In most implementations of genetic algorithms, the focus is on crossover and mut

Question

In most implementations of genetic algorithms, the focus is on crossover and mutation. But somehow, most of them leave out diploid (dominant/recessive) nature of genes. As far as my (limited) understanding goes dominant/recessive nature of genes is a very important factor in deciding the actual characteristics of an organism.

So my question is why is the diploid nature of genes left out of genetic algorithms in most implementation?

Is it because:

it doesn't provide much benefit
it adds unnecessary complexity to an otherwise simple algorithm
it's difficult to implement

Or something else entirely?

Explanation / Answer

I don't know the actual reason, but it feels intuitive: let's think about what the diploid nature of genes does in RL. In essence it allows the recessive gene to remain in the gene pool even if it's currently at disadvantage to exist, and occasionally resurface, giving two things - first, it doesn't go extinct and can re-multiply if it becomes advantageous; and second, it ensures some population variety as you'll continuously have both phenotypes - a part of population that exhibits the gene and part that doesn't.

Both of these things can be achieved in simpler ways by the mutating/crossover engine - you can directly 'fetch' random good-performing items from 100000 generations ago (which nature usually can't); and you can keep multiple varied subpopulations while protecting the non-primary ones from extinction, which nature usualy doesn't do.

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