[mlpack-git] [mlpack/mlpack] NeuralEvolution - implemented gene, genome (#686)

[Marcus Edel]

> +      }  
> +    }
> +  }
> +
> +  // Crossover two genome to get one genome.
> +  void Crossover(Genome& genome1, Genome& genome2, Genome& childGenome) {
> +    if (CompareGenome(genome1, genome2)) {  // genome1 is better
> +      CrossoverLinkAndNeuron(genome1, genome2, childGenome);
> +    } else {
> +      CrossoverLinkAndNeuron(genome2, genome1, childGenome);
> +    }
> +  }
> +
> +  // Measure two genomes' disjoint (including exceed).
> +  // NOTICE: we can separate into disjoint and exceed. But currently maybe it is enough.
> +  double Disjoint(Genome& genome1, Genome& genome2) {

Let us distinguish between disjoint and excess here. At the same time, we can decrease the runtime, by not looping over the genomes twice:

```
size_t disjoint = 0;
size_t excess = 0;

if (genome1.NumLink() >= genome2.NumLink())
{
    DisjointExcess(genome1, genome2, disjoint, excess);
}
else
{
    DisjointExcess(genome2, genome1, disjoint, excess);
}


void DisjointExcess(Genome& genome1, Genome& genome2, size_t& disjoint, size_t& excess, size_t& matching)
{
    size_t maxInovation = genome2.GetMaxInnovation();

    for (size_t i = 0; i < genome1.NumLink(); ++i)
    {
        size_t innovationNumber = genome1.aLinkGenes[i].InnovationId();

        if (!genome2.ContainLink(innovId))
        {
            if (innovationNumber > maxInovation)
            {
                excess += 1;
            }
            else
            {
                disjoint += 1;
            }
        }
        else
        {
            matching += 1;
        }
    }

    disjoint += genome2.NumLink() - matching;
}
```

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