.0) together with a more normal, lower (but still quite high) mutation probability (0.5)appeared to be best choices.
Initially, we used 50 generations of both GAs.
For the GA with the two-point bit equalizer crossover operator, this number
appeared to be large enough to guarantee convergence to the same final solution in all experiments.
However, this number is not sufficient to guarantee convergence to the final solution in case of using the GA with the two-point order based crossover operator. We therefore also tried that GA using 100 generations.
The fact that this way, eventually, optimal solutions are found yet seems to
be based on an almost less random search (caused by the high mutation rate) in combination with the application of an elitism strategy.
This was the reason to look for another cross-over operator, the above-mentioned two-point bit equalizer crossover.
In the second series of experiments while applying this operator,a more usual crossover probability (1.0) together with a more normal, lower (but still quite high) mutation probability (0.5)appeared to be best choices.
Initially, we used 50 generations of both GAs.
For the GA with the two-point bit equalizer crossover operator, this number
appeared to be large enough to guarantee convergence to the same final solution in all experiments.
However, this number is not sufficient to guarantee convergence to the final solution in case of using the GA with the two-point order based crossover operator. We therefore also tried that GA using 100 generations.
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