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When we get the offspring and fitness so why we prefer to apply mutation in genetic algorithm?

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  • $\begingroup$ to get some randomness in the process. Also, genetic algorithm are inspired by evolution, and it's how evolution works, some genes can be mutated $\endgroup$ – Jérémy Blain Nov 28 '18 at 14:18
  • $\begingroup$ @JérémyBlain: That seems to be the start of an (correct IMO) answer - do you have time to write that in slightly longer form? $\endgroup$ – Neil Slater Nov 28 '18 at 14:36
  • $\begingroup$ actually I am not sure that this is the only reason ? Maybe there is more scientific or mathematical reason for this pattern ? $\endgroup$ – Jérémy Blain Nov 28 '18 at 14:40
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Mutation is (usually) needed to introduce new genes not found in the population. Suppose that you have 4 possible genes $A$, $B$, $C$, and $D$, and that your chromosomes have a non-binary encoding. In that case, if no member of your population has gene $D$, then no amount of crossover operations will result in the introduction of that gene.

I say that new genes usually need mutations to be introduced. However, if your chromosomes use a binary encoding, then new genes could be introduced as a side effect of crossover operations. But it is always safer to have some kind of mutation to ensure that all genes can be accessed.

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    $\begingroup$ Could you add an example section? I think I've only seen binary encodings so far. $\endgroup$ – Martin Thoma Nov 28 '18 at 17:50
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    $\begingroup$ @MartinThoma Certainly, here is a paper mentioning several non-binary encodings for the TSP. $\endgroup$ – Philippe Olivier Nov 28 '18 at 19:03
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Mutation is used to maintain diversity in the solutions. Crossover alone cannot do this.

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